OPTIMISATION WITH INTEGRATION OF RENEWABLE ENERGY SOURCES INTO ENERGY SUPPLY CHAIN

Transcription

1 OPTIMISATION WITH INTEGRATION OF RENEWABLE ENERGY SOURCES INTO ENERGY SUPPLY CHAIN PhD Thesis Hon Loong LAM Supervisor: Prof. Dr. Jiří J. Klemeš, DSc Co-supervisor: Dr. Petar S. Varbanov Doctoral School of Information Science and Technology University of Pannonia Veszprém 2011

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3 OPTIMISATION WITH INTEGRATION OF RENEWABLE SOURCES OF ENERGY INTO ENERGY SUPPLY CHAIN (A MEGÚJULÓ ENERGIA FORRÁSOK OPTIMÁLÁSA AZ ENERGIA ELLÁTÁSI LÁNCBA VALÓ INTEGRÁLÁSSAL) Értekezés doktori (PhD) fokozat elnyerése érdekében Írta: Hon Loong Lam Készült a Pannon Egyetem Informatikai Tudományok Doktori Iskolája keretében Témavezető: Dr. Jiří J. Klemeš Elfogadásra javaslom (igen / nem) Témavezető: Dr. Petar S. Varbanov Elfogadásra javaslom (igen / nem)... (aláírás)... (aláírás) A jelölt a doktori szigorlaton... %-ot ért el, Veszprém... a Szigorlati Bizottság elnöke Az értekezést bírálóként elfogadásra javaslom: Bíráló neve:... igen / nem Bíráló neve:... igen / nem Bíráló neve:... igen / nem... (aláírás)... (aláírás)... (aláírás) A jelölt az értekezés nyilvános vitáján... %-ot ért el Veszprém, A doktori (PhD) oklevél minősítése: a Bíráló Bizottság elnöke... Az EDT elnöke

4 ACKNOWLEDGEMENTS First and foremost I offer my sincerest gratitude to my supervisor, Prof Jiří Jaromír Klemeš, DSc, who supported me throughout my thesis with his patience and knowledge, whilst allowing me the space to work in my own way. One simply could not wish for a better or friendlier supervisor. Special words go to my co-supervisor Dr Petar Sabev Varbanov. Thank you for all your advice, scientific support, and also for the private-life guidance. I would also like to thank Prof Ferenc Friedler, DSc, the Dean of Faculty of Information Technology, University of Pannonia and Dr Rozália Pigler-Lakner, the Deputy Dean and also the Secretary of The Information Technology PhD School, Ms Orsolya Ujvári, the School s Project Officer for their administrative support and endless helps. Furthermore, I would like to thanks, Prof Tamás Szirányi, from University of Pannonia and Hungarian Academy of Sciences, Prof Petr Stehlík from Brno University of Technology, Czech Republic and Dr László Czúni from University of Pannonia, who gave helpful support and advice in preparing me for the comprehensive examination. In my daily work I have been blessed with a friendly and cheerful group of fellow students. I would like to take this opportunity to show heartfelt thanks to my colleagues in the Centre for Process Integration and Intensification, University of Pannonia: Ms Zsófia Fodor, Ms Andreja Nemet, Ms Lidija Čuček, Mr Luca De Benedetto, and Mr László Sikos who gave me a helping hand whenever I needed help during my stay in Hungary. Special thanks to Dr Hella Tokos and Mr Mate Hegyhati who provided useful guidance in mathematical programming. The financial support from (i) EC project Marie Curie Chair (EXC) MEXC-CT Integrated Waste to Energy Management to Prevent Global Warming INEMAGLOW is gratefully acknowledged. III

5 Finally, I would like to express gratitude to my parents, family members and my girlfriend. Thank you for your love, understanding and selfless support, even though distance sometimes made it difficult to keep in touch. Hon Loong LAM IV

6 Abstract Optimisation with Integration of Renewable sources of Energy into Energy Supply Chain This thesis presents several steps and approaches to the optimisation and synthesis of regional networks for biomass and biofuel production and supply. The first step is to form clusters of zones, which minimise the environmental impact of the biomass energy exchanges between the zones within the overall supply chain network. A cluster can be understand as a set of zones related through energy transfer links using local infrastructures. The Regional Energy Clustering (REC) algorithm is developed to manage the energy balancing among the zones. The energy surpluses and deficits from various zones can be matched and combined to form energy supply chain clusters. The result of REC analysis is visually illustrated with Regional Energy supply and Demand Curves (RESDC). The RESDC is a pair of cumulative curves which represent cumulative energy surplus and deficit profiles. It is able to indicate the size of each cluster and the total energy involved in the supply chain within the cluster. The result of REC is then further analysed by the Regional Resources Management Composite Curve, in this thesis it is shortened to RMC. The main purpose of developing RMC is to tackle simultaneously the issues of the biomass supply chain and transportation and land use. RMC is a tool for supporting decision making in regional recourse management. It provides a complete view of energy and land availability in a region, displaying their trade-offs in a single plot. A set of rules for RMC manipulation is presented. These rules can be implemented while manipulating regional resources such as land and the surplus energy. These rules give a clear overview picture and useful hints to the planner on how to manage regional resources with a single graph. The network synthesis is carried out by P-graph (Process Graph) tools. P-graph is a directed bipartite graph, having two types of vertices one for operating units and another for those objects representing material or energy flows/quantities. In this V

7 procedure, firstly a maximum feasible superstructure for biomass production network is generated from which the optimal structure is then selected by the Branch and Bound method. This graph-based method clearly shows where, how, and what kind of material and energy carriers will be transferred from raw material biomass supplier to the process plants and customers. In order to test the efficiency of the model, a demonstration case study of regional renewable network problem was solved using these methods. Their performances were tested and the results confirmed the applicability on a regional scale. The results are very positive and some suggestions for future work are given in the conclusion. VI

8 Kivonat A megújuló energia források optimálása az energia ellátási láncba való integrálással A disszertáció új vizsgálati lépéseket és megközelítési módokat mutat be biomassza és bioüzemanyag előállítás és ellátás regionális hálózatának optimális kialakítására. Az első lépés az övezeti zónák meghatározása, ami minimálisra csökkenti a biomassza energia környezetre gyakorolt hatását a zónák közötti átfogó ellátási hálózat révén. Klaszterként olyan zónák összességét definiáltam, melyek a helyi infrastruktúrán keresztül egymást között energiát tudnak cserélni. A kifejlesztett Regionális Energia Klaszterezési (REC) algoritmus lehetővé teszi az energiamérleg meghatározását az egyes zónákban, valamint az azok között szükségessé váló energiaforgalmat. A különböző zónákban, illetve klaszterekben jelentkező energiatöbblet és hiány együttesen határozza meg a regionális energia ellátási láncot. Az eredményt vizuálisan mutatja az úgynevezett Regionális Energia Ellátási és Keresleti görbe (RESDC). A RESDC egy kumulatív görbe pár, ami a terület függvényében bemutatja a kumulált energia többlet és -hiány görbéit. Segítségével meghatározható az egyes klaszterek mérete, miközben megadja a teljes elfogyasztott energiát az ellátási lánc klaszterein belül. A REC eredményeit tovább vizsgáljuk az úgynevezett Regional Resources Management Composite Curve létrehozásával, amit a továbbiakban RMC-nek nevezünk. Az RMC kifejlesztésének legfontosabb indoka, hogy segítségével egyszerre tudjuk kezelni a biomassza ellátási lánc, valamint a szállítás és földhasználat során felmerülő problémákat. Az RMC egy - a regionális energia menedzsment során alkalmazható - döntéstámogató rendszer. Átfogó képet ad egyes régiókban elérhető energia mennyiségéről, annak területi megoszlásáról, és szemléletes módon mutatja be ezeket. Az értekezés ismerteti az RMC működésének általános szabályait. Ezek a szabályok jól alkalmazhatók a VII

9 regionális forrásokra, nevezetesen az energetikai célra rendelkezésre álló termőföld és a többlet energia meghatározására. Ezek a szabályok tiszta képet és eligazítást adnak a tervezőnek, hogy mi módon kell kezelni a regionális erőforrásokat egy egyszerű gráf segítségével. A hálózatszintézis a P-gráf (Process Graph) módszer alkalmazásával történik. A P-gráf egy irányított páros gráf, aminek kétféle csúcsa van az egyik típusú csúcs a működő technológiai egységeket, a másik az energiaáramot, illetve annak minőségét reprezentálja. Ebben az eljárásban, először a biomassza termelés hálózatának lehetséges maximális struktúráját határozzuk meg, majd ennek alapján a Branch and Bound módszer segítségével választjuk ki az optimális struktúrát. Ennek a grafikon-alapú módszernek az alkalmazásával egyértelműen meghatározható, hogy hol, hogyan, és milyen anyagok és energiahordozók kerülnek át a biomassza nyersanyag besszálítóktól az üzemekbe és az ügyfelekhez. Annak érdekében, hogy teszteljük a modell hatékonyságát, esettanulmányokat mutatunk be a megújuló energiaforrások regionális hálózatának létrehozásával kapcsolatos problémák megoldására. A tesztek eredményei megerősítették az új módszerek regionális szintű alkalmazhatóságát. A bemutatott eredmények jelentőségüket tekintve nagyon biztatóak, aminek alapján javaslatot teszünk a munka jövőbeli folytatására. VIII

10 摘 要 能 源 供 应 链 中 可 再 生 能 源 的 集 成 本 文 介 绍 了 生 物 质 及 生 物 燃 料 的 生 产 - 供 应 区 域 网 络 合 成 的 步 骤 和 方 法 第 一 步 是 形 成 区 域 集, 以 使 整 个 供 应 链 网 络 中 的 区 域 间 生 物 能 交 换 对 环 境 所 造 成 的 影 响 最 小 化 一 个 集 可 以 理 解 为 一 组 由 能 量 传 递 链 相 连 的 有 关 区 域, 它 们 通 过 使 用 当 地 的 基 础 设 施 来 达 到 能 量 的 传 递 开 发 了 区 域 能 源 聚 类 Regional Energy Clustering(REC) 算 法, 以 管 理 区 域 之 间 的 能 量 平 衡 对 不 同 区 域 的 能 量 盈 余 和 赤 字 进 行 匹 配 和 组 合, 可 形 成 能 源 供 应 链 集 群 REC 的 分 析 结 果 用 区 域 能 源 供 - 需 曲 线 (RESDC) 进 行 了 形 象 的 诠 释 RESDC 是 一 对 累 积 曲 线, 代 表 累 积 盈 余 和 赤 字, 它 能 够 显 示 每 个 区 域 集 的 大 小 以 及 其 中 供 应 链 所 包 含 的 总 能 量 然 后, 我 们 通 过 区 域 资 源 管 理 复 合 曲 线 (Regional Resources Management Composite Curve, 本 文 将 之 缩 写 为 RMC) 对 REC 的 结 果 进 行 了 进 一 步 的 分 析 开 发 RMC 的 主 要 目 的 是 为 了 同 时 处 理 生 物 质 供 应 链 运 输 和 土 地 利 用 等 问 题 RMC 是 一 个 区 域 资 源 管 理 决 策 支 配 工 具, 它 提 供 了 一 个 地 区 内 的 可 用 能 源 和 土 地 的 完 整 视 图, 并 把 权 衡 考 量 都 显 示 在 一 个 单 一 的 图 表 中 本 文 提 出 了 一 组 如 何 操 作 RMC 的 规 则, 这 些 规 则 可 以 在 操 作 区 域 资 源 ( 如 土 地 和 盈 余 能 量 ) 时 实 施 对 于 如 何 用 一 个 图 来 管 理 区 域 资 源, 这 些 规 则 为 规 划 者 提 供 了 一 个 清 晰 的 概 图 和 有 用 提 示 网 络 的 合 成 是 通 过 工 艺 图 (P-graph) 实 现 的 P - 图 是 一 个 有 向 二 部 图, 有 两 种 顶 点 : 一 个 表 示 操 作 单 元 ; 另 一 个 表 示 代 表 物 质 或 能 量 流 股 / 数 量 的 对 象 在 这 个 过 程 中, 首 先 生 成 了 一 个 关 于 生 物 质 生 产 网 络 的 所 有 可 行 的 超 结 构, 然 后 由 分 支 定 界 方 法 从 中 选 择 出 最 优 结 构 这 个 基 于 图 表 的 方 法 清 楚 地 表 明 哪 里 如 何 什 么 样 的 物 质 和 能 源 载 体 将 从 一 个 供 应 链 层 转 移 到 另 一 个 为 了 检 验 该 模 型 的 效 率, 我 们 进 行 了 示 范 案 例 研 究, 即 用 这 些 方 法 来 解 决 区 域 可 再 生 能 源 网 络 问 题 对 该 方 法 的 效 能 测 试 结 果 证 实 了 其 在 区 域 范 围 内 的 适 用 性 结 果 非 常 乐 观, 同 时, 在 结 论 部 分 对 今 后 的 工 作 提 出 了 一 些 建 议 IX

11 TABLE OF CONTENTS ACKNOWLEDGEMENTS... ABSTRACT... KIVONAT (Abstract in Hungarian) 摘 要 (Abstract in Chinese) TABLE OF CONTENTS.. LIST OF FIGURES.... LIST OF TABLES... NOMENCLATURE..... III V VII IX X XII XIV XV 1. INTRODUCTION Problems Definition Research Objectives Methodology and Research Strategy Outline of the Thesis LITERATURE REVIEW Biomass as the Future Energy Sources Environmental and Ecological Indicators Overview of the Biomass Supply Chain and Network Synthesis Optimisation with Mathematical Programming P-graph Framework Graphical targeting approaches for heat recovery systems, hydrogen recovery and supply chains REGIONAL ENERGY CLUSTERING The REC Algorithm Regional Energy Surplus-Deficit Curves (RESDC) Demonstration Case Study Further Development of REC. 62 X

12 CFP Pay-back analysis Application of REC for Waste-to-Energy Supply Chain Chapter Summary REGIONAL RESOURCE MANAGEMENT COMPOSITE CURVE Construction of Regional Resources Management Composite Curve Demonstration Case Study Chapter Summary OPTIMISATION OF REGIONAL RENEWABLE ENERGY SUPPLY CHAINS: P-GRAPH APPROACH P-graph Procedure for Biomass supply chain Synthesis Demonstration Case Study Chapter Summary SUMMARY OF ACCOMPLISHMENTS Original Contributions Thesis Tézisek (in Hungarian) List of Publications REFERENCES XI

13 LIST OF FIGURES Figure 1.1: Two-level strategy for supply chain network synthesis... 6 Figure 1.2: Workflow in the energy supply chain... 8 Figure 2.1: Main conversion options for biomass Figure 2.2: Energy used for transporting 1 MJ of biomass energy content 1 km Figure 2.3: Environmental Performance Strategy Map 21 Figure 2.4: Biomass flow in the energy supply chain Figure 2.5: Major steps of process synthesis Figure 2.6: Model creation procedure 29 Figure 2.7: P-graphs representing the process structure of three operating units Figure 2.8: P-graphs representing process structures that violate (a) Axiom (S2) or (b) Axiom (S4) 37 Figure 2.9: Inputs to and outputs from the three P-graph Algorithms Figure 2.10: Heat recovery with Pinch Analysis. 41 Figure 2.11: Composite Curves and hydrogen surplus diagram.. 42 Figure 2.12: Material balance in aggregate planning.. 45 Figure 2.13: Supply Chain Composite Curve. 45 Figure 2.14: Generating the energy demand Composite Curve with CO 2 constraint. 48 Figure 3.1: Regional energy clusters Figure 3.2: REC algorithm flowchart Figure 3.3: Visual representation of the optimal biomass exchange flows Figure 3.4: Optimal biomass exchange flows resulting from the LP optimization Figure 3.5: RESDC for the case study Figure 3.6: CFP Pay Back Analysis for alternative road construction Figure 3.7: Location of EFB suppliers and consumers.. 66 XII

14 Figure 4.1: Construction of the RMC Figure 4.2: Optimal biomass transfer flows resulting from the REC algorithm Figure 4.3: Energy and land use management with the RMC. 76 Figure 4.4: Modification of the RMC if the surplus in Cluster 1 is exported to energy market Figure 4.5: Modification of the RMC if a certain area in Zone 1 is used for other purposes Figure 4.6 Modification of the RMC if a certain area in Zone 4 is used for other purposes Figure 4.7: Modification of the RMC if the surplus in Cluster 1 is transferred to Cluster Figure 4.8: Modification of the RMC if a certain amount of energy is imported to fulfill the demand in Cluster Figure 4.9: Modification of the RMC if the biomass production rate in the Cluster 2 is increased Figure 4.10: Modification of the RMC if the energy demand in the Cluster 2 is reduced Figure 5.1: P-graph procedure for biomass supply chain synthesis Figure 5.2: Combinatorially feasible process structures Figure 5.3: Optimum solution for minimum production cost with P-graph representation XIII

15 LIST OF TABLES Table 2.1: Environmental limits for emissions from incineration 14 Table 2.2: Pros and cons of biomass utilisation Table 2.3: P-graph symbols that represent process elements. Table 3.1: Data structure for regional energy clustering Table 3.2: Regional data for demonstration case study Table 3.3: Optimised bioenergy exchange flows Table 3.4: Cluster properties Table 3.5: Data for EFB Suppliers and Consumers Table 3.6: Distance between EFB suppliers and consumers Table 3.7: Parameter for CHP calculation Table 3.8: Allocation of EFB between Suppliers and Consumers Table 4.1: Regional data for demonstration case study for RMC case study 74 Table 4.2: Data for RMC construction. 76 Table 5.1: Materials and streams used in the case study... Table 5.2: Candidate operating units specification XIV

16 NOMENCLATURE Abbreviations ABB AD CFP CHP EFB FP FC FCGT G GIS GT HRSG HDV INC JEP LCA LF NG MSG MSW PP POB REC RES RESDC RMC Accelerated Branch-and-Bound (algorithm) Anaerobic digester Carbon Footprint Combined heat and power Empty fruit bunches Fermentation plant Fuel cell Combined fuel cell and gas turbine Gasifier Geographic Information System Gas turbine Heat recovery steam generator Heavy Duty Vehicles Incinerator Juice extraction plant Life Cycle Assessment Landfill Natural gas Maximal Structure Generation Municipal Solid Waste Pellet plant Palm oil biomass Regional Energy Clustering Renewable energy sources Regional Energy Surplus-Deficit Curves Regional Resource Management Composite Curve XV

17 SFP SPI SSG ST T WTE Saccharification-fermentation plant Sustainable Process Index Solution Structure Generation Steam turbine Transport Waste to Energy XVI

18 Notations and variables Subscripts i j index for source zones Index for sink zones Mathematical operator meaning each Parameters A i Total area of Area for Zone I, km 2 AB i Available biomass surplus in Zone i, t A cc Fixed capital costs, B cc C CEF Linear cost coefficient for calculating capital cost, /MW HVD capacity, t Carbon Emission Factor, (kg CO 2 )/(L of fuel) CFP Total CFP for biomass transfer, CO 2 /y CFP i,j CFP for biomass transfer from Zone i to Zone j, kg CO 2 /y D i Dist i,j FC i,j HV i L i f N zones S i TD j U Cap ceff ik P TR P M Energy demand of Zone i, TJ/y Two-way distance, km Specific Fuel Consumption for transporting biomass, L/km Heating value for the biomass from Zone i, GJ/t Energy land use rate for Zone i, km 2 /TJ Energy conversion from the raw materials to the final energy carriers such as heat and energy, TJ/TJ Number of zones Energy supply of Zone i, TJ/y Total deficit of Zone j, TJ/y Operating unit capacity, MW or t Efficiency of satisfying demand i if he belongs to Cluster k Transportation Cost Maintenance Cost XVII

19 Variables B i,j BE i,j CC PB CFP PB Cost Y i,k M k Biomass transported flow from Zone i to Zone j, t/y Bioenergy delivered from Zone i to Zone j, TJ/y Capital cost, CFP payback period, y Cost payback period, y Binary variable which denotes demand D i to Cluster k Infrastructure maintenance of cluster k XVIII

