Modelling biomass and biofuels supply chains

Transcription

1 21st European Symposium on Computer Aided Process Engineering ESCAPE 21 E.N. Pistikopoulos, M.C. Georgiadis and A.C. Kokossis (Editors) 2011 Elsevier B.V. All rights reserved. Modelling biomass and biofuels supply chains Christiana Papapostolou a, Emilia Kondili b, John K. Kaldellis c a,b Optimisation of Production Systems Lab., TEI of Piraeus, 250 P. Ralli and Thivon Av., Aegaleo 12244, Greece c Lab of Soft Energy Applications & Environmental Protection, TEI of Piraeus, P.O. Box 41046, Athens 12201, Greece Abstract Traditionally, biomass derived energy, due the dispersed character of its primary recourses and the easiness in their processing, has been mainly exploited at a local level by employing well-known practices. Today, various types of biomass may be converted through specific conversion routes to either heat, power or/and biofuels or to a combination of the above. The aim of the present work is the analysis of the biomass and biofuels supply chain and the development of a generic mathematical model for the optimal exploitation of the biomass for heat, power and/or biofuels production. Model implementation can support the decision making in planning and operational issues such as infrastructure investments, the quantities of raw materials to be cultivated, the quantities of biofuels and / or heat and power to be produced and/ or to be imported, the extent of the cultivated land for the feedstock production, with the main goal being the identification of the best available solution for the optimal design and operation of the biofuels and biomass supply chain, per case considered. Keywords: Biomass decentralized exploitation, biomass conversion processes, supply chain optimization 1. Introduction scope of the work One of the most important barriers in increased biomass utilization is the cost of the respective supply chain and the selection of the converted final energy form (heat, and/or power, and/or biofuels) according to the demand profile and the resources based allocation. The present work aims to the optimization of the biomass and biofuels supply chain through an integrated raw materials conversion route final output consideration, taking into account the economic and technical characteristics of the problem. The model may be used as a decision support system for the strategic planning of biomass exploitation and investment selection and, at a later stage, for the optimal operation of the supply chain when the basic strategic choices have been implemented. 2. Characteristics of biomass and conversion routes The biomass and more specifically the biomass derived energy (bioenergy) is the renewable energy made from any organic material from plants or animals. The primary production of biomass has been classified into five categories, namely energy crops, agricultural residues, forestry, aquatic biomass and wastes. This classification is dictated by the differentiation of the methods that each raw material may be processed with and the final outputs that derive from them. Generally, biomass conversion is undertaken by various major technologies, depending on the type of raw material and the desired output. The selection criterion of the raw material, the conversion route and,

2 1774 C. Papapostolou et al. accordingly, the final output is a combination of technical and economic considerations and limits, as well as the demand pattern. 3. Optimization of biomass and biofuels Supply Chains Supply Chain Management (SCM) has been a very widely applied approach for coordinating and controlling in an integrated manner all the stages that may seem independent in previous considerations, recognizing that any parameter affecting one specific point of the Supply Chain (SC), in fact affects its entire behaviour and performance. The applications of the mathematical modelling approaches to bioenergy SCs in the literature are typically focused on the development of process models and simulation tools to facilitate the assessment of SC performance. Many research works have been carried out (Huang et al., 2010, Hugo and Pistikopoulos 2003, Van Dyken et al., 2010, Zamboni et al., 2009) dealing with the optimization of the SC performance taking into account different scales of problems and parameters. Moving towards more complex systems, State-Task-Network (STN) approaches have also been used in order to minimize total system cost whilst ensuring satisfaction of the prescribed heat load (Dunnett et al., 2007). However, classical modelling of biomass SC, revealing the best possible energy mix, taking into consideration the type and quantities of available raw materials, the conversion technologies, as well as the demand side needs, is not extensively investigated. Acknowledging this gap and as a progress in our modelling experience on integrated biofuels SC (Papapostolou, 2008), an extended biomass SC will be analyzed following the same principles. In the following section, the model development for the integrated biomass SC consideration will be described with its potential benefits for the strategic and operational planning for biomass exploitation not only for biofuels but also to heat and/or power generation. 4. Model development of biomass and biofuels SC 4.1. Model characteristics Biomass resources can be applied at varying scales, including utility-scale electricity generation, biofuel production and distributed power generation. The mathematical optimization model developed in the present work aims to the decision support of the biomass exploitation for heat, power and/or biofuels production. The exploitation of the available biomass quantities in a region for the production of heat, power or biofuels is a strategic choice that will be determined according to various parameters, such as the biomass type and availability and, therefore, the corresponding production conversion routes as well as outputs, the need of local power generation and specific demand patterns in the area for heat, power, biofuels and the infrastructure in the area under consideration, for example the existence of a district heating network, of a biofuels distribution network. In general, the structure of the model is as follows: Maximisation of the Total Biomass Supply Chain Value, i.e. Max (Total Value) = max [Total Heat Output*Heat Value + Total Power Output*Power Value + Total Biofuels Output*Biofuels Value)], where the Value of each of the system outputs may include the expected profit or, equivalently, the price minus the corresponding cost. The constraints of the system include the following: Total Quantity of raw material converted to Heat= Sum (Quantity of Raw Material allowed to be converted to Heat) Total Quantity of raw material converted to Power = Sum (Quantity of Raw Material allowed to be converted to Power).