20 Chapter 1 INTRODUCTION Renewable sources of energy have been used as direct and mostly prime energy resources since the beginning of civilization. The direct heat captured from solar irradiation has been used for heating and drying. When stored in the biomass the solar energy had been used for heating and cooking. When with the humans develop further simple tools they also managed to exploit the hydro power for mechanical drive purposes. After the period when the fossil fuels had been to most widely used source of energy for the developed economies, the implementation of renewable energy has been considerably extended with novel high-tech technologies. This has been mainly due to the pressure of the growing energy demand and environmental issues. Over the recent decades, global energy demand has increased due to the world economy growing (National Petroleum Council, 2007). It is expected that the expanding populations and developing economies are going to result in continuous increases in energy demand in the near future experts have predicted that by 2030, world energy consumption will increase by as much as 50% from the year 2000 levels (IEA, 2010; Schumacker, 2010). The continuously increasing energy demand in the transportation however also in residential, commercial and to the certain extend in industrial sectors. They consume enormous amounts of fossil fuels. The global energy crunch scenario (Friedrichs, 2010) as well as the environmental impact issues lead to a key problem to be solved: How to achieve economic development with sufficient energy supply while minimising the environmental problems to an acceptable levels? Utilisation of renewables, notably regionally available biomass, as energy resources is one of the key actions to address this issue (Faaij, 2006; Karp and Shield, 2008; Narodoslawsky, 2010). Several studies have predicted that the use of biomass would be increased significantly in the future 1

21 Chapter 1 Introduction (WEA, 2004; IEA, 2005) and the European Commission has stipulated strongly its extended use as renewable energy sources (RES). This thesis proposes several tools for regional energy targeting and supply chain synthesis using conceptual insight and appropriate visualisation. A demand-driven approach is applied to assess the feasible ways for transferring energy from renewable sources to customers in a given region. These graphical tools provide straightforward information of how to manage the available resources (biomass and land use) in a region. 1.1 Problem Definition Utilising biomass as an energy source requires innovative technologies to be efficient and economically competitive. The biomass supply potential is constrained by its features as low energy density, high specific land use (area per unit energy) and distributed availability. For those reasons the biomass energy infrastructure is generally more expensive to build and operate then that for standard fossil fuels. Building and use the infrastructure to transfer biomass energy over longer distances would tend to increase its cost. Another challenge is the land use and the management of resources. Biomass mostly requires large land areas to collect and process the incoming solar radiation before the energy can be harvested. Because of the growing demand for energy crops, agricultural production, the land for settlement, and land use management, form an environmental and societal trade-off. The large land areas required for growing energy crops and refining them into marketable fuels can result in food price increases and deforestation (Koh and Ghazoul, 2008). This in turn could lead to the loss of biodiversity and create a conflict between the atmospheric carbon balance and natural ecosystems (Huston and Marland, 2003), leading to net CO 2 build-up in the atmosphere. 2

22 Chapter 1 Introduction Besides economic optimisation, the reduction of the environmental impact over the whole life cycle has become a necessity. One of the environmental impact indicators is the carbon footprint - CFP (POST, 2006). Since the biomass sourcing locations (farms, forests etc.), require supply chain infrastructure, which includes field and road transport for collection and transportation, these would tend to increase the CFP of biomass energy supply. The distributed availability of biomass over regions indicates that future bioenergy industrial structure should be necessarily more decentralised (Narodoslawsky, 2010). A method for optimal synthesis of regional biomass supply chains is needed accounting its logistic properties the low transport density and high moisture content. A new balance between the 'environmental' and the 'economical' will have to be found: transport of the biomass and the associated energy consumption and CFP have to be balanced against the efficiency increase of larger scale installations. The solution will likely be a moderately decentralised combination of collection points and process plants (Lam et al., 2010a; Narodoslawsky, 2010). Based on the above analysis, there are several issues concerning regional biomass utilisation: (i) The bio-energy networks should utilise the local raw materials to the maximum possible extent by generating as many useful products as economically feasible, at maximum efficiency to obtain the most favourable economics of the biomass utilisation. (ii) The choices of feedstock and products are mutually related and affect significantly the overall economic viability and emissions. These are important information in designing the energy-product supply networks. (iii) The trade-off between energy generation and land use should be tackled and solved simultaneously to avoid detrimental eco-social impacts to the considered region. 3

23 Chapter 1 Introduction (iii) Another important challenge is to provide a framework and tools allowing the evaluation of alternative options for identifying, placing and sequencing the various processing and transportation operations in the supply networks. 1.2 Research Objectives This thesis presents new methods for regional energy targeting and supply chain synthesis. The main objectives are: i To develop a novel conceptual approach based on property targeting and visualisation, to the analysis and synthesis of renewable energy generation for a given region. ii To enable identification of the maximum extent the local RES usage in a region. iii To enable decision, asking for sustainable management of regional resources. iv To provide a clear insight into renewable energy supply chains. These objectives lead to the following specific research goals: i Develop graphical tools to account for the interaction of each layer in a biomass production network. ii Create an algorithm for the regional energy targeting and clustering. iii Develop a visualisation allowing the decision makers to quickly evaluate the tradeoff between the energy and land use issues. iv Develop a combinatorially efficient synthesis procedure for biomass production networks. 4

24 1.3 Methodology and research strategy Chapter 1 Introduction Research is an intensive and purposeful search of knowledge (Kumar, 2008). In this thesis, it is more specific as a scientific activity undertaken to develop analytical tools for biomass supply chain synthesis. A two-level methodology for the synthesis of regional biomass energy supply chains has been formulated. The methodology is illustrated in Figure 1.1. This comprehensive research strategy is divided into two levels: supply-demand targeting and detailed supply chain synthesis. The first level including steps for REC, targeting the use of regional biomass resources and fossil fuels, and RMC provides the strategy for the regional resource management, which mainly involve the land-use and energy generation issues. In the second level, detailed supply chain synthesis inside each cluster step has defined a new application of the P-graph process optimization framework (Friedler et al., 1992). Supply chains take as inputs various resources (biomass and fossil) and deliver to the final users of energy products for instance heat, power, processed fuels such as pellets and ethanol. All unit operations in the supply chain can be characterised by relevant performance specifications, reflecting their energy conversion efficiencies, thus linking the product generation rates to the resource (input) consumption rates. An energy supply system can include various components and contexts. The system boundary includes a region whose exact definition is case dependent. The region under consideration is modelled as a collection of zones. The zones are smaller areas within the region, accounting for administrative or economic boundaries, which are considered atomic (i.e. non-divisible). For each zone corresponding rates of energy demands and biomass resource availability are specified. 5

25 Chapter 1 Introduction Problem Formulation REC Level - I Cluster and their properties Regional resource management strategy Generate a RMC Level - II Supply Chain Synthesis with P-graph Supply chain network Figure 1.1 Two-level strategy for supply chain network synthesis The workflow for delivering the energy from the biomass sources to the customers within a single zone is illustrated in Figure 1.2. The biomass raw materials, intermediate materials and the final products can be transported between the zones within the cluster, allowing larger processing facilities to be built, if this is economically beneficial. 6

26 Chapter 1 Introduction Transport to Local Energy market Export Energy market Figure 1.2 Workflow for energy supply from biomass (Lam et al., 2010b) On the left-hand side of Figure 1.2 is given the general sequence for harvesting, conversion and utilisation of the resources. The workflow used for identifying the possible streams/materials and the candidate operating units is shown on the right, following those generic stages. The solid line represents the transportation within the same zone, and the dotted line represents the transportation that into/ out crossing other zones. Various interconnections are possible. They are constrained mainly by the compatibility between the materials and energy carriers, and also the operations. Additional constraints as upper bounds on throughputs and on investments are usually imposed. Due to the mainly topological complexity the synthesis procedure beneficially employs the P-graph framework. 7

27 Chapter 1 Introduction 1.4 Outline of the Thesis After the introduction provided in this Chapter 1, Chapter 2 gives the review on the topics related to the research scope and background, such as (i) Renewables as the future energy sources, (ii) Biomass supply chains and biomass to energy technologies, (iii) Biomass supply chain synthesis, (iv) Applying and extending the Process Integration methodology into new fields. Chapter 3, 4 and 5 present the original research outcome of this PhD works. Chapter 3 demonstrates the development of Regional Energy Clustering (REC) algorithms (Lam et al. 2010a). The developed mathematical Programming model is presented and supported by a case study demonstration. Furthermore, the extension work from REC is also presented such as the CFP pay back analysis and the application of REC in the waste-to-energy network synthesis. Chapter 4 extends the techniques of REC to regional resources management with a novel tools namely Regional Resource Management Composite Curve - RMC (Lam et al., 2010c). A step-by-step case study is given demonstrating how to apply the RMC in handling a trade-off problem: competitive land use and energy generation. Chapter 5 tackles the synthesis of biomass energy networks using P-graph (Friedler et al., 1992). The procedure has steps for maximum structure generation and optimum structure generation. 8

28 Chapter 1 Introduction Chapter 6 is the summary of accomplishments of this PhD. It concludes the original contributions of the PhD candidate works and also the list of publications related this PhD research topic. 9

29 Chapter 2 LITERATURE REVIEW Biomass is one of the key renewable energy sources (RES), offering the potential for reducing environmental impact regarding energy supplies (WEA, 2004; Karp and Shield, 2008; Demirbas, 2009). It is a versatile source from which heat, electricity, and liquid bio-fuels can be generated. Its utilisation can also improve energy security, the development of rural regions, and employment (Gwehenberger and Narodoslawsky, 2008). This chapter starts with a review of the potential of biomass as a future energy source and what are the pros and cons of utilising biomass for this purpose. This is followed by a review of biomass supply chain systems, as well as the environmental impact indicator along the systems. This chapter also provides an overview the application of mathematical programming in supply chain synthesis. The last part of this chapter presents the advantages of employing a graphical method for system analysis on the examples of Pinch Analysis and its modifications that could be further extended for biomass supply chain synthesis. 2.1 Biomass as the Future Energy Sources Potential sources of biomass There are many types of types of vegetation in the world, and many ways they can be used for energy production (Faaij, 2006; Karp and Shield, 2008). In general there are two approaches: growing plants specifically for energy use (energy crops), and using the residues from plants to be used for other things. The best approaches vary from one region to another according to climate, soil properties, geography, population, etc (Faaij, 2006). 10

30 Chapter 2 Literature Review The main sources for biomass are generally divided into these categories: Energy crops can be grown on farms in potentially very large quantities, however in many cases replacing and competing with food crops, for example corn, sugar cane and sweet sorghum (Faaij, 2006, Demirbas, 2009). Multifunctional crops could be used both for food and energy production simultaneously for example corn grain for food and corn stover for energy generation (Čuček et al., 2010). Oil plants. Plants such as soybeans (Mandal et al., 2002), oil palm (Prasertsan, 1996; Lam et al., 2010d) and Jatropha (Achten et al, 2008) produce oil, which can be used to produce fuels. A rather different type of oil crop with great promise for the future is microalgae (Chinnasamy et al., 2010). Forestry waste and woods. Forestry waste Schlamadinger and Marland, 1996) and wood waste are sawdust and bark from sawmills, shavings produced during the manufacture of furniture, and organic sludge (or "liquor") from pulp and paper mills (Joshi and Mehmood, 2010). Other biomass residues. The forestry, agricultural, and manufacturing industries generate plant and animal wastes in large quantities (Faaij, 2006). City waste, in the form of rubbish and sewage, is also a source for biomass energy. Biomass conversion technologies The traditional way of converting biomass to energy, practiced for thousands of years, has been to simply burn it to produce heat. The heat can then be used directly, for heating, cooking, and also for industrial purposes. Today, new ways of using biomass are still being discovered and it always refer as analogy from waste-to-energy technologies (Vollebergh, 1997; Maniatis and Millich, 1998; Turkenburg et al., 2000; Stehlík, 2007a, & 2007b; Celma et al., 2007; Stehlík et al., 2008; Demirbas, 2009; Veringa 2010; Gregg, 2010). The general paths for biomass conversion and utilisation technology options are shown in Figure 2.1. The main routes are analysed next. 11

31 Chapter 2 Literature Review Thermochemical conversion Biochemical conversion Biochemical and physical conversion Combustion Gasification Pyrolysis Digestion Fermentation Extraction Steam Gas Oil Charcoal Biogas Oil Steam turbine Gas turbine Hydrogen Synthesis Gas engine Distillation Transesterification Fuel Cell Ethanol Bio-diesel Heat Electricity Fuels Figure 2.1 Main conversion options for biomass (Turkenburg et al., 2000) Combustion and incineration. A classic application of biomass combustion is heat production for domestic applications (Faaij, 2006; Stehlík, 2007a). Traditionally, the use of wood generally takes place at low efficiency and generally emits considerable amounts of pollutants such as dust and soot. Technological development has led to the application of improved heating systems, which are automated, have catalytic gas cleaning and make use of standardized fuels (Stehlík, 2007a & 2007b; Stehlík, 2009). Incineration of biomass combined with waste can be considered as a form of recycling energy contained in treated materials. It partly releases the energy consumed during their production. Biomass combustion is labelled as waste-to-energy technology (WTE) (Stehlík, 2009). WTE is also referred to as the thermal processing of waste, including energy utilization. The combustion (thermal processing, incineration) of various types of waste is substantially reducing the waste volume and in addition, WTE systems can provide relatively clean, reliable, and renewable (to some extent) energy. 12

32 Chapter 2 Literature Review Gasification. Gasification is one of the technologies for utilising the thermo chemical conversion of biomass through the generation of gaseous fuels suitable for more efficient consumption (Kirubakaran et al., 2009). Gasification with air is a conversion of organic matter into low-energy gas (syngas or synthesis gas) which, after some modification, is suitable for use in boilers, combustion engines, gas turbines and, after proper cleaning, even in high-temperature fuel cells (Varbanov and Friedler, 2008). Anaerobic digestion is a biochemical process where, in the absence of oxygen, bacteria break down organic matter to produce biogas and digestate (De Baere and Mattheeuws, 2008). The digestate can be produced out of many different kinds of biomass such as energy crops, organic waste, manure or a combination of these raw materials. Fermentation usually refers to the bioethanol production such as from corn (Mojović et al., 2006), sugar cane and sweet sorghum (Nguyen and Prince, 1996) Selection of a convenient biomass utilisation method needs to conform to the applicable local environmental legislation. National environmental legislations are far from uniform; large variations can be found when comparing different countries (Stehlík, 2007b). The differences among emission limits of EU, the USA, and China are displayed in Table 2.1 (Stehlík, 2007b). The values in the Table 2.1 are related to allowable emissions limits based on concentration (mg/nm 3 ) and they are not measured in total volume. The limits are given by environmental legislation and are obligatory for medium and large sources of emissions generation. E.g. NO x concentration is re-calculated to NO 2 concentration and measured before the stack in incineration plants. The same is valid for other emissions. The measurement is collected at the end of the discharge point. Table 2.1 Environmental limits for emissions from incineration (Stehlík, 2007b) 13

33 Chapter 2 Literature Review Pollutant Units EU USA China Dust mg/nm CO mg/nm NO x mg/nm SO 2 mg/nm HCl mg/nm Hg mg/nm Cd + TI mg/nm As +Co+Ni+Cr+Pb+Cu+Mn+V+Sb mg/nm Dioxins/furans TEGng/Nm Pros and Cons of Biomass Utilisation All energy sources have advantages and disadvantages, and it is important to evaluate them to determine whether the particular source (e.g. biomass) is really clean, safe, efficient, and effective. Table 2.2 summarises the main pros and cons of biomass utilisation. There are always topics for the discussion about the pros and cons of biomass utilisation, for example: Is biomass energy better in production cost? Biomass is usually locally available. The typical locations of biomass sources (farms, forest, etc.) have the relatively low energy density, and the distributed nature of the sources require extensive infrastructures and huge transport capacities for implementing the biomass supply networks. For regional biomass supply chains road transport is the usual mode for collection and transportation. This tends to increase the cost of the biomass based energy. From the regional development point of view, biomass energy can be produced and supplied in the area, so there is no need for large pipelines or other massive infrastructure building. This also eliminates or decreases the cost and maintenance fees 14

34 Chapter 2 Literature Review caused by vehicles transporting the energy source from a long distance source point, e.g., the natural gas sending over from Russia. Table 2.2 Pros and Cons of Biomass Utilisation (Hall and Scrase, 1998; Demorbas, 2009; Biofuel Watch, 2010) Impact Advantages Disadvantages Environmental Cleaner energy alternative to Fossil fuels Waste-to-energy Overuse threatens biodiversity Green house gases emitted along the supply chain Societal and - economical Can reduce the dependence of fossil fuels (imported in most countries) Can stimulate regional growth Maximise the usage of local energy sources and increase the productivities Increase the local job opportunity for rural areas Security of energy supply Development of related industries Export potential Higher production cost Land-use limitation May reduce food production Another hot issue: Is biomass energy clean enough? In one hand, biomass is considered clean low carbon emission. Biomass is considered as Clean Energy because it can be naturally replenished (Karp and Shield, 2008; Demirbas, 2009) through the process of photosynthesis, chlorophyll in plants captures the sun's energy by converting CO 2 from the air and water from the ground into carbohydrates, complex compounds composed of C, H 2 and O 2. When these carbohydrates are burned, they turn back into CO 2 and water and release the sun's energy they contain. On the down side, the biomass process plant has to be 15

35 Chapter 2 Literature Review properly designed else It can release greenhouse gases into the atmosphere when burned. Therefore, biomass energy plants need to be equipped with exhaust gas cleaning technology to make them completely environmentally friendly. Furthermore, biomass processes required a huge amount of power for the pre-treatment such as drying, chipping, compacting, cutting and shredding massive volumes of biomass is frequently required. The higher production cost as compare to fossil fuel would put biomass at disadvantage in purely economic terms (Krotscheck et al., 2000). The nature of biomass: distributed source points and low energy - mass density increase the load of transportation, which directly affected the biomass production cost. Moreover, harvesting, collecting, and storing raw biomass materials can be very costly, especially if we take into consideration the large volumes needed in comparison to fossil fuels. This observation contradicts the conventional industrial wisdom regarding the economy of scale, where larger plants are usually favoured, and processing is centralized. Centralised biomass processing over a larger area would eventually lower transportation efficiency in the case of transporting raw biomass, due to the unnecessary amounts of water and the low physical density of the transported materials volumes. Even though, such cleaning technology is often not economically feasible for smaller plants. There are always a pay-off for the economical impact, especially the development of regional scale biomass utilisation always bring a potential job and export (energy and other biomass products) opportunities. The pre-treatment step has a significant influence on the performance of bioenergy networks, especially on logistics. Densification, compaction, and drying prior to transportation is crucial, as converting biomass into a higher density intermediate product can save transport and handling costs. It can improve the efficiencies of the conversion stages (Uslu et al. 2008). 16

36 Energy for Transportation of I MJ Biomass kj/mj km Chapter 2 Literature Review The importance of pre-treatment is supported by studies on the types of biomass transportation. Gwehenberger and Narodoslawsky (2008) evaluated the energies used by several modes when transporting biomass. Figure 2.2 shows that transportation using ships uses the smallest amount of energy, whilst, in contrast, transportation of low-density raw materials such as straw using tractors uses a greater amount of energy truck rail 1 rail 2 ship 1 ship 2 tractor Energy density MJ/m 3 Figure 2.2 Energy used for transporting 1 MJ of biomass energy content 1 km (Gwehenberger and Narodoslawsky, 2008) Because of the growing demand for biomass energy crops, agricultural production, the space for settlement, and land use management, form an environmental and societal trade-off. The large land areas required for growing energy crops and refining them into 17

37 Chapter 2 Literature Review marketable fuels can result in food price increases and deforestation (Koh and Ghazoul, 2008). This in turn could lead to the loss of biodiversity and create a conflict between the atmospheric carbon balance and natural ecosystems (Huston and Marland, 2003). Land use conflict occurs when the land in an area is limited and the same land can support a variety of different uses. The latter define a trade-off scenario for the region. Because of the significant growth in world population and the resource demand per capital, it makes land use management more critical: to produce food, energy and space for living in a competing resource base. Consequently land use strategy is required to be properly planned for sustainable development (Chen et al., 2005). The planning should be focused on the social and economic needs with integral considerations of the environment to be sustainable in the long term. The concepts of sustainable regional resource and land use management have been presented elsewhere. Yamamoto et al. (2000) developed a global land use and energy model (GLUE) to evaluate bioenergy supply potentials, land use changes, and CO 2 emissions in the world. The model analyses the land use competitions and overall biomass flows. Silalertruksa et al. (2009) used consequential life cycle assessment (LCA) approach to evaluate the environmental consequences of bioenergy (bio-ethanol) policy target on land use and greenhouse gas emissions. A strategic method focused on the land-use trade-off between biomass production and other land uses such as for food crops and urbanisation development is needed. 2.2 Environmental and Ecological Impact Indicators Energy production, transformation, transports and end-use generally impacts the environment and ecology even though form RES that has been claimed as green energy. Increasing efficiency along the energy supply chain can reduce the environmental impact of emissions, although increasing efficiency generally requires 18

38 Chapter 2 Literature Review greater operating cost, increasing the environmental burdens associated with these and somewhat offsetting the environmental gains of improved efficiency (Rosen, 2009). There are several previous works demonstrated the implementation of different environmental/ ecological impacts: i Rosen (2009) presented the potential usefulness of exergy in addressing environmental impact. Exergy analysis is a thermodynamic technique for assessing and improving systems and processes, which is similar but advantageous to energy analysis, in large part because it is based on the second law of thermodynamics. The exergy of an energy form or a substance is a measure of its usefulness. Relations between environmental impact and exergy in general and chemical exergy of waste emissions in particular are observed to support the use of exergy as such an indicator. The measure of disequilibrium with respect to a reference environment provided by exergy is considered, along with the consequence that the exergy of unrestricted waste emissions has the potential to impact the environment. ii The Sustainable Process Index (SPI) developed by Narodoslawsky and Krotscheck (1995) is a useful approach to calculate the ecological footprint casused by a process. SPI is based on the assumption that a sustainable economy builds only on solar exergy. Surface area is needed for the conversion of exergy into products and services. Surface area is a limited resource in a sustainable economy because the Earth has a finite surface. Area is the underlying dimension of the SPI. The more area a process needs to fulfil a service the more it costs from the sustainable point of view. SPIonExcel (Sandholzer et al., 2005) calculates the ecological footprint and the SPI of a product or service through the input that characterizes the process given by an eco-inventory (Sandholzer and Narodoslawsky, 2007). The eco-inventories used for the calculation of the overall footprint contain engineering mass and energy 19

39 Chapter 2 Literature Review flows of processes in terms of input and output flows. Two different classes of inputs, impacts and intermediates, can be defined. An intermediate is a flow derived from and/or going to another process. This includes processes like electricity generation, transportation or waste flows to treatment plants and also products going to final consumption. Intermediates are produced via processes using themselves mass and energy flows. Their ecological pressure can be traced back to these flows, which in themselves can be either impacts or other intermediates. The SPI approached has been used to evaluate environmental performance in (i) integrated bioenergy systems (Krotscheck et al., 2000), and (ii) energy production systems (Narodoslawsky and Krotscheck, 2004) iii De Benedetto and Klemeš (2009, 2010) proposed a sustainable environmental performance indicator Environmental Performance Strategy Map. This particular graphical map allows combination of the main environmental indicators (footprints) with the additional dimension of cost. The core of the concept is to calculate some specific sustainability indicators, based on Life Cycle Assessment (LCA). It is suggested to evaluate all options against the following categories: Carbon footprint; Water footprint; Energy footprint (Land, renewables, non-renewables); Emission footprint (emissions in air, in water, in soil, waste materials); Work environment footprint (work-environment and toxicological impacts). To represent these relations and to compare options from an environmental and, more generally, business perspective a new graphical representation needs to be introduced: the Environmental Performance Strategy Map (Figure 2.3). The objective of this representation is to build upon the strength of Ecological Footprint and Life Cycle Analyses to provide a single indicator for each option. The practitioner can make use of this indicator to direct the decision-making 20

40 Chapter 2 Literature Review process towards the best option from a sustainability and environmental perspective. Figure 2.3 Environmental Performance Strategy Map (De Benedetto and Klemeš, 2009) In this work, Carbon Footprint (CFP) is used to evaluate environmental impacts of biomass process and supply chain. CFP as defined in (POST, 2006) is the total amount of CO2 and other greenhouse gases emitted over the full life cycle of a process or product. CFP has become an important environmental protection indicator as most industrialised countries have committed to reduce their CO 2 emissions by an average of 5.2% in the period in respect to the level of 1990 (Sayigh, 1999). The CFP of a biomass supply chain is the total CO2 amount emitted throughout the supply chain life cycle (Perry et al., 2008). Energy supplied from biomass cannot be considered truly carbon-neutral even though the direct carbon emissions from combustion had been offset by carbon fixation during feedstock photosynthesis (Anderson and Fergusson, 2006). The net CFP is mainly caused by the indirect carbon emissions generated along the supply chain especially by processing, transportation and burning which releases emissions. Especially transportation activities could contribute as the major part of the CFP in the supply chain (Forsberg, 2000). 21

41 Collection and distribution point & storage Energy conversion Plant & storage Chapter 2 Literature Review Another CFP contribution results from the use of fertilisers and land cultivation activities for raising energy crops. The typical locations of biomass sources (farms, forest, etc.), the relatively low energy density, and the distributed nature of the sources require extensive infrastructures and huge transport capacities for implementing the biomass supply networks. For regional biomass supply chains road transport is the usual mode for collection and transportation. This tends to increases the CFP of the biomass based energy. 2.3 Overview of the Biomass Supply Chain and Network Synthesis A typical biomass supply chain is shown in Figure 2.4. Maximising renewable energy utilisation requires a balanced mix of primary resources, including biomass, integrated in combined supply chains with food production and waste management (Junginger et al., 2001; Raven and Gregersen, 2007). Biomass supply chains deal with harvesting, densification, drying, storage, and transportation activities. Shah and his group (Dunnett et al., 2007) presented a systems modelling framework for the simultaneous design and operations scheduling of a biomass to heat supply chain. They proposed an efficient scheduling system that is affected by the harvest yield, crop moisture content, ambient drying rates and seasonal demand. Rentizelas et al. (2009) focus on the logistics issue of biomass utilisation, especially the storage and multi-biomass supply chain optimisation. The scope of those papers involves only harvesting, transportation and storage of biomass and does not integrate the energy conversion processes into the supply chain. Upstream Supply Chain Downstream Supply Chain Biomass Production and Pre-treatment Transportation Electricity Heat Fuel Industry Resident Commerce Primary energy carrier Secondary energy carrier 22

42 Chapter 2 Literature Review Figure 2.4 Biomass flow in the energy supply chain (Lam et al., 2008) There are also several biomass supply chain case studies presented such as: i A case study of Far West Texas regional power generation systems has been presented by Becerra-López and Golding (2007). In this work the array of participating technologies has been optimised within a sustainability framework, which translates into a multi-objective optimisation problem. The problem is formulated and solved to determine supply shares for some chosen technologies based on both renewable power conversion and natural gas use. The cost based on the exergy and economic evaluations is established as primary competing factors. The deployment of renewable power technologies hypothetically follows the Gompertz Growth Model, which is constrained by exergy self sustenance. The solution is given as a Pareto trade-off front for arrays of optimal technologies and capacities. Additionally, the sustainability of these arrays is analysed through indicators, and the current goal for renewable power technologies is discussed. ii Junginger et al. (2001) proposed a fuel supply strategies for large-scale bioenergy projects the electricity generation from agricultural and forest residues in North-eastern Thailand. The scope of study presented a methodology to set up fuel supply strategies for large-scale biomass conversion units (between 10 and 40 MWe), and to determine the connected risks and to minimize them. The methodology focuses (amongst others) on variations in residue quantities produced, limited accessibility of residues, utilization by other competitors and logistical risks. For each risk, possible ranges are determined and incorporated in different fuel supply scenarios which indicate how biomass quantities and prices may vary under different circumstances. iii Freppaz et al. (2004) demonstrated a decision support system, which aim to optimise forest biomass exploitation for energy supply at a regional level. The 23

43 Chapter 2 Literature Review geographic information system based techniques are integrated with mathematical programming methods to yield a comprehensive system that allows the formalisation of the problem, decision taking, and evaluation of effects. The aim of this work is to assess the possibility of biomass exploitation for both thermal and electric energy production in a given area, while relating this use to an efficient and sustainable management of the forests within the same territory. iv Berndes et al. (2003) gave several case studies of the contribution of biomass in the future global energy supply. The question how an expanding bioenergy sector would interact with other land uses, such as food production, biodiversity, soil and nature conservation, and carbon sequestration has been insufficiently analyzed in the studies. A refined modelling of interactions between different uses and bioenergy, food and materials production - i.e., of competition for resources, and of synergies between different uses would facilitate an improved understanding of the prospects for large-scale bioenergy and of future land-use and biomass management in general. v A study of Mississippi region with the supply chain design for biomass-to-ethanol industry has been presented by Ambarish and his group (2008). This paper models the in-bound supply chain of a biorefinery as a network design problem with additional constraints. This model takes as an input the distribution and supply of biomass (corn and corn stover), and identifies the number and size of biorefineries needed to make use of the available biomass. vi An Integrated Biomass Supply and Logistics (IBSAL) Model has been proposed and presented by Shahab et al. (2008). IBSAL is a powerful tool for evaluating the biomass supply chain from field to biorefinery. IBSAL consists of a series of equations that calculate the collectible fraction of biomass, while tracking biomass moisture during harvest and storage, machinery performance, compositional changes, and dry matter losses. The model analyzes the effects of 24

44 Chapter 2 Literature Review variations (annual weather patterns, variations in yield and moisture, variable biomass composition) associated with the feedstock supply. vii Iakovou et al. (2010) gave an overview of the generic system components and then the unique characteristics of Waste biomass-to-energy supply chain management that differentiate them from traditional supply chains. Their work proceeds by discussing state-of-the-art energy conversion technologies along with the resulting classification of all relevant literature. It followed by the natural hierarchy of the decision-making process for the design and planning of waste biomass supply chain and provide a taxonomy of all research efforts as these are mapped on the relevant strategic, tactical and operational levels of the hierarchy. The critical synthesis demonstrates that biomass-to-energy production is a rapidly evolving research field focusing mainly on biomass-to-energy production technologies. There are several papers discussed and presented the implementation of regional clustering approach in the biomass production networks: i Williams et al. (2008) presented a quantitative analytical method to provide delineation of agro-ecoregions in a more objective and reproducible manner, and with use of generalized crop-related environmental inputs offers an opportunity for delineation of regions with broader application. A raster (cell-based) environmental data at 1 km scale were used in a multivariate geographic clustering process to delineate agro-ecozones. Environmental parameters included climatic, edaphic and topographic characteristics hypothesized to be generally relevant to many crops. Clustering was performed using five a priori grouping schemes of 5 25 agro-ecozones. ii Aguilar et al. (2009) utilized geo-referenced data on the location of primary wood products manufacturers in the US South to examine spatial clustering within this 25

45 Chapter 2 Literature Review industry. A marginal analysis indicated that counties with adequate transportation infrastructure and presence of related industries were most likely to attract new primary forest products manufacturers. iii A two levels general Bioenergy Decision System (gbeds) for bioenergy production planning and implementation was developed by Ayoub et al. (2007). It also includes a scenario database, which is used for demonstration to new users and also for case based reasoning by planners and executers. Based on the information base, the following modules are included to support decision making: the simulation module with graph interface based on the unit process (UP) definition and the genetic algorithms (GAs) methods (Ayoub et al., 2007) for optimal decisions and the Matlab module for applying data mining methods (fuzzy C-means clustering and decision trees) to the biomass collection points, to define the location of storage and bioenergy conversion plants based on the simulation and optimization model developed of the whole life cycle of bioenergy generation. iv Kajikawa and Takeda (2008) demonstrated their work in a citation network analysis of scientific publications to know the current structure of biomass and bio-fuel research. By clustering and visualizing the network, these revealed their taxonomic structure. Emerging technologies are detected by analyzing the average publication year of clusters. The paper also analyzed the position of each cluster in the global structure of research. 2.4 Optimisation with Mathematical Programming Optimisation is one of the most powerful tools in process synthesis. Optimisation involves selecting the best solution from a set of candidate or feasible solutions (El- Halwagi, 2006). Process optimisation problems are formulated as mathematical models, where variables correspond to decisions - e.g., the flowrate of a stream, the amount of 26

46 Chapter 2 Literature Review heat provided by high-pressure steam -,constraints corresponding to the conceptual model of the system - e.g., mass balance (Klemeš et al, 2010), and the objective function corresponding to the mathematical description of an optimisation criterion in the case of single-objective optimisation, or to the description of two or more criteria in the case of multi-objective optimisation. Optimisation aims at finding appropriate values the variables, in such a way that (i) Constraints involving these variables are satisfied and (ii) The objective function of the problem is minimised (or maximised). The constraints define the search space, whilst the objective function is used to determine the most favourable point or points within this space. The principles of optimisation theory and algorithms are covered by various books (e.g., Grossmann 1996; Edgar and Himmelbalu 2001; Grossmann and Biegler 2004; Williams, 2005; El-Halwagi, 2006; Ravidran et al., 2006, and Klemeš et al., 2010) and overviews as Friedler (2009, 2010). In general, a process synthesis problem is defined by specifying the available raw materials, candidate operating units, and desired products. Each of them is given by an individual mathematical model. The models cannot, by themselves, directly constitute the mathematical programming model for the synthesis problem. The mathematical model will be unclear with the risk of failure. The major steps of process synthesis are illustrated in Figure

47 Chapter 2 Literature Review Mathematical Programming It is important not to confuse the concept of mathematical programming (MP) with computer programming. Mathematical programming is programming in the sense of planning, as such it has nothing to do with computers (Williams, 2006). However MP becomes involved with computing since practical problems are complex: so large quantities of data and arithmetic can only be solved by the calculating power of a computer. Cost and constraints for the units and raw materials. Price and constraints for the products Model Generation Mathematic model (LP, MILP, NLP, MINLP) Solution Procedure Optimal Network (Flowsheet) Figure 2.5 Major steps of process synthesis (Klemeš et al., 2010) The mathematical formulation of an optimisation problem entails the following steps (El- Halwagi, 2006): 28

48 Chapter 2 Literature Review 1. Determining the objective function 2. Developing the game plan to tackling the problem 3. Developing the constraints 4. Improving formulation In general, modelling begins by accumulating sufficient information about the process in order to develop an understanding of the elements and the relationships between them and to proceed with formulating a mathematical description of the process that as is implemented on a computational platform. A distinctive characteristic of this procedure is its iterative nature (Figure 2.6). The mathematical modelling often leads to changes in the conceptual model; the result is an iterative feedback loop, as shown in the figure. A similar correction loop is also present at the output of the implementation block. The discussion that follows addresses only those activities in Figure 2.6 that involve conceptual and mathematical modelling. Overview of the MP Development Mathematical models are classified according to the types of variables (continuous or integer) and constraints (linear or nonlinear). A model itself cannot be a programming. Programming is an activity of optimisation using an algorithm suitable of solving a given type of model. If a model is linear and described by continuous variables, the corresponding programming is called linear programming (LP). If it is nonlinear, then we are talking about nonlinear programming. In the case of a linear model with mixed continuous and discrete variables, the programming is mixed-integer linear programming (MILP), whist in the presence of non-linearities in the model we are dealing with mixed-integer nonlinear programming (MINLP). 29

49 Chapter 2 Literature Review BEGIN Yes Conceptual modelling Mathematical modelling Yes Need corrections? No Computational implementation Need corrections? No END Figure 2.6 Model creation procedure (Klemeš et al., 2010) Linear programming (LP) problems appear over a wide range of applications, including transportation, distribution from sources to sinks, and management decisions (see, e.g., Klemeš and Vašek, 1973; Jeżowski, 1990; Jeżowski, Shethna, and Castillo, 2003; Williams, 2006; El-Halwagi, 2006). LP problems can conveniently be solved by the simplex method (Dantzig, 1968) and its improvements (see, e.g., Maros, 2003a; Maros, 2003b). In most cases, NLP is difficult to solve, certain limitations on the constraints and objective function may be necessary for it to be practically solvable by specific methods (Seidler, Badach, and Molisz, 1980; Banerjee and Lerapetritou, 2003; Sieniutycz and Jeżowski, 2009). General technique for solving mixed-integer programming problems is the branch and bound framework (Land and Doig, 1960) where the original problem is solved via the systematic generation and solution of a set of relaxed subproblems. 30

50 Chapter 2 Literature Review Process synthesis is an inventive step; in fact, it is one of the earlier actions to be taken by the process designer to create the structure, network or flowsheet of a process, satisfying the given requirements in terms of constraints and specifications and attaining the prescribed objectives. In this thesis, P-graph approach is used for network synthesis which will be discussed later in Chapter 5. The relationships among the mathematical models, the process being modelled, and the solver being deployed are usually complicated, which makes it difficult to establish the most effective and valid model. There has only been a limited discussion of generating mathematical models in the literature, and the topic is covered in only a few publications (see, e.g., Grossmann, 1990; Kovacs et al., 2000) concerning specific areas. Tools for mathematical programming optimisation and process synthesis Process optimisation problems in chemical engineering are generally complex tasks of a considerable scale and comprehensive interactions. The application of information technology and computer software tools are essential for providing fast and as much as possible accurate solutions with a user-friendly interface. General purpose optimisation and modelling tools overviews have been available throughout the years of development, from dedicated conferences and publications (Klemeš and Vašek, 1973; Grossmann and Daichendt, 1996; Casavant and Côté, 2004; Gani, 2008; Friedler, 2009, 2010). A number of computer based systems have been developed to support process engineers in energy and mass balance calculations. However, due to the substantial ongoing funding needed for continuous development, only a limited number have remained on the market. They have only been secured by a substantial number of continuous sales. In this research work, GAMS (General Algebraic Modelling System), a high-level modelling language and efficient interface for mathematical programming is used. GAMS is designed for modelling linear, nonlinear and mixed-integer optimisation 31

51 Chapter 2 Literature Review problems. This system is tailored for complex, large-scale modelling applications and allows the user to build large maintainable models that can be adapted to new situations (GAMS, 2010). GAMS is especially useful for handling large, complex, one-of-a-kind problems which may require several revisions in order to establish an accurate model. The user can change the formulation quickly and easily, can specify different models, and can convert from linear to nonlinear models. GAMS was the first algebraic modelling language (AML), and is similar in form to similar programming languages. Models are described as algebraic statements which are easy to read for both for humans and machines. GAMS has the ability to formulate models within many different types of problem classes. The same data, variables, and equations within different types of models can be used at the same time. GAMS provides a range of different types of solvers for different types of models such as (i) BARON (Branch-And-Reduce Optimisation Navigator) for solving non-convex optimisation problems to global optimality, (ii) CPLEX for Linear Programming (LP), Mixed-Integer Programming (MIP), Quadratically Constraint Programming (QCP) and second order cone programs, and Mixed-Integer Quadratically Constraint Programming (MIQCP) based on the Cplex Callable Library (iii) DICOPT for solving MINLP models (iv) OQNLP for global optimisation of smooth constrained problems with either all continuous variables or a mixture of discrete and continuous variables. Various processes have been modelled by using GAMS e.g. synthesis of (i) Mass exchange networks (Chen and Ciou, 2007), (ii) Water networks (Chew et al., 2008; Tokos and Novak Pintaric, 2009), (iii) Biogas production (Drobez et al., 2009). 32

52 Chapter 2 Literature Review 2.5 P-graph Framework The P-graph framework is robust; its algorithms have been validated as mathematically rigorous in that they are based on a set of axioms (Friedler et al., 1992). These axioms express the necessary structural properties for feasible process networks. The algorithms are able to guarantee the resultant mathematical model s validity, reduce the search space, and generate the optimal solution. In a P-graph, one class of nodes is assigned to operating units or activities and the other to their inputs and outputs. Raw materials, resources (precursors), and preconditions (activating entities) are inputs to the operating units; products, effects (resulting entities), and targets are outputs from the operating units. Table 2.3 shows the P-graph representation of process structure elements. Table 2.3 P-graph symbols that represent process elements (Klemeš et al., 2010) Process element P-graph representation Raw material or precursor Final product or final target Intermediate material or entity By-product Operating unit In a process network, functional units that perform operations (e.g., mixing, reacting, separating) are termed operating units. These operating units, which correspond to the blocks in a process flowsheet, alter the physical and/or chemical states of materials being processed or transported. Such transformations are carried out by one or more unit operations, and the overall process converts raw materials into the desired 33

53 Chapter 2 Literature Review product(s). A process may also generate by-products, which are either recoverable for further use or treated as waste. In process network synthesis, a material is uniquely defined by its components and their concentrations - in other words, by its composition, which is identified by a symbol used to mark the material. Two classes of materials (or material streams) are associated with any operating unit: Input materials and output materials. For example, operating unit O 2 in Figure 2.7 consumes raw materials E and F while producing intermediate material C and by-product B. Note that a material may consist of more than one component. Figure 2.7 P-graphs representing the process structure of three operating units (Klemeš et al., 2010) The P-graph provides not only a formal description of the process but also an unambiguous representation of the possibilities for structural decisions. If an operating unit requires multiple inputs, each provided by a single operating unit, then structural alternatives cannot be defined. In contrast, if multiple operating units are capable of providing a particular input, then any combination of these units can, in theory, be used. In Figure 2.7 (a), for example, materials C and D are necessary inputs to operating unit O 1. Material C can only be produced by operating unit O 2, and material D can only be 34

54 Chapter 2 Literature Review produced by operating unit O 3. Thus, for unit O 1 to operate it is necessary that units O 2 and O 3 are both included in the process structure. In Figure 2.7 (b), however, material C can be produced by unit O 2, unit O 3, or both. In addition to unambiguous structural representation, the P-graph framework also provides a set of rigorous and effective algorithms for the synthesis and optimisation of process networks. The extreme complexity of process network synthesis is mainly due to the problem s combinatorial nature. This complexity grows exponentially with the number n of candidate operating units, because the optimal network must be found among 2 n possible combinations of the units (i.e., alternative networks) unless some possibilities can be eliminated (e.g., by heuristics) in advance. The factor 2 n is derived by simple induction. The first observe action is that a single additional decision (regarding the inclusion or exclusion of an operating unit) doubles the number of potential design alternatives: 2 n 2= 2 n+1. This means that a designer contemplating a system, e.g. with a total of 35 operating units, is faced with more than 34 billion (2 35 = ) alternative arrangements. Reducing such large a number of alternatives requires robust decision-making tools that are mathematically rigorous (preferably axiomatic) and effectively implementable on computers. These ends have been met largely by employing the well-established mathematics of graph theory, which can be regarded as a branch of combinatorics. Thus was developed the graph-theoretic, algorithmic method was developed, as described in this section. This method is based on using P-graphs to extract those universal combinatorial features (properties) inherent in feasible processes. Such properties can be expressed mathematically as a set of axioms that characterize the combinatorial feasibility of processing networks. A given process network is said to be combinatorially feasible (or to be a solution structure) if it satisfies the following five structural axioms (Friedler et al., 1995). (S1) Every final product and target is represented in the structure. 35

55 Chapter 2 Literature Review (S2) (S3) (S4) (S5) An entity represented in the structure has no input if and only if it represents a raw material or precursor. Every operating unit represented in the structure is defined in the problem. Any operating unit represented in the structure has at least one path leading to a final product or a final target. An entity belongs to the structure if and only if it is either an input entity to or an output entity from at least one operating unit already represented in the structure. Figure 2.8 depicts two process structures that are not combinatorially feasible. The P- graph in Figure 5.2 (a) shows a process structure in which material F is consumed as an input. Yet because material F is not a raw material and was never produced, the structure is not combinatorially feasible according to Axiom (S2). In the P-graph of Figure 2.8 (b), operating unit O 3 produces only by-product B. Here O 3 does not output any final product or material that is later used to yield a final product, so the process structure violates Axiom (S4). In short, the structural properties expressed by Axioms (S1) (S5) are necessary conditions for process structures to be feasible. This means that reducing the search space to combinatorially feasible structures does not result in the loss of any practically feasible or optimal processes. 36

56 Chapter 2 Literature Review Figure 2.8 P-graphs representing process structures that violate (a) Axiom (S2) or (b) Axiom (S4) (Klemeš et al., 2010). The P-graph s mathematical engine: MSG, SSG, and ABB When combined with the structural axioms, P-graph representation makes it possible to implement effective algorithms for the structural analysis, synthesis, and optimisation of process structures. The Maximal Structure Generation (MSG) algorithm (Friedler et al., 1992) generates a superstructure that can be rigorously proven to incorporate each combinatorially feasible process structure. Then the Solution Structure Generator (SSG) algorithm (Friedler, Varga, and Fan, 1995) is used to enumerate all the combinatorially feasible process structures that satisfy Axioms (S1) (S5) or the Accelerated Branch and Bound (ABB) algorithm (Friedler et al., 1996) is used to generate the optimal process structure together with a ranked, finite list of near-optimal structures. Figure 2.9 illustrates the connections among the three algorithms. Algorithm MSG generates the maximal structure, and can be followed either by algorithm SSG to generate the combinatorially feasible process structures or by algorithm ABB to generate the optimal and near-optimal processes. Algorithms MSG and SSG require the 37

57 Chapter 2 Literature Review list of candidate operating units as input, each defined by the set of its input materials - preconditions - and products (effects). Algorithm ABB requires, in addition to these data, quantitative information relevant to assessing network optimality e.g., prices of raw materials, costs and capacity constraints of operating units). Software tools for P-graph: PNS Editor PNS Editor (2010) is a software package solving Process Network Synthesis (PNS) based on P-graph approach. The aim of the PNS problem is to examine the feasible structures and select the optimum from among them. The optimal structure can be assessed in terms of cost, profit, etc. Both structural information (which functional units are connected and how) and sizing information (how much is produced from a given material) are needed in order to define the optimal structure,. Figure 2.9 Inputs to and outputs from the three P-graph algorithms (Klemeš et al., 2010). 38

58 Chapter 2 Literature Review The issues addressed by the PNS Editor are: (i) How to represent the building blocks of a process network? (ii) What are the solution structures of the problem? (iii) What is the maximal structure (which includes all solution structure)? (iv) What is the optimal structure? The maximal structure comprises all the combinatorially feasible structures capable of yielding the specified products from the specified raw materials. Certainly, the optimal network or structure is among these feasible structures. During the composition phase, the nodes are linked, again step wise and layer by layer, starting from the shallowest end, i.e., final-product end, of the remaining input structure by assessing if any of the linked nodes violates one or more of the axioms, as described in Friedler et al. (1996). MSG is performed transparently, as the maximum structure is the input for the Solution Structure Generation (SSG) algorithm. SSG gives all of the combinatorially feasible solution structures of a given problem. Often, the number of feasible structures is still too large to use for explicit enumeration. The ABB algorithm determines the optimal structure without generating all the solutions. It needs the structural relationships between materials, operating units and some additional information as to the costs of each raw material, fixed and proportional costs of the operating units, and constraints on quantity of materials or capacities of the operating units. This method has been implemented in several different process synthesis case studies e.g.: (i) Azeotropic distillation system (Feng et al., 2000), (ii) Heat exchanger network (Nagy et al., 2001), (iii) Reduction of carbon emissions involving fuel cell combined cycles (Varbanov and Friedler, 2008). 39

59 Chapter 2 Literature Review 2.6 Graphical targeting approaches for heat recovery systems, hydrogen recovery and supply chains Pinch Analysis (also called Process Integration) approach has been extensively used in the processing and power generating industry over the last 30 years and was pioneered by the Department of Process Integration, UMIST (now the Centre for Process Integration, CEAS, The University of Manchester) in the late 1980 s and 1990 s. Its further development resulted a methodology for integrating mass and water integration in particular. The main strategy of Pinch-based Process Integration is to identify the performance targets before starting the core process design activity. Following this strategy yields important clues and design guidelines (Klemeš et al., 2010, Friedler 2009, 2010). Pinch analysis was originally developed based on thermodynamic principles to identify optimal energy utilization strategies for process plants (Linnhoff et al., 1982). The Second Law of thermodynamics states that heat flows from higher-temperature to lower-temperature regions. The basic concept is to match the available internal heat sources with the appropriate heat sinks to maximize energy recovery, and to minimize the need of external utilities such as purchased fuels and cooling agents (see Figure 2.10). Any given pair of hot and cold process streams may exchange as much heat as allowed by their temperatures and the minimum temperature difference. 40

60 Chapter 2 Literature Review T [ C] Pinch 50 ΔT MIN = 10 C 0 Q C,MIN = 328 Q REC = 5912 Q H,MIN = 1168 ΔH [kw] Figure 2.10 Heat recovery with Pinch Analysis (Klemeš et al., 2010). This chapter focuses on the additional applications and especially recent developments, which have expanded the generic Pinch Analysis ideas in various other directions that related to energy planning and supply chain analysis. Design and management of hydrogen networks The evolution of Pinch Analysis has allowed mass integration to be extended to hydrogen management systems. In one of the earliest works in this field, Alves (1999) proposed a Pinch approach to targeting the minimum hydrogen utility. This method was based on an analogy with process heat recovery. Just as the distribution of energy resources in a plant can be analyzed and designed via using Pinch Analysis, so can the distribution of hydrogen resources be handled in refineries, which typically have several potential sources (each capable of producing a different amount of hydrogen) and 41

61 Chapter 2 Literature Review several hydrogen sinks (with varying requirements). However, the designer has more flexibility in determining the hydrogen loads of individual units by varying the throughput of units and operating many processes over a range of conditions. As a result, there is considerable potential for optimizing refinery performance. A two-dimensional plot of total gas flow rate versus purity represents the mass balance of each sink and source in the hydrogen network. A plot that combines the profiles for hydrogen demand (dashed line) and hydrogen supply (solid line) yields the hydrogen Composite Curve - CC (Figure 2.11). The sink and source profiles start at zero flow rate and proceed to higher flow rates with decreasing purity. The circled plus signs in the figure indicate the surplus - where sources provide more hydrogen than is required by sinks. Where the sources do not provide enough hydrogen to the sinks, a circled minus sign appears on CCs to indicate a deficit of supply. Figure 2.11 Composite Curves and hydrogen surplus diagram (Alves, 1999) The area beneath the entire sink curve is the flow rate of pure hydrogen that the system should provide to all the sinks. The area beneath the source curve is the total amount of pure hydrogen available from the sources. 42

62 Chapter 2 Literature Review For the hydrogen network to be feasible there should be no hydrogen deficit anywhere in the network; otherwise, the sources will not be able to provide enough hydrogen to the sinks. The hydrogen utility can be reduced by horizontally moving the curve toward the vertical (purity) axis until the vertical segment between the purities of the sink and the source touches the vertical axis, thereby forming the Hydrogen Pinch. Separating the hydrogen source and sink parts then determines the target value for the hydrogen utility minimum flow rate. The procedure for calculating the supply target requires varying the flow rate of gas supplied to the system until a Hydrogen Pinch is found (Alves, 1999). The sources from hydrogen-consuming processes or from processes generating hydrogen as a secondary product (dehydrogenation plants) have flow rates that are determined by normal process operation; these rates are assumed to be fixed for the purposes of designing a hydrogen network. However, process hydrogen sources with variable flow rates can be regarded as imports from external suppliers and from processes (i.e., steam reformers or partial oxidation units) that generate hydrogen as a main product. Those sources are hydrogen utilities. One approach to minimising hydrogen utility consumption is to increase the purity of one or more sources. A hydrogen purification system introduces an additional sink (feedstock for purification) and two sources (purified stream and residue stream), resulting in new targets. By employing Hydrogen Pinch Analysis, an engineer can make the best use of hydrogen resources in order to meet new demands and improve profitability. Pinch Analysis for production and supply chains The power of Pinch Analysis, which combines quality (e.g., temperature, concentration) with quantity (e.g., heat duty, mass flow), has been successfully applied to analyzing 43

63 Chapter 2 Literature Review supply chains. In this case, (reduced) time is the quality and the amount of material (e.g., number of units, mass) is the quantity. The objective of the aggregate planning is to satisfy demand in a way that maximizes profit. Demand must be anticipated and forecasted, and production must be planned in advance for that demand. Aggregate planning is particularly beneficial to plants whose products encounter significant fluctuations in demand. Such planning determines the total production level in a plant for a given time period, rather than the quantity of each stock keeping unit produced. Singhvi and Shenoy (2002) formulated the aggregate planning problem as follows. Given the demand forecast D t for each period t in a planning horizon that extends over T time periods, maximize the profit over the specified time horizon (t = 1,..., T) by determining the optimum levels of the following decision variables: Production rate P t = number of units produced in-house in time period t Overtime O t = amount of overtime worked in time period t Subcontracting C t = number of units subcontracted (outsourced) in time period t Workforce W t = number of workers needed for production in time period t Machine capacity M t = number of machines needed for production in time period t Inventory I t = inventory at the end of time period t Stock out S t = number of units stocked out (backlogged) at the end of time period t Figure 2.12 illustrates how material is accumulated at the end of a time period t. The accumulation of material balances can be expressed mathematically as (Previous inventory + Total production) = (Demand + current inventory) (2.1) 44

64 Chapter 2 Literature Review Figure 2.12 A mass balance in aggregate planning (Singhvi and Shenoy, 2002) These equations are reflected in the supply chain Composite Curves used for Pinch analysis, as shown in Figure Figure 2.13 Supply Chain Composite Curve (Singhvi, Madhavan, and Shenoy, 2004). Singhvi, Madhavan, and Shenoy (2004) extended this suggested methodology to the case of planning for multiple product scenarios. Singhvi (2002) proposed the following algorithm for minimizing inventory cost: 45

65 Chapter 2 Literature Review List all the products in order of increasing production rates, and produce the products in that order. For products that have the same production rate, first produce the one whose inventory holding cost is lower. For products that have the same production rate and the same inventory holding cost, first produce the one for which demand is lower. Using the Pinch to target CO 2 emission in energy planning Emission targeting via Pinch analysis was investigated in the 1990s by Linnhoff and Dhole (1993), and Klemeš et al. (1997). The applications, which employ the Total Site concept, address optimization within industrial facilities, not within regional or national energy sectors. However, a later work (Perry, Klemeš, and Bulatov, 2007) included the regional dimension in a Total Site Analysis of integrating renewable sources of energy (Varbanov and Klemeš, 2010) Tan and Foo (2007) presented a further application of Pinch Analysis to energy-sector planning under carbon emission constraints: Carbon Emission Pinch Analysis (CEPA). The main problems addressed by the proposed methodology are: 1. Identifying the minimum quantity of zero-emission energy resources needed to meet the specified energy requirements and emission limits of different sectors or regions in a system 2. Designing an energy allocation scheme that meets the specified emission limits while minimising use o f the energy resources. The sequence of the CEPA is as follows (Tan and Foo, 2007): i Tabulate the energy source and demand data. The resulting table must contain the quantity of the energy sources (S i ) and demands (D j ) and their respective emission factors (C out,i and C in,j ). 46

66 Chapter 2 Literature Review ii Arrange the energy sources and demands in order of increasing emission factors. iii Calculate the emission levels (S i C out,i ) and limits (D j C in,j ), respectively, of the energy sources and demands. iv Plot the demand Composite Curve (Figure 2.14) with the energy quantity (D j ) as the horizontal axis and the emissions limit (D j C in,j ) as the vertical axis. Hence the slope of the Composite Curve at any given point corresponds to the emissions factor (C in,j ). v Plot the source Composite Curve in the same manner as with the demand composite, but use instead the quantities S i and S i C out,j. In this curve, the slope at any given point corresponds to the emissions factor S i C out,j. vi Superimpose the two Composite Curves on the same graph. vii Shift the source CC horizontally to the right so that it does not cross the demand CC. In final position, the former should lie diagonally below and to the right of the latter. The two curves must touch each other tangentially without crossing; their point of contact is the Pinch point. viii Note the distance from the origin of the graph to the leftmost end of the source Composite Curve. This distance gives the minimum amount of zero-carbon energy needed to meet the system s specified emissions limits. ix Finding the Pinch point yields valuable insights to decision makers - in particular, it identifies the system bottleneck. The golden rule of Pinch Analysis can then be applied to the problem: in order to meet all the specified emission limits for the system, the zero-carbon energy resource is supplied only to those energy demands below the Pinch point. Any allocation of this resource above the Pinch point will either lead to an infeasible solution or require more zero-carbon energy than the minimum quantity established by Pinch Analysis. 47

67 Chapter 2 Literature Review Figure 2.14 Generating the energy demand Composite Curve with CO 2 constraint (Tan and Foo, 2007) Later on, the CEPA method is further applied in to several case studies such as in the Irish (Crilly and Zhelev, 2008) and the New Zealand (Atkins et al., 2010) electricity sector. 48

68 Chapter 3 REGIONAL ENERGY CLUSTERING This chapter presents the original research on the regional energy clustering method. Clustering is the partitioning of objects into a number of groups. Porter (1998) defines clusters as geographic concentrations of interconnected suppliers, services providers, associated institutions and customers in a region that compete but also co-operate. Clustering analysis has been widely discussed as a tool to improve the basis of regional competition and cooperation. It has been applied in the areas of marketing development, manufacturing and supply chain network design (Srinivasa and Moon, 1999; Beckeman and Skjoldebrand, 2007). The introduction of clustering approach has been emerging recently in the field of energy supply chains. In this work, a cluster is defined as a set of zones related through energy transfer links using local infrastructures. The benefits of the proposed clustering are: i Partitioning of the solution space and problem decomposition. This allows breaking down the initial complex problem into several problems of smaller size and complexity, greatly facilitating the modelling and solution efforts. ii Reducing the scope and size of the problems to consider allows adding more details and precision to the process models, thus increasing the confidence in the resulting supply chain networks. Forming clusters reveals the sets of zones, between which the biomass transfer is most beneficial. This original research minimise the CFP as the criterion for clustering. Since at this stage only biomass exchange is considered, CFP minimisation tends to also minimise the costs. A cluster combines smaller zones to secure sufficient energy balance within the cluster. A zone can be a province/county, a community settlement/ borough, an industrial park 49

69 Chapter 3 Regional Energy Clustering or an agriculture compound from the studied region. The REC is used to manage the energy balancing among the zones. The energy surpluses and deficits from various zones can be matched and combined to form energy supply chain clusters as shown in Figure 3.1. The algorithms and application of REC have been presented and published in several international conferences (Lam et al., 2008a; Lam et al., 2008b; Lam et al., 2008c) and journals of Resources, Conservation & Recycling (Lam et al., 2010a) and Computers and Chemical Engineering (Lam et al., 2010b). Zone Zone Zone Cluster 1 Zone Zone Zone Zone Zone Zone Zone Cluster 3 Zone Zone Zone Zone Zone Cluster 2 Zone Zone Zone Zone Zone Figure 3.1 Regional energy clusters (Lam et al., 2009a) 3.1 The REC Algorithm The Algorithm for Regional Energy Clustering (REC) is formulated in Figure 3.2. The algorithm steps are discussed next. 50

70 Chapter 3 Regional Energy Clustering Begin Specification of energy sources and demands Optimise biomass exchange flows between zones Display the optimal biomass exchange flows Cluster formation Clusters and their properties End Figure 3.2 REC algorithm flowchart (Lam et al., 2010b) Step 1. The specification of the data for energy sources and demands. The data structure for the specifications is shown in Table 3.1. The Table defines the available quantities of the potential energy sources, the bioenergy demands and the locations of the biomass collection points. The balance of each zone is calculated by subtracting the corresponding demand from the supply. Zones with positive balance form the set of the Source Zones and those with negative balance form the set of the Sink Zones. Each Source Zone, which supplies the energy that exceeds its demand, creates a surplus. Sink Zones are confronted with deficits. 51

71 Chapter 3 Regional Energy Clustering Table 3.1 Data structure for regional energy clustering Zone Centroid coordinate Area Potential Biomass Heating Value Energy Supply Bioenergy Demand (km, km) (km 2 ) (t/y) (MJ/kg) (TJ/y) (TJ/y) Z 1 (x 1, y 1 ) A 1 B 1 HV 1 S 1 D 1 Z 2 (x 2, y 2 ) A 2 B 2 HV 2 S 2 D 2 Z N (x N, y N ) A N B N HV N S N D N Energy supply, S i is defined as: S B HV (3.1) i i i where is the energy conversion factor from the raw materials to the final energy carriers such as heat and energy. Step 2. Optimise the biomass exchange flows between the zones. This step uses a LP formulation with the objective to minimise the total CFP within the given region. At this regional (inter-zone) scope the exchanges take place mainly via the transportation infrastructures, which use predominantly fossil fuels, sometimes with some addition of biofuels. It has been assumed that all fuels contribute some CFP. The CFP is in a way proportional to the overall transportation cost. However the cost has got some other contributors as the profit margin for transport as well sales, taxation and some others. For biomass transfer from Source Zones (i) Sink Zones (j), varying i = 1..N I ; j= 1..N J, the following objective function is defined: N N I J Min ( CFP) CFP i, j (3.2) i= 1 j= 1 52

72 Chapter 3 Regional Energy Clustering Bi, j CFPi, j FCi, j Disti, j CEF (3.3) C The distance for transporting the biomass (Dist i,j ) is a set of user-specified parameters. For quick estimations they can be approximated by the straight line distances between the zone centroids. For more realistic studies, road distances retrieved from Geographical Information Systems (GIS) or road maps should be provided. The following constrains are inherent to the problem. a) The total amount of biomass transported out from Zone (i) to other zones cannot exceed the available surplus AB i (Eq 3.5) and flows for transferring biomass to the same zone are forbidden (Eq 3.6). j B i, j ABi i., (3.4) AB i S D (3.5) i i S i and D i is the energy supply and demand from Zone (i). B i, j 0 i j (3.6) b) The total bioenergy delivered to Zone (j) cannot exceed the deficit in that zone: BE, TD, j (3.7) i i j j BE HV B, i j (3.8) i, j i i, j, TD j D S (3.9) j j HV i is the heating value for the particular biomass from Zone (i) and TD j is the total deficit in Zone (j). 53

73 Chapter 3 Regional Energy Clustering It is important to stress that the values for AB i and TD j are derived from the same data on the energy demand and supply by the initial balancing calculations. After their identification they are used in a different way. Conceptually AB i represent the upper limit of the energy can be transferred from the zone with surplus energy. TD j represents the upper limit of the energy that can be transferred to the deficit zone. c) The biomass flows in the system must be non-negative: B i, j 0, i, j (3.10) Step 3. Display the optimal biomass exchange flows. Illustrating the optimal biomass flows is an important feedback for the decision maker, representing visually the mapping of the exchanges between the zones. The zone centroids are plotted in 2-D Cartesian coordinates (Fig 3.3). For the convenience of orientation, the reference point is indicated as (0, 0). The bioenergy exchange flows (BE i j ) are shown with the corresponding directions and loads. 54

74 Chapter 3 Regional Energy Clustering y (km) y j Z j BE i, j y i Z i BE i+m, j y i+m Z i+m x i x j x i+m x (km) Figure 3.3. Visual representation of the optimal biomass exchange flows (Lam et al., 2010b) Step 4. Forming the clusters. Clusters formation is based on the principle that the residual bioenergy imbalance within the newly formed clusters is minimised (preferably zero). The step can be performed by various methods. Mixed Integer Linear Programming (MILP) is proven to be a convenient one. Once the biomass flows BE ij has been determined from Eq (3.10). The biomass flows model shown in Step I.2 is extended for clustering formation: a. The energy balance between the delivered in or transferred out from Cluster k is defined as j D i Si, j Yi, k 0 (3.11) j 55

75 Chapter 3 Regional Energy Clustering where D i is demand at location i, S i,j is supply to demand location i form supply j and Y i,k is a binary variable which denotes demand D i to Cluster k. b. The total length of transportation inside the cluster k,, is defined by the sum of all connections between demand and supply locations: Dist total k Dist Y i, j i, j i, k k (3.12) where Dist i,j is distance between the demand location i and supply j. The upper and lower bound for the total transportation length inside Cluster k is given by equations: total Dist k Dist i, j i, j k (3.13) Dist 0 k (3.14) total k c. The infrastructure maintenance of cluster k, M k, is defined by total demand i inside the cluster and the factor, ceff ik representing the efficiency of satisfying demand i if he belongs to Cluster k: 1 M k Di Yi, j Ceff i k K (3.15), k i d. Logical constraint which set the binary variably for connections between supply j and demand i, is: Y Si, j, k Yi, k i ; j; k i BE i, j (3.16) where BE(i,j) is a set of a priori identified demand-supply connections that define in Eq 3.10 and also illustrated in Figure 3.3. Activation of the previous binary variable Y Si j, k, helps us to allocate supply location j to cluster k to which is sent the largest supply from location j, Max S i, j applying the next logical constraint: F Y Y 0 S i; j; k (3.17) Max i, j Si, j, k Sj, k 56

76 Chapter 3 Regional Energy Clustering where Y, is a binary variable which assigns supply location j to Cluster k. F Sj k e. Logical constraint which ensures that demand i belongs to just one Cluster k is: k Y i, k 1 i (3.18) f. The objective function is: total TR M MIN Dist P M P (3.19) k k k Where P TR is transportation cost and is the maintenance cost. 3.2 Regional Energy Surplus-Deficit Curves (RESDC) The formed clusters should also be visualised to support the solution. For this purpose, the Regional Energy Surplus-Deficit Curves (Lam et al., 2010a) are proposed. They have been derived from for Process Integration background created by UMIST group. Process Integration background has been overviewed by Linnhoff et al (1982 and 1994), Klemeš et al. (2008) and the recent developments by Friedler (2009, 2010) and Klemeš et al. (2010). RESDC are a pair of monotonic curves one for the cumulative biomass energy supply and another representing the cumulative energy demand. The steps to build the pair of RESDC for a problem are: (i) Arrange the zones within each cluster in descending order according to their energy balance, starting with the largest surplus. (ii) Plot the energy supply curve with the cumulative area of the zones accounted for on the x-axis and the accumulated energy balance on the y-axis. The cluster with the smallest imbalance is assumed as the first cluster. (iii) Plot the energy demand cumulative curve in the same manner as in (ii). 57

77 Chapter 3 Regional Energy Clustering 3.3 Demonstration Case Study The study is focusing on the biomass transportation network and the supply chain synthesis. The upstream activities such as plantation, fertilising and harvesting are outside the boundary of the considered system. Their properties are not optimised or manipulated. The outcomes of such activities can be used as inputs to the biomass flow network optimisation. The algorithm described in Sections 3.1 is illustrated by using a set of data collected from a generalised Central European region. Step 1. Specification of the energy sources and demands. The information needed as an input to the REC procedure is shown in Table 3.2. It contains the relevant regional data potential biomass sources, energy demands, the area and location of the collection point for each particular zone. The assumptions for the major parameters are: i) Carbon Emission Factor, CEF = 2.69 (kg CO 2 )/L (IEA, 2005) ii) Fuel Consumption by a 20 t Heavy Duty Vehicles, HDV = 0.3 L/km iii) L f = 0.5 Taking the balance between the supply and demand inside each zone, the source and sink sets of the LP problem, described in Section 3.1, are formed. Zones 1, 2, 3, 5, 8 and 9 become Source Zones (i) and Zone 4, 6, 7 and 10 Sink Zones (j). Step 2. Optimise biomass exchange flows between zones. Using the obtained source and sink zones with their specified parameters, an LP optimisation problem described in Section 3.2 is formulated. The objective function for the LP problem becomes: 58

78 Chapter 3 Regional Energy Clustering Min ( CFP) = CFP CFP CFP 1,6 1,4 1,10 + CFP + CFP 2,6 + CFP 2,4 2,10 + CFP + CFP 3,6 + CFP 3,4 + CFP 3,10 + CFP 5,6 5,4 + CFP + CFP 5,10 + CFP 8,6 8,4 + CFP + CFP + CFP 8,10 9,6 9,4 + + CFP + 9,10 (3.20) Table 3.2 Regional data for demonstration case study Zone Area Location Biomass Heating Energy Energy Potential value Supply Demand (km 2 ) (km, km) (t/y) (MJ/kg) (TJ/y) (TJ/y) (0, 0) (4.4, 2.5) (5.3, 2.4) (4.1, 0.2) (7.9, 5.1) (6.4, 5.5) (2.4, 6.8) (9.4, 5.5) (3.2, 6.6) (2.3, 7.3) The LP problem has been solved by GAMS and the optimal biomass exchange flows, BE i,j obtained in the solution is listed in Table 3.3. Table 3.3 Optimised bioenergy exchange flows Variable Value (TJ/y) Variable Value (TJ/y) BE BE BE BE BE BE BE BE BE

79 Chapter 3 Regional Energy Clustering Step 3. Display the optimal biomass exchange flows. The optimal biomass exchange flows are further illustrated in Figure 3.4 where they are combined with the locations of the zone centroids (represented by the symbol). The biomass exchanges are represented by the corresponding flow magnitudes and also arrows, indicating the directions. y (km) Zone TJ/y 3.1 TJ/y Zone 7 Zone TJ/y 6.7 TJ/y Zone 6 Zone 8 Zone TJ/y 2.1 TJ/y 0 Zone TJ/y Zone 1 Zone tj/y Zone TJ/y x (km) Figure 3.4. Optimal biomass exchange flows resulting from the LP optimisation (Lam et al., 2010a) Step 4. Cluster formation. The cluster formation is presented in Table 3.4. It can be seen that the region is partitioned into 3 clusters. Zones 1, 2, 3, and 4 form Cluster 1. Zones 5, 6, and 8 are grouped into Cluster 2, and Zones 7, 9 and 10 into Cluster 3. Table 3.4 contains also columns (Zone, Area, Supply, Demand, and Energy balance), needed for the following cluster visualisation. The composite behaviour of the clusters is further illustrated in the RESDC (Figure 3.5). The solid line is the cumulative supply curve, and the dashed line represents the cumulative demand. Figure 3.5 indicates the size of each cluster and the total energy involved in the supply chain within the cluster. Since each cluster is a group of entities with relatively stronger links, this provides an opportunity to develop efficient energy planning and management strategies within a 60

80 Chapter 3 Regional Energy Clustering simpler supply chain, confined within the cluster, rather than comprising the whole region. Table 3.4 Cluster properties Cluster Zone Area Supply Demand Energy balance Imbalance within the cluster (km 2 ) (TJ/y) (TJ/y) (TJ/y) (TJ/y)

81 Chapter 3 Regional Energy Clustering Figure 3.5 RESDC for the case study (Lam et al., 2010a) 3.4 Further Development of REC There are several works that being extended based on the REC algorithm result CFP Pay-back analysis The CFP Pay Back Analysis can be applied to assess projects for retrofit or creation of new infrastructure in the supply chain network (Lam et al., 2010a). In many cases the economy has been a decisive criterion and economical pay-back figures can be analysed and used as a trade-off to the CFP. The need to reduce CO 2 emission motivates the suggestion of constructing an alternative transportation route (road, railway or water transportation) which could reduce the transportation CFP and cost. Pay back analysis is a method to determine the break-even point when an investment cost and the CFP from the construction will generate a positive return. It is used to evaluate the environmental and economic tradeoff. This method assesses the consequences of the alternative route construction cost 62

82 Chapter 3 Regional Energy Clustering and CFP comparing with the existing condition. The equations for CFP pay back (PB CFP ) and cost pay-back (PB Cost ) analysis are given in Eq 3.21 and Eq 3.22: CCFP PB CFP (3.21) CFP - CFP * PB Cost CC (3.22) FS CT where, CCFP is the Construction CFP, CFP* is the CFP of the new road. CC is the cost of new road construction, FS is the cost saving from fuel due to the shorter distance, CT is the carbon tax for the diesel/petrol. Usually there is already some infrastructure in place in each region. Therefore, one useful tool of evaluating the potential benefits of optimizing the supply chain is the pay back analysis for infrastructure investments. For example, let us assume that 1 km of new road is to be built between Zone 1 and Zone 2, which will shorten the transportation distance by 0.3 km. The pay back analysis for 1 km new road construction is carried out based on the following assumptions: i) CFP for 1 km alternative road construction = 18,000 kg CO 2 (Olsson et al., 2006) ii) The HDVs for biomass transportation used 10% of the road capacity iii) The CFP for road maintenances are assumed the same for existing road and the alternative road, therefore they will be cancelled out in the calculation. iv) Cost for 1 km alternative road construction = 10,000 (Jaarsma and Dijk, 2002) v) Carbon tax = 15 /(t CO 2 ) vi) Petrol price = 1 /L Figure 3.6 demonstrates the pay-back period for construction cost and CFP. The crossing point between the line of PB cost and PB CFP with the horizontal reference line 63

83 Total CFP and cost saving Road construction CFP Road construction Cost Chapter 3 Regional Energy Clustering given that, the pay back are 1.1 y and 1.4 y. These figures depend on the cost level and can change with their fluctuation. However the selected case study should provide basic information for the price values selected. This shows that the alternative road will take longer period to generate the positive cost revenue compared to the positive CFP saving. kg CO PB Cost 0 year PB CFP Figure 3.6 CFP Pay Back Analysis for alternative road construction (Lam et al., 2010a) The pay back period may change subject to the values of the parameters. Especially the pay back period in term of cost very much depends on the price of the fossil fuel. The PB Cost will equal to the PB CFP when the price for fuel increases from 1.0 to The carbon tax has smaller contribution compared to the fuel price. It needs to be increased from 15 /t CO 2 to 129 /t CO 2 to match the PBCost and PBCFP. The pay-back analysis also provides the information of total cost and CFP saving for required period which is one of the important decision making factors in infrastructure management. The result of CFP Pay Back Analysis for every possible link can be 64

84 Chapter 3 Regional Energy Clustering ranked and it gives the priority of which option of infrastructure should be considered first in the regional planning Application of REC for Waste-to-Energy Supply Chain The REC method has been extend for synthesising of Sabah (Borneo, Malaysia) Palm Oil Biomass supply chain under the collaboration between University of Pannonia, Hungary; University of Nottingham Malaysia Campus, and University of Technology Malaysia. The results are reported in Lam et al. (2010d). The main contribution of author of this Thesis is providing the methodology and solving the problem in GAMS. Palm oil biomass (POB) residues such as palm empty fruit bunches (EFB), palm fibres and palm shell obtained from the milling process have good potential to meet the targeted share of renewable energy in Malaysia. After some simple pre-treatment processes (e.g. drying and densification), POB with high heating value may be used as the feedstock for combined heat and power (CHP) plant. The generated heat and power may be used by the local plant where CHP is based, while the excess power from the CHP can then be sent to the national grid system. EFB is the most abundant and widely available POB residue in the mill (the other POB are normally burnt within the mill). In most cases, EFB are transported out from the mill by empty HDV that bring in fresh fruit bunches from the nearby oil palm plantation estates. Hence, the CO2 emission of these HDV is of concern in the planning of regional energy supply chain that is based on palm oil EFB. Typical location of palm oil plantation are distributed in the rural areas, the relatively low energy density (energy per unit volume) and the distributed nature of the sources require extensive infrastructure and huge transport capacity for biomass supply. For POB supply chains, road transport is the usual mode for collection and transportation of 65

85 Chapter 3 Regional Energy Clustering the fuel. As a result the heavy road transport increases the CFP of the biomass energy supply chain. A Malaysian case study is used to illustrate the developed model presented in Section 3.1. An illustrative case study is used here to illustrate the application of the proposed model. Data of the case study is based on the actual palm oil mills, refineries and oleochemical plants in the northern part of Borneo Island Malaysia (Figure 3.7). However, actual names of the companies are not shown for business propriety reason. Kota Kinabalu Sandakan Lahad Datu Figure 3.7 Location of EFB suppliers and consumers (Lam et al., 2010d) Table 3.5 shows the suppliers and consumers of the EFB. The former is a group of palm oil mills where EFB is produced. In most cases, these mills are owned by small enterprises without plantation estates that may utilise the EFB (as fertiliser). On the other hand, the EFB consumers are palm oil refinery (C1) and oleochemical plant (C3) that experience energy deficit. It is assumed that combined heat and power (CHP) plants are built in these plants to generate power for plant usage. Besides, a new CHP plant is also planned for an existing mill, i.e. C2. In other words, C2 is essentially the same plant as S8. The geographical location for these plants is shown in Figure 3.7; while their distance from each other (suppliers and consumers) is given in Table

86 Chapter 3 Regional Energy Clustering Table 3.5 Data for EFB Suppliers and Consumers EFB Desired power EFB Suppliers capacity Consumers output requirement (t/y ) (MW) (t/y ) S1 90,000 C ,000 S2 75,000 C ,000 S3 80,000 C ,000 S4 85,000 S5 82,000 S6 86,000 S7 92,000 S8 78,000 S9 80,000 S10 88,000 S11 84,000 Table 3.6 Distance between EFB suppliers and consumers (km) C1 C2 C3 S S S S S S S S S S S

87 Chapter 3 Regional Energy Clustering Table 3.7 shows the parameter used for the calculation for the new CHP plant. Solving the objective function yield the minimum CO 2 emission at 304,254 kg/y. The optimum allocation scheme is shown in Table 3.8. Table 3.7 Parameter for CHP calculation Steam requirement for turbine 5.8 kg steam/ kw power Boiler design capacity 80 t/h steam Boiler load factor 85 % Energy required to raise 1 kg stream 3195 kj/kg at 50 bar Thermal efficiency of boiler 85% Average calorific value of dry solids kj/kg solid Moisture in EFB 30 % Annual operation 8,000 h Table 3.8 Allocation of EFB between Suppliers and Consumers (t/y) C1 C2 C3 Unutilised S1 90,000 S2 72,000 3,000 S3 80,000 S4 85,000 S5 32,000 50,000 S6 86,000 S7 92,000 S8 78,000 S9 80,000 S10 88,000 S11 22,000 62,000 68

88 Chapter 3 Regional Energy Clustering 3.5 Chapter Summary A new procedure for regional energy clustering has been develop and demonstrated with a case study on CFP minimisation and regional energy management. A set of biomass transfer flows with minimum CFP is given as the solution of the supply chain. The result is further analysed with graphical display which provides decision makers easier and faster solution rather than using mathematical calculation. After the optimised supply chain flows are obtained with the developed algorithm, a demonstration case shows that how the formation of energy clusters graphically represented by RESDC. This curve visually shows the grouping of energy cluster within the boundary of the supply and demand curves. This provides an opportunity to develop efficient energy planning and management strategies within a simpler supply chain compared to the whole region network. CFP Pay back analysis is applied to assess the suggestion of building alternative infrastructure in the cluster. The pay back period and the total saving of CFP and investment cost for construction can be used as the decision factors for regional planner. The REC algorithm has also been extended for waste-to-energy network synthesis under the collaboration within 3 universities namely (i) University of Pannonia, Hungary, (ii) University of Nottingham Malaysia Campus, Malaysia and (iii) University Technology Malaysia, Malaysia (Lam et al., 2010d). The REC approach is going to be extended to into: (i) The studied region by using RMC, see Chapter 4. (ii) Synthesis of supply chain to manufacture biogas and liquid biofuel as alternative biomass products. A proposed efficient synthesis and optimisation tools is P-graph algorithms (Friedler et al., 1992) see Chapter 5. 69

89 Chapter 4 REGIONAL RESOURCE MANAGEMENT COMPOSITE CURVE The exploitation of the energy potential in biomass in a specific geographical region is frequently constrained by high production costs and the amount of land required per unit of energy generated. In addition, the distributed nature of the biomass resource and its normally low energy density may result in large transportation costs. Previous chapter on Regional Energy Clustering (REC) have been extended here to tackle simultaneously the issues of the biomass supply chain and transportation and land use. The Regional Resources Management Composite Curve, in this thesis it is shortened to RMC. This original research outcome is a tool for supporting decision making in regional recourse management. It provides a complete view of energy and land availability in a region, displaying their trade-offs in a single plot. RMC can be obtained with two steps. The first step presents the Regional Energy Cascade Analysis, which estimates the energy target within regional supply chains and provides the result for energy exchange flows between zones, the quantity of energy required to be imported/exported, and the locations of the demands. In the second step, the initial results are analysed against potential measures for improving the energy and land use targets by using the RMC and a set of rules for its manipulation. The presented method provides the option to assess the priorities: either to produce and sell the surplus energy on the fuel market or use the land for other purposes such as food production. This extended approach is illustrated with a comprehensive case study, which demonstrates that with the RMC application it is possible to maximise the use land and to maximise the biofuel production for the requested energy demand. 70

90 Chapter 4 Regional Resource Management Composite Curve 4.1 Construction of Regional Resources Management Composite Curve The RMC has been developed by the PhD candidate (Lam et al., 2009a, 2009b; Lam et al., 2010a) to graphically represent the relationship between the land use and the generation and consumption of energy. The RMC is a graphical tool that can be used to support the decision-making process in regional resource management, as shown in the next sections. The principle idea of a Grand Composite Curve (Linnhoff and Townsend, 1983) has been exploited and translated to the problem of regional resource management. For regional resource management, the x-axis represents the energy supply/demand profile (TJ/y) and the y-axis represents the cumulative land area for the studied region. The pockets represent the supply-demand among the zones. The procedure for the RMC construction is: 1. Construct a Regional Energy Cascade Analysis for biomass transfer between zones. i) Create a sequence in the descending order for cluster imbalance. Start with zone with the largest energy surplus. ii) Calculate the energy land use rate, L for each zone. Ai L i, Si Di 0 (4.1) S D i i where, A i is the total area of Area for Zone i, S i and D i is the bioenergy supply and demand from Zone i. Then surplus/ deficit (S i D i ) cannot be zero value as zero surplus/ deficit mean, the zone is self-supported and shouldn t be considered in the energy supply chain targeting. iii) Sequence the zones within the cluster by descending value of L i. iv) Cascade the surplus or deficit value of each zone by using bottom-up or topdown direction. Each zone size becomes a cumulative area interval for the plot. The cascade is just a cumulative tool and it doesn t reflect to the particular links between the clusters. 71

91 Chapter 4 Regional Resource Management Composite Curve 2. Plot the RMC with the cumulative area (km 2 ) as y-axis and the accumulated energy balance (TJ/y) on the x- axis. The coordinates for the points in the RMC represent the accumulated area and the surplus/deficit value from cascade interval. Figure 4.1 shows two options of presenting the RMC. The energy cascading can be performed from the bottom up (a) and from the top down (b). In this paper, the bottom-up cascading is used. Cumulative Area (km 2 ) Cumulative Area (km 2 ) Zone 5 H A B Zone 4 Zone 3 Zone 2 Zone 1 E F H F E C D G Zone 5 Zone 4 Zone 3 Zone 2 E F C D G A B Cumulative energy balance (TJ/y) Zone 1 H Cumulative energy balance (TJ/y) (a) Bottom-up cascaded RMC (b) Top-down cascaded RMC Figure 4.1. Constuction of the RMC (Lam et al., 2010c) RMC puts together the information about energy surpluses/deficits as well as land use, allowing for a direct assessment of the trade-off between them. The curve from Point A to E in Figure 4.1 represents Cluster 1. From the left-hand turning Point E, to Point H the curve represents Cluster 2. The intra-cluster energy transfer (supply-demand relationships) between the zones is represented by the shaded areas or Pockets B-C- D-E, F-G-H. For the bottom-top cascaded RMC, as shown in Figure 4.1 (a), the zones with positive slope supply biomass to the demanding zones which are with the negative slope as 72

92 Chapter 4 Regional Resource Management Composite Curve direction shown by arrows in the figure. Since the zone areas are fixed, a steeper positive slope means less net energy is available from a zone. A steeper negative slope means the net energy demand for the zone is smaller. The parts of the RMC plotted on the right hand side of the y-axis represent the activities within the studied region. Based on the RMC several options are possible to tackle the problem of resources management in a region. They are mainly based on the issue of energy surplus and deficit and the land use management. After the bottom-up cascaded RMC has been constructed there are 5 basic rules to be applied while manipulating regional resources such as land and the surplus energy. These rules give a clear overview picture and useful hints to the planner on how to manage regional resources with a single graph. Rule 1: If m TJ/y of surplus energy is being planned for export from Zone i, the curve of Zone i and all of the curves after that are shifted together to the left horizontally by m TJ/y Rule 2: If an extra land of n km 2 from Zone i is being used for other purposes instead of energy production the segment given by n km 2 is cutting out and shifting the curves above it to the left. Rule 3: If p TJ/y of external energy is being planned for import to Zone i, the curve of Zone i and all of the curves after that are shifted together to the right horizontally by p TJ/y Rule 4: If the production rate for energy crops in Zone i is increasing or the energy demand is decreasing the slope of the curve is decreased. Rule 5: If the production rate for energy crops in Zone i is decreasing or the energy demand is increasing the slope of the curve is increased. 73

93 4.2 Demonstration Case Study Chapter 4 Regional Resource Management Composite Curve A demonstration case study is used to illustrate how to implement the suggested rules. The information about the potential biomass sources, the area and location for a particular zone are shown in Table 4.1. The utilisation percentage of the biomass is set as 60 % of the theoretical potential bioenergy which is the product of potential biomass (t/y) and its heating value (MJ/kg). The study is focusing on the biomass production and its transportation network from the source point to the demand site. Table 4.1. Regional data for demonstration case study for RMC case study Zone Area Location Potential Biomass Heating value Energy Supply Energy Demand (km 2 ) (km, km) (t/y) (MJ/kg) (TJ/y) (TJ/y) 1 30 (0, 0) (8, 1) (8, 5) (10, 4) (12, 10) (14, 9) (18, 10) The case study considers the local community in the studied region as the energy consumers. The biomass surplus is transported from the collection point nearby the sourcing location to the biomass energy conversion plant to support the regional energy demand. A co-generation power plant with biofuel boilers is used for this purpose. 74

94 Chapter 4 Regional Resource Management Composite Curve The optimal biomass transfer flows estimated by REC algorithm are shown in Figure 4.2 The biomass transfer is indicated by the corresponding flow magnitudes and the arrows indicate the transfer directions from the energy surplus zones to deficit zones. The locations of the centroids are represented by the symbols. It can be seen that the region is partitioned into 2 clusters. Zones 1, 2, 3, and 4 form Cluster 1. Zones 5, 6, and 7 are grouped into Cluster 2. y (km) 2.25 TJ/y Zone 5 Zone 7 Zone 6 1.0TJ/y Cluster TJ/y 0 Cluster 1 Zone 3 1.5TJ/y Zone 1 Zone tj/y Zone TJ/y x (km) Figure 4.2 Optimal biomass transfer flows resulting from the REC algorithm (Lam et al., 2010c) The RMC construction is illustrated in Table 4.2. The data is arranged according to the procedure described in the Section 4.1. This data is then plotted in a RMC (Figure 4.3). The RMC displays the information about energy surpluses/deficits as well as land use, allowing assessing the trade-off between them directly. 75

95 Table 4.2: Data for RMC construction Chapter 4 Regional Resource Management Composite Curve Cluster Zone, Z Area (km 2 ) 1 2 Surplus/ Deficit (TJ/y) L i (km 2 /TJ/y) Cumulative Area (km 2 ) Zone 5 Zone 7 Zone 6 Zone 2 Zone 4 Zone 3 Zone S R Cluster 2 Q O P N Cluster M L K -1 0 J Cumulative energy balance (TJ/y) Figure 4.3 Energy and land use management with the RMC (Lam et al., 2010c) The first interval has a surplus of 1.0 TJ/y, which is cascaded to the next interval. This cascaded result is reflected as point L on the RMC on the right hand side. The second interval and the third interval have also 1.50 TJ/y and 2.00 TJ/y, which accumulate to 4.50 TJ/y (Point N) to be cascaded further. In the forth interval, the zone has a deficit of 76

96 Chapter 4 Regional Resource Management Composite Curve 3.75 TJ/y, which leaves 0.75 TJ/y (Point O) to be cascaded to the next interval and so on. The RMC also shows that, the region is divided into 2 clusters. Each left-hand turning point (could be also called a cluster pinch) indicates the start of a new cluster. The cluster will have surplus of biomass energy if the last point is plotted right side of the starting point otherwise the cluster will have energy deficit. The energy balance for the whole region is a deficit 0.50 TJ/y which represented by Point S that plotted on the left hand side of the y-axis. Figure 4.3 also indicates the size of each cluster. There are several possible options to tackle the energy-land trade-off problem. The segment between Points J and K in Figure 4.3 represents the surplus in Cluster 1. Three options can be considered to deal with this surplus: i. Export 0.75 TJ/y of energy from Zone 1 to energy market. Figure 4.4 shows that, the whole composite profile is moved to the left 0.75 TJ/y (Rule 1). The Point J is plotted on the left hand side of y-axis; this means that 0.75 TJ/y is transported to the market. Cumulative Area (km 2 ) K K -1 J 0 J Cumulative energy balance (TJ/y) Figure 4.4 Modification of the RMC if the surplus in Cluster 1 is exported to energy market (Lam et al., 2010c) 77

97 Chapter 4 Regional Resource Management Composite Curve ii. Use the extra land of 22.5 km 2 from Zone 1 for other purposes instead of energy production. As shown in Figure 4.5 the new point, K is created by cutting out the segment 22.5 km 2 and shifting the remaining part of the RMC 0.75 TJ/y to the left. (Rule 2). Cumulative Area (km 2 ) Original curve K K 22.5 km TJ/y Cumulative energy balance (TJ/y) Figure 4.5. Modification of the RMC if a certain area in Zone 1 is used for other purposes (Lam et al., 2010c) Another option is using km 2 of area from Zone 4. As illustrated in Figure 4.6 Point T is shifted by 0.75 TJ/y to T. A gap of km 2 area appears on the resulting RMC. 78

98 Chapter 4 Regional Resource Management Composite Curve Cumulative Area (km 2 ) T M T Cumulative energy balance (TJ/y) Figure 4.6 Modification of the RMC if a certain area in Zone 4 is used for other purposes (Lam et al., 2010c) iii. Transfer 0.75 TJ/y of energy to Cluster 2. This situation is presented by Figure 4.7. Firstly the original RMC in Fig. 4 moved to the left. The intermediate step is resulting in Figure 4.4. The exported energy (Segment J K ) is transferred to Cluster 2 (as shown by the arrow). As a result, the segment of 22.5 km 2 land is cut out (Rule 2) and the point after O is horizontally moved 0.75 TJ/y to the right (Rule 3) 79

99 Chapter 4 Regional Resource Management Composite Curve Cumulative Area (km 2 ) TJ/y 100 O 50 K -1 J Cumulative energy balance (TJ/y) Figure 4.7 Modification of the RMC if the surplus in Cluster 1 is transferred to Cluster 2 (Lam et al., 2010c) For the deficit case in Figure 4.3, the energy demand can be satisfied by i. Import 1.25 TJ/y from the energy market. Figure 4.8 shows that after received the import, the pocket for Cluster 2 has been expended by moving 1.25 TJ/y to the right after Point O (Rule 2). 80

100 Chapter 4 Regional Resource Management Composite Curve Cumulative Area (km 2 ) O O 1.25TJ/y Cumulative energy balance (TJ/y) Figure 4.8 Modification of the RMC if a certain amount of energy is imported to fulfill the demand in Cluster 2 (Lam et al., 2010c) ii. Increase the biomass production rate in the Cluster 2 (Rule 4), e.g.: - Convert certain areas of land from other applications to energy crops. - Grow other type of energy crops with higher yield. - Increase the share of agriculture residues into energy sources, for example use straw for energy production instead of using it for animal feed. The effect of these changes decreased the slope of OP and PQ to OQ in Figure

101 Chapter 4 Regional Resource Management Composite Curve Cumulative Area (km 2 ) Q Q 0 Slope PQ = 11.1 Slope OP P = 15.0 Slope OQ = O Cumulative energy balance (TJ/y) Figure 4.9 Modification of the RMC if the biomass production rate in the Cluster 2 is increased (Lam et al., 2010c) iii. Reduce the energy demand in Cluster 2 by improving the efficiency of energy conversion technologies (Rule 5). The slope of QS increased as shown in Figure

102 Chapter 4 Regional Resource Management Composite Curve Cumulative Area (km 2 ) 20 0 S S Slope S Q = Slope SQ = 5.6 Q Cumulative energy balance (TJ/y) Figure 4.10 Modification of the RMC if the energy demand in the Cluster 2 is reduced (Lam et al., 2010c) 4.3 Chapter Summary The chapter presents an extended approach for regional resource management. Previous work has been enhanced by the Regional Energy Cascade Analysis. The cascade has proved to be a useful tool to assess the energy target within regional supply chains where the energy planning and management is extended to the evaluation of the trade-off between biomass generation and land use. A case study has demonstrated the potential of the RMC for decision-making support in regional resource management. It has shown by using a step by step approach how the RMC graphically represents the relationship between the land use and the generation and consumption of energy. This graphical method gives a clearer picture of how to manage and manipulate regional resources into a desired target. The potential savings based on the 83

103 Chapter 4 Regional Resource Management Composite Curve application of this methodology could be considerable and substantially decrease both the cost and emissions, including the CFP, of the biomass supply chain. It provides the regional policy maker with guidance and useful results for energy flow evaluation. It shows how much energy is required to be imported/exported and the locations of the demands. The RMC helps to illustrate the potential of different options of the regional resource planning. In addition to biomass production, solar and wind collectors also occupy significant land space, and these scenarios could be included in a wider case study in the future. RMC should be also further developed and supported by a decision making tool. The comprehensive decision analysis for regional resources management should be extended to cover (i) economic and environmental pay back analysis, (ii) the regional development plan. 84

104 Chapter 5 OPTIMISATION OF REGIONAL RENEWABLE ENERGY SUPPLY CHAINS: P-GRAPH APPROACH This chapter demonstrates the original research on the application of P-graph approach to syntheses a regional renewable energy supply chain. After the region zones are grouped into clusters, the biomass energy supply chains within each cluster can be synthesised. The supply chain involves a variety of potential operations of different types. These include the operations within zones and also the activities among the zones, which can be biomass conversion plants, energy recovery technologies, and transportation and delivery operations. This chapter will start with the P-graph procedure for biomass supply chain synthesis and then the demonstration of a case study. 5.1 P-graph Procedure for Biomass supply chain Synthesis The procedure for the supply chain network synthesis inside each cluster follows the algorithm illustrated in Figure 5.1. To apply the P-graph approach certain types of information need to be obtained, evaluated and specified. Most of the necessary information has already been generated during the clustering stage. 85

105 Chapter 5 P-graph Approach Begin Identification of materials and streams Identification of the candidate operating units Maximal superstructure and solution structures generation Optimisation of the superstructure. Optimal Biomass Energy Supply Chain Network End Figure 5.1 P-graph procedure for biomass supply chain synthesis (Lam et al., 2010b) Step 1. Identification of the involved materials and streams. This is a preprocessing step, to prepare input information for Step 3. The materials involve raw biomass, products (heat, power, pellets and ethanol) and intermediates (pellets, syngas, biogas, etc). The material prices follow a sign convention. Inputs are assigned positive prices if the plant has to pay for them and negative ones if it receives payment. Similarly all outputs generating revenues are assigned positive prices and those generating costs negative prices. 86

106 Chapter 5 P-graph Approach Step 2. Identification of the candidate operating units, their capital cost and unit s performance. This is also a pre-processing step preparing the input information for Step 3. The operating units are of both conversion and transportation types. The capital costs of all operating units have been assumed to change linearly adhering to the form given in Eq. 13: CC A CC B CC UCap (5.1) where the operating unit capacity is measured by its throughput of a key inlet stream. Background for more detailed evaluation of the capital costs and an appropriate assessment has been published by Taal et al. (2003). Step 3. Generation of the maximal superstructure and all the combinatorially feasible individual networks between the involved materials and streams with the candidate bioenergy conversion units. This step is performed internally by the P-graph algorithms MSG and SSG. Step 4. Optimisation of the generated superstructure. This results in the selection of the optimal network by using the ABB algorithm (Friedler et al. 1996). The latter selects the bioenergy conversion units and the quantities of the streams that formed the optimal network. More detailed explanation of ABB algorithm has been provided by (Friedler et al. 1996; Nagy et al. 2001). 5.2 Demonstration Case Study The Regional data for demonstration case study in Table 4.2 is repeated here. Once the clusters are obtained, a biomass supply chain can be synthesised inside each cluster using the P-graph framework tools. The data for Cluster 1, given in Table 4.4 is used for demonstrating P-graph procedure that described in the previous section. 87

107 Chapter 5 P-graph Approach The biomass types from Zones 1, 2, 3, and 4 are wood, sweet sorghum, grass silage and MSW respectively. The synthesis accounts for the locations of the energy carrier conversion operations. Figure 5.2 shows the brief schematic structure for the feasible process combinations that may form the supply chain. The symbol represents a material such as raw biomass, intermediate energy carriers, or products. The labeled boxes represent the operation units and the symbol T with a frame represents the transportation activities. The heat to power ratio of the customer demands is assumed 2:1, which is the average value for Europe during 2003 (ECOHEATCOOL, 2006). As a result, customer energy demands by zones become 1.13 TJ/y heating and 0.57 TJ/y power for Zone 1, 3 TJ/y heating and 1.5 TJ/y power for Zone 2, 7.87 TJ/y heating and 3.93 TJ/y power for Zone 3, and for Zone TJ/y heating with TJ/y power. Forestry wood, energy crops (grass and sweet sorghum) and Municipal Solid Waste (MSW) are the input of raw materials. They are converted into other energy carriers, having higher energy densities and suitable for use in power generation facilities. CHP systems combining Fuel Cells, biofuel boilers, steam turbines and gas turbines are defined as options to be used for the regional energy conversion system. The electricity produced is supplied to the cluster customers. The generated heat is used for domestic, commercial and industrial applications, mainly for space heating and as hot utility. Step 1. Identification of materials and streams. The synthesis method requires a comprehensive list and information of materials, as well as another list for the candidate operating units, as described in Section 5.2. The materials and streams for Cluster 1 biomass supply chain have been identified (Step 1 of the procedure from Section 5.2) and are shown in Table

108 Chapter 5 P-graph Approach Step2. Identification of the candidate operating units. The candidate operating units have been indentified using a qualified assessment using the general workflow from Figure 5.2 as a guide. They are listed in Table 5.2. For each candidate operating unit the table provides the streams/materials accepted as inputs, the outputs, the estimated performance and capital cost coefficients. The unit performance and economic data have been estimated to provide the basis for appropriate economic evaluation of the designs. Step 3. Maximal superstructure and solution structures generation. Combining the information from Tables 5.1 and 5.2, the P-graph MSG algorithm has built the problem superstructure (referred to as the Maximal Structure), which is shown in Figure 5.2 Step 4. Optimisation of the superstructure. The software tool Combinatorial Process Network synthesis Editor (PNS Editor, 2009) was used to obtain the optimum solution for minimum production cost. The optimum solution provides the selected pathways which include: Input biomass quantities Type of energy carriers (input and intermediate materials) Operating units Final products for customers The result from the P-graph ABB optimisation algorithm is illustrated in Figure 5.3. Firstly, t/y of wood from Zone 1 (biomass A) are pelletised, transported, and used as fuel feed for the gasifier in Zone 1 as well as for the boilers in Zones 1, 2 and 3. Pellets A (wood origin) of t/y are used to generate heat in Zone 2 by the home bioenergy conversion unit. Another product stream, t/y of wood is sent to Zone 4 for direct use by steam boilers. Biomass B (sorghum) from Zone 2 is converted to t/y bio-ethanol for the energy market. 89

109 Figure 5.2 Combinatorially feasible process structures (Lam et al., 2010b) Chapter 5 P-graph Approach 90

110 Chapter 5 P-graph Approach Table 5.1 Materials and streams used in the case study Symbol Description Price A Forestry Wood 25 /t B Sweet Sorghum 20 /t C Grass Silage 15 /t D Biomass MSW -10 /t AZ1, 2, 3, 4 Biomass A Transported to Zone 1, 2, 3 and 4 30 /t Ptr Petrol 1 /l CO 2 CO 2 emission - PA Pellet from Biomass A - PAZ1, 2, 3 and 4 Pellet A transported to Zone 1, 2, 3 and 4 - PC Pellet from Biomass C - PCZ1, 2, 3 and 4 Pellet C transported to Zone 1, 2, 3 and 4 - SG 1,2,3 and 4 Syngas generated from Zone 1,2,3 and 4 - BG 1,2,3 and 4 Biogas generated from Zone 1,2,3 and 4 - Steam 1,2,3,4 Steam generated from Zone 1,2,3 and 4 - Ba Sorghum Bagasse after juice extraction - Juice Sorghum Juice after juice extraction - ETN Ethanol - EEM Ethanol for Energy Market 350 /t PAEM Pellet A for energy market 70 /t PCEM Pellet C for energy market 60 /t Q 1,2,3 and 4 Heat generated for Zone 1,2,3 and 4 - P 1,2,3 and 4 Power generated for Zone 1,2,3 and 4-91

111 Chapter 5 P-graph Approach Table 1.2 Candidate operating units specification Symbo l Description Input Capital Cost Performance A CC ( ) B CC ( /MW) T Transport Ptr CO 2 : 2.69 kg CO 2 /L PP Pellet Plant A, C Pellet: 0.98 t/t CO 2 : 0.15kg CO 2 /t JEP Juice Extraction plant B Juice:0.69 t/t Bagasse: 0.31 t/t CO 2 :0.08 kg CO 2 /t FP Fermentation Juice ETN: 0.07 t/t SFP Plant Saccharification -Fermentation Plant G Gasifier A, PA, Ba, C, PC B Boiler A, PA, Ba, C, PC FCGT Fuel Cell with Gas Turbine CO 2 : 0.06 kg CO 2 /t Ba ETN: 0.13 t/t CO 2 : 0.12 kg CO 2 /t SG: 0.70 MJ/MJ CO 2 :0.007kg CO 2 /MJ Stm: 0.80 MJ/MJ CO 2 : 0.058kg CO 2 /MJ SG, BG P: 0.7 MJ/MJ Q: 0.2 MJ/MJ CO 2 : 0.055kg CO 2 /MJ C, Ba BG: 0.58 MJ/MJ CO 2 :0.007kg CO 2 /MJ A, C, PA, Q: 0.7 MJ/MJ PC CO 2 :0.060kg CO 2 /MJ AD Anaerobic Digester HC Home used Biomass Convertor (Boiler, Stove, etc) INC_H Incinerator and D Steam: 0.7 MJ/MJ RSG Heat Recovery CO 2 :0.055kg CO 2 /MJ Steam Generator LF Landfill D BG: 0.5 MJ/MJ CO 2 :0.028kg CO 2 /MJ ST Steam Turbine Stm P: 0.24 MJ/MJ Q: 0.56 MJ/MJ CO 2 :0.041kg CO 2 /MJ 92

112 Chapter 5 P-graph Approach The bagasse after the juice extraction from the sorghum (originating from Zone 2) is sent to anaerobic digestion in Zone 3, which produces biogas to be used as the fuel for a combined fuel cell and gas turbine unit. All of the grass feedstock (biomass C) collected in Zone 3 is pelletised. Grass pellets ( t/y) are sent to Zone 4 as the fuel for its pellets boiler and the rest are used as direct burning fuel for the house bioenergy conversion units in Zone 4. The waste-based resource, 940 t/y of MSW fraction (biomass D) is sent to a landfill in Zone 4 to produce biogas. These intermediate energy carriers (pellets, steam, biogas and syngas) are converted to heat and power for meeting the energy demands described in the beginning of this section. For example, steam turbine and combined fuel cell and gas turbine in Zone 3 produce 7.87 TJ/y of heat and 3.93 TJ/y of power to fulfil the local demands. The surplus of biomass is converted into ethanol (60.59 t/y) and wood pellet ( t/y), which can be transported to another cluster or traded in the market. The total annualised cost for the system is /y (lifespan: 15 y) which is equivalent to /MJ. Assuming the retail price for district heating is 0.03 /MJ, the cost for power generation is /MJ. This compares favourably with the recent average electricity wholesale price. The recent wholesale price include: USA, 2009: /MJ (EIA, 2010) and EU, 2008: /MJ (EUROSTAT, 2008) The demonstration case study results show that the supply chain using biomass can achieve reasonable production cost levels, remaining within the profit margins for retail prices for most of Europe. CO 2 emission for the energy generation is kg CO 2 /MJ, which is lower than the CO 2 emission for coal-fired, oil-fired and LNG-fired power generation 0.27, 0.21 and 0.17 kg CO 2 /MJ (Hiroki, 2005) 93

113 Figure 5.3 Optimum solution for minimum production cost with P-graph representation (Lam et al., 2010b) Chapter 5 P-graph Approach 94

114 Chapter 5 P-graph Approach 5.4 Chapter Summary A new methodology for the synthesis of regional-scope biomass energy supply chain networks has been formulated. It consists of two levels: clustering (presented in Chapter 4) and detailed synthesis using P-graph. It has been tested and the results confirmed the applicability at regional scale. The applied two-level strategy has been proven to successfully manage the complexity of the biomass energy supply network problem, by simultaneously simplifying the corresponding infrastructure links and their eventual design and implementation tasks. In this regard, applying the clustering technique at the upper design level plays a significant role. 95

115 Chapter 6 SUMMARY OF ACCOMPLISHMENTS 6.1 Original Contributions Theses Based on the novel approaches and scientific contributions presented and illustrated by comprehensive case studies in the previous chapters, the following theses, representing three basic discoveries are summarised: 1. A two-level hierarchical methodology for regional resources and biomass supply chains synthesis has been developed. This comprehensive research strategy is divided into two levels: supply-demand targeting and detailed supply chain synthesis. The first level including steps for REC, targeting the use of regional biomass resources and fossil fuels, and RMC provides the strategy for the regional resource management, which mainly involve the landuse and energy generation issues. In the second level, detailed supply chain synthesis inside each cluster step has defined a new application of the P-graph process optimisation framework (Friedler et al., 1992). Based on this methodology, several novel approaches and tools have been developed. The detail elaboration is for each important contribution and discoveries are given in the following points, included their applications, achievements and related publications. 2. A novel approach - Regional Energy Clustering (REC) algorithms has been developed This contribution has been presented in Chapter 3. A cluster is defined as a set of zones within a region related through energy transfer links using local infrastructures. The benefits from applying the proposed clustering algorithm are: 96

116 Chapter 6 Summary of Accomplishments i ii Partitioning of the solution space and problem decomposition. This allows breaking down the initial complex problem into several problems of smaller size and complexity, greatly facilitating the modelling and solution efforts. Reducing the scope and size of the problems to consider allows adding more details and precision to the process models, thus increasing the confidence in the resulting supply chain networks. Forming clusters reveals the sets of zones, between which the biomass transfer is most beneficial. The current criterion used for clustering is to minimise the Carbon Footprint (CFP). Since at this stage only biomass exchange is considered, CFP minimisation tends to also minimise the costs. A mathematical programming model has been developed to estimate the optimal biomass exchange flows among the zones. The energy surpluses and deficits from various zones are further matched and combined to form energy supply chain clusters. The algorithms for REC and their application have been presented and published in several international conferences (Lam et al., 2008a; 2008b; 2008c) and the journals Resources, Conservation & Recycling (Lam et al., 2010a) and Computers and Chemical Engineering (Lam et al., 2010b). The REC algorithm has also been extended for waste-to-energy network synthesis under the collaboration within 3 universities namely (i) University of Pannonia, Hungary, (ii) University of Nottingham Malaysia Campus, Malaysia and (iii) University Technology Malaysia, Malaysia. The outcome resulted in (Lam et al., 2010d). 97

117 Chapter 6 Summary of Accomplishments 3. Several new graphical tools for regional energy supply chain and resources management have been created and demonstrated. Two new graphical visualisation tools have been developed and their usefulness demonstrated in this thesis. These are (i) The Regional Energy Supply-Demand Curves (RESDC) presented in Chapter 3, and (ii) The Regional Resources Management Composite Curve (RMC) presented in Chapter 4. The RESDC visualise the formation of the energy clusters identified by the REC algorithm. The RESDC are a pair of cumulative curves which represent cumulative energy supply and demand profiles. It is able to indicate the size of each cluster and the total energy involved in the supply chain within the cluster. The applications of RESDC are presented in several international conferences with good response from the participants. The RMC is a Process Integration tool similar to the Grand Composite Curve from Heat Integration, which shows the energy imbalances in the considered region and its clusters, helping in the management of resources by exploiting the regional trading-offs between using renewable and fossil energy sources. This graphical tool provides straightforward information of how to manage the surplus resources (biomass and land use) in a region. The details of the RESDC construction and their application are published in journals such as Resources, Conservation & Recycling (Lam et al., 2010a) and Computers and Chemical Engineering (Lam et al., 2010b). RMC has attracted a lot of attention after being presented at several international conferences (Lam et al., 2009a; 2009b; 2010e). The presentation of RMC in the 6 th Dubrovnik Conference on Sustainable Development of Energy, Waster and 98

118 Chapter 6 Summary of Accomplishments Environment System, SDEWES, 2009, was voted as the best presentation in the session, invited for and later published in a conference special issue of the journal Applied Energy (Lam et al., 2010c). 4. New application of P-graph in Biomass supply chain synthesis has been established The supply chain synthesis has been carried out by P-graph (Friedler et al., 1992) based optimisation within each cluster. It provides a more detailed analysis. The use of the P-graph framework as a synthesis toolset provides a strong mathematically proven fundament for handling the complexity of the synthesis problem. It contributes to the optimal network design with a high computational efficiency. This approach contributes to the cleaner generation of energy from biomass, approaching CO 2 neutrality as much as possible. It is beneficial for extending the use of biomass as a renewable source of energy. This work was presented in 19 th European Symposium on Computer Aided Process Engineering, ESCAPE 19, 2009, Krakow (Lam et al., 2009c) and being invited to be published in the conference special issue under the journal of Computers and Chemical Engineering (Lam et al., 2010b). The results of this work also presented in other conferences such as Lam et al., (2009d; 2010f) 99

119 Chapter 6 Summary of Accomplishments Tézisek Az újszerű megközelítéseket és tudományos hozzájárulásokat az előző fejezetek átfogó esettanulmányainak bemutatása és illusztrálása alapján, a következő tézist, három alap felfedezés bemutatásával lehet összefoglalni: 1. Kétszintű hierarchikus módszer kifejlesztése a regionális források és a biomassza ellátási lánc szintézisével Ez az átfogó kutatási stratégia két szintre oszlik: a kereslet-kínálat célzott keresésére és a részletes ellátási lánc szintézisére. Az első szint, beleértve a REC lépéseit, a regionális biomassza források és fosszilis üzemanyagok használatának célzott keresése. Az RMC biztosítja a regionális erőforrás-gazdálkodás stratégiáját, amely elsősorban a földterület használatának és az energia termelésének problémáit foglalja magába. A második szint, a részletes ellátási lánc szintézise, valamennyi klaszterlépésen belül egy meghatározott új P-gráf keret alkalmazása a folyamatoptimalizálásban (Friedler et al 1992). E módszer alapján számos új megközelítés és eszköz lett kifelesztve. Minden fontos hozzájárulás és felfedezés részletes értekezése, a következő pontokban van kifejtve, beleértve az alkalmazásukat, eredményeiket és kapcsolódó kiadványaikat. 2. Egy új megközelítés - a Regionális Energia Klaszterezési (REC) algoritmusok kifejlesztése E munkában a klaszter definíciója, olyan meghatározott övezetek a régión belül, amelyeket az energia átadás köti össze a helyi infrasturktúrák használatával. A javasolt klaszterezési algoritmus előnyei a következőek: i. A megoldást igénylő terület felosztása és a probléma lebontása kisebb szakaszokra. Ez lehetővé teszi a kezdeti probléma lebontását több 100

120 Chapter 6 Summary of Accomplishments ii. problémára, amelyeknek a mérete és összetettsége kisebb, ami nagyban megykönnyíti a modellezést és a megoldást. A nagyobb megfontolást igénylő probléma terjedelmének és méretének csökkentésével lehetővé teszi a további részletek és pontosság hozzáadását, így növelve a ellátási hálózatok megbizhatóságát. A klaszterek megformálása feltárja azon zónák készletét, amelyek között a legelőnyösebb a biomassza átadás. A jelenlegi kritérium a klaszterek formálására a ökológiai lábnyomat (Carbon footprint-cfp) minimalizása. Mivel ebben a szakaszban csak a biomassza-cserét tekintjük, a CFP minimalizása általában a minimálisra csökkenti a költségeket is. A matematikai programozási modell a becsült optimális zónák közötti biomasszacsere folyamatokra lett kifejlesztve. Az energia többletek és hiányok a különböző sávokban párosítva és kombinálva vannak az energia ellátási lánc klasztereinek kialakítása érdekében. A REC algoritmusok és azok alkalmazása számos nemzetközi konferencián (Lam et al., 2008a; 2008b; 2008c) és folyóiratban Resources, Conservation & Recycling (Lam et al., 2010a) és Computers and Chemical Engineering (Lam et al., 2010b) került bemutatásra. A REC algoritmus továbbá ki lett terjesztve a hulladék energia hálózatának szintézisrére az alábbi három egyetem együttmüködésével: (i) Pannon Egyetem, Magyarország, (ii) University of Nottingham Malaysia Campus, Malajzia és (iii) University Techology Malaysia, Malaysia. Az eredmény megtekinthető (Lam et al., 2010d). 101

121 Chapter 6 Summary of Accomplishments 3. Számos új grafikai eszköz a regionális energia ellátási lánc és erőforrásgazdálkodás bemutatására lett kifejlesztve és bemutatva Két új grafikai vizuális eszköz került kidolgozásra és azok hasznosággát bizonyítja ez a disszertáció. Ezek (i) a Regional Energy Supply-Demand Curves (RESDC), és(ii) a Regional Resources Management Composite Curve (RMC). A RESDC vizualizálja a kialakított energia klasztereket, amelyet az REC algoritmus használatával jöttek létre. A RESDC egy pár kumulatív görbéből áll, ami megjeleníti a kumulatív energia ellátás és kereslet profilokat. Lehetővé teszi minden egyes klaszter méretének és a teljes energiájának a megjelenítését, mely részt vesz az ellátási láncban egy klaszteren belül. Nemzetközi konferenciákon való bemutatásakor a résztvevők jól reagáltak a RESDC alkalmazására. Az RMC a Folyamat Integrációban használatos eszköz, hasonló mint a Grand Composite Curve a Hőintegrációban, amely megjeleníti az energia egyensúly hiányát a vizsgált régiókban és klaszterekben, elősegítve a források menedzsmentjét a regionális újrahasznosítható és fosszilis energia források közötti kompromisszumokkal. Ez a grafikai eszköz egyértelmű tájékoztatást ad arról, hogy miként kell kezelni a többlet forrásokat (biomassza és terület/ földhasználat) egy régióban. A részletek az RESDC alkotására és alkalmazására vonatkozóan az alábbi folyóíratban tekinthetőek meg: Resources, Conservation & Recycling (Lam et al., 2010a) és a Computers and Chemical Engineering (Lam et al., 2010b). Ez a módszer nagy figyelmet váltott ki számos nemzetközi konferenciákon való bemutatásakor (Lam et al., 2009a; 2009b; 2010e). Az RMC-ról szóló előadást a 6 th Dubrovnik Conference on Sustainable Development of Energy, Waster and 102

122 Chapter 6 Summary of Accomplishments Environment System, SDEWES, 2009, megszavazták a legjobb előadásra a kategóriájában, meghivást kapott és később megjelent a konferencia külön kiadványában az Applied Energy (Lam et al., 2010c) folyóiratban. 4. A P-graph új alkalmazása a Biomassza ellátási lánc szintézésén bemutatva Az ellátási lánc szintézise P-graph (Friedler et al., 1992) alapján volt kivitelezve, minden egyes klaszteren belül. Ez részletesebb elemzést eredményezett. A P- graph keret használata mint szintézis eszközkészlet erős, matematikailag bizonyított alapot bíztosít a szintézis probléma összetettségének kezelésére. Ez hozzájárul a nagy számítási hatékonysághoz az optimális hálózat kialakításánál. A megközelítés hozzájárul a biomasszából előállított energia tisztább kinyeréséhez, valamint a CO 2 minimalizálásához. Ez előnyös a biomassza, mint megújuló energiaforrásnak a kiterjesztésére. Ez a munkar a 19. European Symposium on Computer Aided Process Engineering, ESCAPE 19, 2009, Krakow (Lam et al., 2009c) lett bemutatva és felkérést kapott a konferencia külön kiadványának a megjelenésére a Journal of Computers and Chemical Engineering (Lam et al., 2010b) folyóiratban. 103

123 Chapter 6 Summary of Accomplishments 6.2 List of Publications The publications in book chapters, international journals and peer reviewed international conference papers that related to this PhD works are listed below: Book Chapters: 1. Klemeš J J, Varbanov P, Lam H L. Water Footprint, water recycling and Food Industry Supply Chain. Chapter. In: K Waldron (ed) Waste management and coproduct recovery in food processing Vol 2, Woodhead Publishing Limited, Cambridge, 2009, ISBN: Klemeš J., Lam H. L., Foo D. C. Y., Water Recycling and Recovery in Food and Drink Processing. Chapter in: K Waldron, G.K. Moates and C.B. Faulds (eds), Total Food: Sustainability of the Agri-Food Chain, RSC Publishing, UK, 2010, ISBN: International Journals with Impact Factor 1. Lam H. L., Varbanov P., Klemeš J., 2010, Regional Renewable Energy and Resource Planning, Applied Energy, 88, [IF = 2.209] 2. Lam H. L., Varbanov P., Klemeš J., 2010, Optimisation of regional energy supply chains including renewables: P-graph approach, Computers and Chemical Engineering, 34, [IF: ]. The14 th Most downloaded paper (June 2010) 3. Klemeš J. and Lam L. H., 2010, Heat Integration, Energy Efficiency, Saving and Security. Energy, 34 (10), [IF: 2.952] 104

124 Chapter 6 Summary of Accomplishments 4. Lam H. L., Varbanov P., Klemeš J., 2010, Minimising Carbon Footprint of Regional Biomass Supply Chains, Resources, Conservation & Recycling, 54(5), [IF = 1.987] International Conference Papers / Presentation: 1. Lam H. L., Foo D. C. Y., Mustafar K. and Klemeš J., Synthesis of Regional Energy Supply Chain Based on Palm Oil Biomass, Chemical Engineering Transactions, 2010, 21, Lam H. L., Klemeš J. J., Varbanov S. P., Regional Renewable Energy and Resource Business Management Tool, 7 th International Conference on Computation Management Science CMS 2010, 28 th - 30 th July 2010, Vienna Austria, session FA2, p Klemeš J, Lam H. L., Varbanov P., Friedler F., Biomass Energy Generation, Carbon Footprint Minimisation and Supply Chains Synthesis, 37 th International Conference of SSCHE, Tatranské Matliare, Slovakia, May 24-28, p Lam H. L., Varbanov P., Klemeš J., Műszaki Kémiai Napok Mükki Conference of Chemical Engineering, Veszprém, Hungary, April 27-29, p Lam H. L., Varbanov P., Klemeš J., Regional Renewable Energy and Resource Planning. Special Session: Integrating Waste and Renewable Energy to reduce the Carbon Footprint Locally Integrated Energy Sectors, SEDEWES 09, Dubrovnik, p (Best Presenter Award) 105

125 Chapter 6 Summary of Accomplishments 6. Klemeš J., Lam H.L., Foo D. C. Y., Water Minimisation, Recycling and Recover- Implementation to the Process Industry. Proceedings of Slovenian Chemical Days, September 24, 2009, Maribor, Slovenia, p. 7 (Plenary lecture) 7. Lam H. L., Varbanov P., Klemeš J., Friedler F., Regional Biomass Energy Supply Chain Management Strategy: P-Graph Approach. 8 th World Congress of Chemical Engineering, Incorporating the 59 th Canadian Chemical Engineering Conference and the 24 th Inter-American Congress of Chemical Engineering, August 23-27, 2009, Montreal, Quebec, Canada, pp. 525b 8. Lam H. L., Varbanov P., Klemeš J., Optimisation of regional energy supply chains utilising renewables: P-graph approach, ESCAPE 19, June, Krakow, Poland, Vol (26) Lam H. L., Varbanov P., Klemeš J., Regional Resource Management Composite Curve, Chemical Engineering Transactions, vol. 18 (2009) Klemeš J., Lam H. L., Foo D. C. Y., Water Recycling and Recovery, Total Food 2009, Norwich, UK. Plenary Lecture 11. Lam H.L., Varbanov P., Klemeš J., An efficient planning and implementation of regional renewable energy supply chain. PRES 2008, Prague. PRES 2008/CHISA 2008 Proceeding (2008) Lam H.L., Klemeš J., Varbanov P., CO 2 Emissions Reduction via a graphical Analysis Method for Renewable Energy Supply Chains. IFORS 2008, July 13-18, 2008, Sandton, South Africa. 13. Lam H.L., Varbanov P., Klemeš J., Development of a Graphical Analysis Method for Renewable Energy Supply Chain. ENERGY FOR SUSTAINABLE FUTURE, 106

126 Chapter 6 Summary of Accomplishments ed P Varbanov, J Klemeš and I Bulatov, UoP University Library Archives, UoP Press 2008/50, Veszprém, Hungary, ISBN Lam H.L., Varbanov P., Klemeš J, Recent Development and Novel Graphical Methods for CAPE, CAPE, (2008), Thessaloniki, Greece, Session

127 References Aguilar F.X., Grala R.K., Bratkovich S.M., 2009, Use of georeferenced data to study clustering in the primary wood products industry of the US South, Canadian Journal of Forest Research, 39 (1), Ambarish A. M., Eksioglu S., Petrolia D., 2008, In-Bound supply chain design for biomass-to-ethanol industry: A study of Mississippi, IIE Annual Conference and Expo, Ayoub N., Martins R., Wang K., Seki H., Naka Y., 2007, Two levels decision system for efficient planning and implementation of bioenergy production, Energy Conversion and Management, 48 (3), Banerjee, I., Ierapetritou, M.G., Parametric process synthesis for general nonlinear models. Computers & Chemical Engineering, 27, Beckeman, M., Skjoldebrand, C., Cluster/networks promote food innovations. Journal of Food Engineering, 79: Berndes G., Hoogwijk M., van den Broek R., 2003, The contribution of biomass in the future global energy supply: a review of 17 studies, Biomass and Bioenergy, 25 (1), 1-28 Biofeul Watch, 2010, Biomass: Pro and Cons, < last accessed Casavant T. E. and Côté R. P., 2004, Using chemical process simulation to design industrial ecosystems. Journal of Cleaner Production 12,

128 Celma A.R., Rojas S., López-Rodríguez F., 2007, Waste-to-energy possibilities for industrial olive and grape by-products in Extremadura, Biomass and Bioenergy, 31(7), Chen C.H., Liu W. L., Liaw S. L., Yu C. H., 2005, Development of a dynamic strategy planning theory and system for sustainable river basin land use management, Science of the Total Environment, 346 (1-3), Chen C.-L and Ciou Y.-J., 2007, Synthesis of a Continuously Operated Mass- Exchanger Network for a Semiconsecutive Process. Industry Engineering Chemical Research, 46, Chew I, M. L., Ng D. K. S., Foo D. C. Y., Tan R. R., Majozi T., Gouws J., 2008, Synthesis of Direct and Indirect Inter-Plant Water Network. Industrial and Engineering Chemistry Research, 47, Dantzig, G.B., Orden, A., Wolfe, P., 1954, The generalized simplex method for minimizing a linear form under linear inequality restraints. Rand Research Memorandum RM-1264, RAND Corporation, Santa Monica, CA, USA De Benedetto L. and Klemeš J., 2009, The Environmental Performance Strategy Map: an Integrated LCA approach to support the Strategic Decision Making Process, Journal of Cleaner Production, 17 (10), De Benedetto L. and Klemeš J., 2010, The Environmental Bill of Materials and Technology Routing: an integrated LCA approach. Clean Technology and environmental Policy, 2010, 6 (12), Demirbas M. F., Balat M., Balat H., 2009, Potential contribution of biomass to the sustainable energy development, Energy Conversion and Management, 50(7),

129 Drobež R., Novak Pintarič Z., Pahor B., Kravanja Z., 2009, MINLP synthesis of processes for the production of biogas from organic and animal waste. Chem. Biochem. Eng. Q. 23(4) ECOHEATCOOL & Euroheat & Power, 2006, Work Package 1: The European Heat Market Final report. < %20WP1% 20Web. pdf> last accessed Edgar, T.F., and Himmelblau, D.M. 2001, Optimization of chemical processes, McGraw Hill, New York, 2 nd Edition EIA, Energy Information Administration, 2009, Average retail price of electricity to ultimate customers by end-use sector, by state. < /epm/table5_6_b.html> Last accessed El-Halwagi M. M., 2006, Process Integration. Elsevier, San Diego European Communities, Official Journal, 2000, Council Directive 2000/76/EC of the European parliament and of the council of 4 December 2000 on the incineration of waste, Official Journal of the European Communities L332, EUROSTAT, Electricity price for first semester 2008, <epp.eurostat.ec.europa.eu/ cache/ity_offpub/ks-qa /en/ks-qa en.pdf> last accessed Faaij A. P. C., 2006, Bio-energy in Europe: changing technology choices, Energy Policy 34,

130 Freppaz D., Minciardi R., Robba M., Rovatti M., Sacile R., Taramasso A., 2004, Optimizing forest biomass exploitation for energy supply at a regional level, Biomass and Bioenergy, 26 (1), Friedler F., Process integration, modelling and optimisation for energy saving and pollution reduction pollution reduction, Chemical Engineering Transactions, 18, Friedler F., Process integration, modelling and optimisation for energy saving and pollution reduction, Applied Thermal Engineering, 30, Friedrichs J., 2010, Global energy crunch: How different parts of the world would react to a peak oil scenario, Energy Policy38(2010) Friedler F., Tarjan K., Huang Y.W., Fan L.T., 1992, Graph-Theoretical Approach to Process Synthesis: Axioms and Theorems. Chem. Eng. Sci., 47, Friedler F., Varga J.B., Fan L.T., 1995, Decision-Mapping: A Tool for Consistent and Complete Decisions in Process Synthesis. Chem. Eng. Sci., 50, Friedler F., Varga J.B., Fehér E., Fan L.T., 1996, Combinatorially Accelerated Branchand-Bound Method for Solving the MIP Model of Process Network Synthesis. In State of the Art in Global Optimization, Ed. Floudas, C.A. and Pardalos, P.M., Kluwer Academic Publishers, Boston, Mass, GAMS, GAMS Home Page.< last accessed 20/11/2010 Gani R., 2008, Computer-Aided Methods and Tools for Chemical Product Design, Chemical Engineering Research and Design, 82(A11) Gregg J. S., 2010, National and regional generation of municipal residue biomass and the future potential for waste-to-energy implementation, Biomass and Bioenergy, 34 (3),

131 Grossmann I. E. and Daichendt M. M., 1996, New trends in optimisation passed approaches to process synthesis, Computers Chemical Engineering, 20 (6/7) Grossmann, I.E. 1996, Editor, Global Optimization in Engineering Design, Kluwer Academic Publisher, Dordrecht, The Netherlands Grossmann, I.E., 1990, MINLP Optimization Strategies and Algorithms for Process Synthesis. FOCAPD, CACHE, Elsevier, Amsterdam, pp Grossmann, I.E., and Biegler, L.T. 2004, Part II. Future Perspective on Optimization, Computer and Chemical Engineering., 28, Hall D.O., Scrase J.I., 1998, Will biomass be the environmentally friendly fuel of the future?, Biomass and Bioenergy, 15, Hiroki H, Life cycle GHG emission analysis of power generation systems: Japanese case, Energy, 30, Huston M. A. and Marland G., 2003, Carbon management and biodiversity. Journal of environmental Management, 67, Iakovou E., Karagiannidis A., Vlachos D., Toka A., Malamakis A., 2010, Waste biomass-to-energy supply chain management: A critical synthesis, Waste Management, In Press, Corrected Proof, Available online 15 March 2010 IEA, 2005, Emission Facts: Average Carbon Dioxide Emissions Resulting from Gasoline and Diesel Fuel. < last accessed

132 Jaarsma, C., F., van Dijk, T., Financing local rural road maintenance. Who should pay, what share and why?, Transportation Research, Part 1, 36: Jeżowski, J., 1990, Linear programming based method of heat exchanger network synthesis. Industrial & Engineering Chemistry Process Design, 10, Jeżowski, J., Shethna, H.K., Castillo, F.J.L., 2003, Area target for heat exchanger network using linear programming. Industrial & Engineering Chemistry Research, 42(8), Kajikawa Y and Takeda Y, 2008, Structure of research on biomass and bio-fuels: A citation-based approach, Technological Foresting and Social Chance, 75 (9), Karp A., Shield I., 2008, Bioenergy from plants and the sustainable yield challenge. New Phytologist, 179 (1), Klemeš J., Friedler F., Bulatov I., Varbanov P., 2010, Sustainability in the Process Industry: Integration and Optimization. McGraw-Hill Professional, USA. ISBN-10: Klemeš, J., Vašek, V., 1973, Methods for optimising complex chemical processes. In Proceedings of the 2nd Symposium on the Use of Computers in Chemical Engineering (Czechoslovak Chemical Society - Working group for Computers, Ústí nad Labem, Czech Republic), pp City Publisher. Koh L.P. and Ghazoul J., 2008, Biofuel, biodiversity, and people: Understanding the conflict and finding opportunities, Biological conservation, 141,

133 Kovacs, Z., Ercsey, Z., Friedler, F., Fan, L.T., 2000, Separation-network synthesis: Global optimum through rigorous super-structure. Computers & Chemical Engineering, 24, Krotscheck C., König F. and Obernberger I., 2000, Ecological assessment of integrated bioenergy systems using the Sustainable Process Index, Biomass and Bioenergy Krotscheck C., König F., Obernberger I., 2000, Ecological assessment of integrated bioenergy systems using the Sustainable Process Index, Biomass and Bioenergy, 18, Lam, H., L., Varbanov, P., Klemeš J., 2008a, Development of a Graphical Analysis Method for Renewable Energy Supply Chain. ENERGY FOR SUISTANABLE FUTURE, ed P Varbanov, J Klemeš and I Bulatov, UoP University Library Archives, UoP Press 2008/50, Veszprém, Hungary, ISBN pp Lam H.L., Klemeš J., Varbanov P., 2008b, CO 2 Emissions Reduction via a graphical Analysis Method for Renewable Energy Supply Chains. Invited Oral Lecture at IFORS 2008, July 13-18, 2008, Sandton, South Africa. Lam H.L., Varbanov P., Klemeš J., 2008c, An efficient planning and implementation of regional renewable energy supply chain. PRES 2008, Prague. PRES 2008/CHISA 2008 Proceeding (2008) Lam H. L, Varbanov P.., Klemeš J., 2009a, Regional Resource Management Composite Curve, Chemical Engineering Transactions, vol. 18 (2009) Lam H. L., Varbanov P., Klemeš J., 2009b, Regional Renewable Energy and Resource Planning. Special Session: Integrating Waste and Renewable Energy to reduce the Carbon Footprint Locally Integrated Energy Sectors, SEDEWES 09, Dubrovnik, pp

134 Lam H. L., Varbanov P., Klemeš J., Friedler F., 2009c, Regional Biomass Energy Supply Chain Management Strategy: P-Graph Approach. 8th World Congress of Chemical Engineering: Incorporating the 59th Canadian Chemical Engineering Conference and the 24th Interamerican Congress of Chemical Engineering, August 23-27, 2009, Montreal, Quebec, Canada, pp. 525b Lam H. L., Varbanov P., Klemeš J., 2009d, Optimisation of regional energy supply chains utilising renewables: P-graph approach, ESCAPE 19, June, Krakow, Poland, Computer Aided Chemical Engineering, Volume 26, 2009, Pages Lam H. L., Varbanov P., Klemeš J., 2010a, Minimising Carbon Footprint of Regional Biomass Supply Chains, Resources, Conservation & Recycling, 54(5), Lam H. L., Varbanov P., Klemeš J., 2010b, Optimisation of regional energy supply chains including renewables: P-graph approach, Computers and Chemical Engineering, 34, Lam H. L., Varbanov P., Klemeš J., 2010c, Regional Renewable Energy and Resource Planning, Applied Energy, 88, Lam H. L., Foo D. C. Y., Mustafar K. and Klemeš J., 2010d, Synthesis of Regional Energy Supply Chain Based on Palm Oil Biomass, Chemical Engineering Transactions, 21, Lam H. L., Klemeš J. J., Varbanov S. P., 2010e, Regional Renewable Energy and Resource Business Management Tool, 7th International Conference on Computation Management Science CMS 2010, 28th - 30th July 2010, Vienna Austria, Session FA2, p

135 Lam H. L., Klemeš J, Varbanov P., Friedler F., 2010f, Biomass Energy Generation, Carbon Footprint Minimisation and Supply Chains Synthesis, 37th International Conference of SSCHE, Tatranské Matliare, Slovakia, May 24-28, p. 248 Land, A.H. and Doig, A.G., 1960, An automatic method of solving discrete programming problems. Econometrica, 28(3), Maniatis K and Millich E., 1998, Energy from biomass and waste: The contribution of utility scale biomass gasification plants, Biomass and Bioenergy, 15 (3), Maros, I., 2003a. A piecewise linear dual phase-1 algorithm for the simplex method. Computational Optimization and Applications, 26(1), Maros, I., 2003b. Computational techniques of the simplex method. Boston: Kluwer. Nagy A.B., Adonyi R., Halasz L., Friedler F., Fan L.T., Integrated Synthesis of Process and Heat Exchanger Networks: Algorithmic Approach. Applied Thermal Engineering, 21, Narodoslawsky M., 2010, Structural prospects and challenges for Bio Commodity Processes, Foot Technology and Biotechnology, 48 (3), Narodoslawsky M., Krotscheck C., 2004, What can we learn from ecological valuation of processes with the sustainable process index (SPI) the case study of energy production systems, Journal of Cleaner Production Narodoslawsky M., Krotscheck C., 1995, The sustainable process index (SPI): evaluating processes according to environmental compatibility, Journal of hazardous Materials,

136 National Petroleum Council, 2007, Facing the Hard Truths About Energy, < last accessed Olsson, S., Karrman, E., Gustafsson, J., P., Environmental systems, analysis of the use of bottom ash from incineration of municipal waste for road construction. Resources, Conservation and Recycling, 48: Parliamentary Office for Science and Technology (POST). Carbon footprint of electricity generation, < last accessed People s Republic of China, 2007, National implementation plan for the Stockholm convention on persistent organic pollutants, < /implementation /nips/submissions/china_nip_en.pdf>, last accessed PNS Editor, 2010, < last accessed Porter, M., E., Cluster and the New Economics of Competition. Harvard Business Review: Ravindran, A., Ragsdell K.M., and Reklaitis G.V., 2006, Engineering Optimization: Methods and Applications (2 nd Ed), John Wiley, New York. Rosen M. A., Indicators for the environmental impact of waste emissions: Comparison of exergy and other indicator, Trans. Can. Soc. Mech Eng, 33 (1), Sandholzer D. and Narodoslawsky M., 2007, SPIonExcel Fast and easy calculation of the Sustainable Process Index via computer, Resources, Conservation and Recycling

137 Sandholzer D., Niederl A., Braunegg G. and CheVeNa N.M., 2005, A new approach for implementing renewable resources in industries, Abstracts of papers, 229th ACS national meeting. Schumacker, S., 2008, Facing the Hard Truths about Energy: < about_ energy>, last accessed Seidler, I., Badach, A., Molisz, W., 1980, Methods of solving of optimization problems. Warsaw: Wydawnictwa Naukowo Techniczne. Shahab S., Anthony T., Erin W., 2008, Integrated biomass supply and logistics, Resource: Engineering and Technology for Sustainable World, 15 (6), Sieniutycz, S., Jeżowski, J., 2009, Energy optimization in process systems. Amsterdam: Elsevier. Silalertruksa T., Gheewala S. H., Sagisaka M., 2009, Impacts of Thai bio-ethanol policy target on land use and greenhouse gas emissions, Applied Energy, 86, Srinivasa, M., Moon, Y., B., A comprehensive clustering algorithm for strategic analysis of supply chain network. Computers and Industrial engineering, 36: Stehlík P., 2007a, Heat exchangers as equipment and integrated items in waste and biomass processing, Heat Transfer Engineering, 28 (5), Stehlík P., 2007b, Waste and biomass utilisation focused on environment protection and energy generation, Chemical Engineering Transaction, 12, Stehlík, P., Smejkal, Q., Štulíř, R., 2008, New unit for clean energy production from contaminated biomass, Chemical Engineering and Technology 31 (5),

138 Taal, M., Bulatov, I., Klemeš, J., Stehlík, P., 2003, Cost Estimation and Energy Price Forecast for Economic Evaluation of Retrofit Projects, Applied Thermal Engineering, 23, Tokos H. and Novak Pintarič Z., 2009, Synthesis of batch water network for a brewery plant. Journal of Cleaner Production, 17, United States Environmental Protection Agency, 2010, Laws and regulations, < last accessed Varbanov P., Friedler F., P-graph Methodology for Cost-Effective Reduction of Carbon Emissions Involving Fuel Cell Combined Cycles. Applied Thermal Engineering, 28(16), Varbanov, P.S., Klemeš, J.J., 2010, Total sites integrating renewables with extended heat transfer and recovery, Heat Transfer Engineering, 31 ( 9), Vollebergh H., 1997, Environmental externalities and social optimality in biomass markets: waste-to-energy in The Netherlands and biofuels in France, Energy Policy, 25, ( 6), WEA, 2004, World Energy Assessment: energy and the challenge of sustainability (2004 updated version). UNDP, UN-DESA and the World Energy Council United Nations Development Programme, New York. Williams C. L., Hargrove W. W., Liebman M., James D. E., 2008, Agroecoregionalization of Iowa using multivariate geographical clustering, Agriculture, Ecosystems & Environment, 123 (1-3),

139 Williams H P., 2005, Model Building in Mathematical Programming, John Wiley & son Ltd., England. Yamamoto H., Yamaji K., Fujino J., 2000, Scenario analysis of bioenergy resources and CO2 emissions with a global land use and energy model, Applied Energy, 66, Kumar C. R., 2008, Research Methodology, APH Publishing Corporation, New Delhi. Townsend D. W., Linnhoff B., 1983, Heat and power networks in process design. Part II: Design procedure for equipment selection and process matching, AIChE Journal, 29 (5), Gwehenberger G. and Narodoslawsky M., 2008, Sustainable processes The challenge of the 21 st century for chemical engineering. Process Safety and Environmental Protection, 86(5), Čuček L., Lam H.L, Klemeš J., Varbanov P., Kravanja Z, 2010, Synthesis of regional networks for the production and supply of bioenergy and food, Clean Technologies and Environmental Policy, doi: /s Mandal K. G., Saha K. P., Ghosh P. K., Hati K. M., Bandyopadhyay K. K., 2002, Bioenergy and economic analysis of soybean-based crop production systems in central India, Biomass and Bioenergy, 23 (5), Prasertsan S. and Prasertsan P., 1996, Biomass residues from palm oil mills in Thailand: An overview on quantity and potential usage, Biomass and Bioenergy, 11 (5), Achten W.M.J., Verchot L., Franken Y.J., Mathijs E., Singh V.P., Aerts R., Muys B.,2008, Jatropha bio-diesel production and use, Biomass and Bioenergy, 32(12), Chinnasamy S., Bhatnagar A., Claxton R., Das K.C., 2010, Biomass and bioenergy production potential of microalgae consortium in open and closed bioreactors using 120

140 untreated carpet industry effluent as growth medium, Bioresource Technology,101(17), Schlamadinger B. and Marland G., 1996, The role of forest and bioenergy strategies in the global carbon cycle, Biomass and Bioenergy, 10 (5-6), Joshi O. and Mehmood S. R., 2010, Factors affecting nonindustrial private forest landowners' willingness to supply woody biomass for bioenergy, Biomass and Bioenergy, doi: /j.biombioe Turkenburg, W.C., Faaij, A., 2000, Renewable Energy Technologies. Chapter 7 of the World Energy Assessment of the United Nations, UNDP, UNDESA/WEC. UNDP, New York Veringa H.J, 2010, Advanced techniques for generation of energy from biomass and waste, < last accessed Kirubakaran V., Sivaramakrishnan V., Nalini R., Sekar T., Premalatha M., Subramanian P., 2009, A review on gasification of biomass, Renewable and Sustainable Energy Reviews, 13, Mojović L., Nikolić S., Rakin M., Vukasinović M., 2006, Production of bioethanol from corn meal hydrolyzates, Fuel, 85 (12 13), Nguyen M. H. and Prince R. G. H., A simple rule for bioenergy conversion plant size optimisation: Bioethanol from sugar cane and sweet sorghum, Biomass and Bioenergy, 10 (5-6), De Baere L. and Matteeuws B., 2008, State-of-the-art 2008 Anaerobic digestion of solid waste, < articles/waste-management-world/volume-9/issue-4/features/state-of-the-art anaerobic-digestion-of-solid-waste.html>, last accessed

141 Uslu A, Faaij A.P.C., Bergmann P.C.A., 2008, Pre-treatment technologies, and their effect on international bioenergy supply chain logistics: Techno-economic evaluation of torrefaction, fast pyrolysis and pelletisation. Energy, 33(8), Sayigh, A., 1999, Renewable energy: the way forward. Applied Energy, 64, Perry, S., Klemeš, J., Bulatov, I., 2008, Integrating Waste and Renewable Energy to reduce the Carbon Footprint of Locally Integrated Energy Sectors. Energy, 33, Anderson, G., Q., A., and Fergusson M. J., 2006, Energy from biomass in the UK: Sources, processes and biodiversity implications. Ibis, 148, Forsberg, G., 2000, Biomass energy transport. Analysis of bioenergy transport chains using life cycle inventory method. Biomass and Bioenergy, 19, Junginger, M., Faaij, A., Van den Brock, R., Koopmans, A., Hulscher, W., 2001, Fuel Supply Strategies for Large-Scale Bio-Energy Projects in Developing Countries. Electricity Generation from Agricultural and Forest Residues in Northeastern Thailand, Biomass and Bioenergy, 21, Raven R.P.J.M. and Gregersen, H.H., 2007, Biogas Plants in Denmark: Successes and Setbacks, Renewable and Sustainable Energy Reviews, 11, Dunnett A., Adjiman C., Shah N., 2007, Biomass to heat supply chains applications of process optimisation, Process Safety and Environmental Protection, 85 (5), Rentizelas A. A., Tolis A. J., Tatsiopoulos I. P., 2009, Logistics issues of biomass: The storage problem and the multi-biomass supply chain, Renewable and Sustainable Energy Reviews 13,

142 Becerra-Lopez H. R. and Golding P., 2007, Dynamic energy analysis for capacity expansion of regional power-generation systems: case study of far west Texas, Energy, 32, Dantzig, G.B., Orden, A., Wolfe, P., 1954, The generalized simplex method for minimizing a linear form under linear inequality restraints. Rand Research Memorandum RM-1264, RAND Corporation, Santa Monica, CA, USA Linnhoff B., Townsend D.W., Boland D., Hewitt G.F., Thomas B.E.A., Guy A.R., Marslamd R.H., 1982, Auser guide on process integration for the efficient use of energy. Rugby, UK: IChemE (Revised edition published in 1994) Alves J., 1999, Analysis and design of refinery hydrogen distribution systems. PhD thesis, UMIST, Manchester, U.K Singhvi A. and shenoy U.V., 2002, Aggregate planning in supply chains by pinch analysis, Chemical engineering Research and Design, 80(6), Singhvi A., 2002, Multi-level planning of supply chains under uncertainty. MTech dissertation, Indian Institute of Technology, Bombay Singhvi A., Madhavan K.P., Shenoy U.V., 2004, Pinch analysis for aggregate production planning in supply chains, Computers and Chemical Engineering, 28(6-7), Linnhoff B. and Dhole V.R., 1993, Targeting for CO 2 emission for total sites. Chemical Engineering and Technology, 16 (4), Tan R., and Foo D.C.Y., 2007, Pinch analysis approach to carbon-constrained energy sector planning, Energy, 32 (8), Atkins M. J., Morrison A. S., Walmsley M. R. W., 2010, Carbon Emissions Pinch Analysis (CEPA) for emissions reduction in New Zealand electricity sector, Applied Energy, 87 (3),

143 Crilly, D. and Zhelev T., 2008, Emissions Targeting and Planning An Application of CO2 Emissions Pinch Analysis to the Irish Electricity Generation Sector, Energy, 33 (10),