3 Modelling biomass and biofuels supply chains 1775 Total Quantity of raw material converted to Biofuels= Sum (Quantity of Raw Material allowed to be converted to Biofuels). Total Heat Output = (Total Quantity of raw material converted to Heat) * (biomass to heat conversion factor). Total Power Output = (Total Quantity of raw material converted to Power) * (biomass to power conversion factor). Total Biofuels Output = (Total Quantity of raw material converted to biofuels)* (biomass to biofuels conversion factor). Total Quantity of Raw material available (for each type) < = Corresponding Raw material maximum availability. For the raw materials that are energy crops, the following constraint also applies: Quantity of each raw materials being cultivated = Land availability*land yield 4.2. Detailed Model description i: Raw materials j: End product, i.e. Heat, Power, Biofuels, Combined Heat Power (1: output to Heat, 2: output to Power, 3: output to Biofuels, 4: output to Combined Heat and Power (CHP) t: time horizon of the model For a specific time Horizon t (e.g. one year) and for each time period t: I 1, I 2, I 3 : Sets of raw materials allowed to be converted to Heat, Power, Biofuels. A it : e it : cf i1 : cf i2 : cf i3 : p 1 : p 2 : p 3 : c i1 : c i2 : c i3 : Land available for the cultivation of raw material i in time period t (in 1000m 2 ) Yield of land for raw material i (in time period t) (in kg/1000m 2 ) Conversion factor of raw material i to Heat (in kwh th /kg) Conversion factor of raw material i to Power (in kwh el /kg) Conversion factor of raw material i to Biofuels (in m 3 /kg) Value of Heat produced (in /kwh th ) Value of Power produced (in /kwh el ) Value of Biofuel (bioethanol or biodiesel or biogas) (in / m 3 ) Cost of Heat produced (in /kwh th ) Cost of Power produced (in /kwh el ) Cost of Biofuel (bioethanol or biodiesel or biogas) (in / m 3 ) Q it1max, Q it2max, Q it3max : total maximum available quantity of raw material i allowed to be directed to Heat, Power, Biofuels respectively. Model variables Q it1, Q it2, Q it3 : Quantity of raw material i being transformed to heat, and/or power, and/or biofuels, respectively in time period t H it : Heat produced by raw material i in time period t (kwh th ) P it : Power produced by raw material i in time period t (kwh el ) B it : Biofuel produced by raw material i in time period t (m 3 ) Model optimization criterion Max Total Value = ) (1) Subject to: H it = Q it1 * cf i1 for each raw material allowed to produced Heat, i to I1 (2) P it = Q it2 * cf i2 for each raw material allowed to generate Power, i to I2(3) B it = Q it3 * cf i3 for each raw material allowed to be converted to Biofuels, i to I3(4) Q it1 Q it1max, i to I1 (5) Q it2 Q it2max, i to I2(6) Q it3 Q it3max, i to I3 (7) Q it A it *e it (8)

4 1776 C. Papapostolou et al. 5. Case study: biomass optimization in the area of Thessaloniki, Greece The model that has been developed is implemented in an exemplar case study, in Thessaloniki, Greece. Thessaloniki has a permanent population of approx. 1,000,000 people in a total geographical extent of 3,700,000km 2. Concerning the biomass availability, the area presents a rather high, mean annual yield of an extended feedstock variety suitable for the production of heat, power and biofuels. Model implementation will attempt to meet for a hypothetical, future elaborated biomass network, in an optimal way the typical, urban demand profile at local level, with specific heating, electrical and transportation consumption needs (Table 1), by converting the existing or perspective raw materials to bio-heat, bio-power and biofuels. For the needs of the case study, a moderate serving population percentage (of the city) is considered, 10% i.e. 100,000caps. Demand profile basic assumptions are illustrated in Table 1. For biomass optimization, five in total raw materials are considered: two for the production of heat and power and three for the production of bioethanol. For each of the above feedstock the selected values for model - testing are shown in Table 2. It should be underlined that not all the biomass initially produced is driven to bio-conversion: a reduction factor of 0.7 is assigned so as to incorporate possible various losses. Table 1. Selected values on the demand side for model optimization Basic assumptions made for demand profile estimation Transportation Consumption: TC= m 3 /week x 52weeks/y= 1.3 m 3 /y/family. With a 5% bioethanol substitution rate: TC= m 3 /y/family bioethanol needs or TC= 2,275 m 3 /y for 100,000caps. Electricity Consumption: EC= 175,000MWh el /y for 100,000caps- Heating Consumption: EC=350,000MWh th /y for 100,000caps In Table 2 the basic values assignment (Boukis et al., 2009) is illustrated. Table 2. Parameters assigned values for a horizon of 1 year (Boukis et al., 2009) Values assigned for model optimization For a time period t= 1y Set of raw materials to be converted to heat and power Fiber Cynara -(dry) sorghum cardunculus Set of raw materials to be converted to bioethanol Sweet Sugar Corn sorghum Beet Cultivated land for raw material i (1000m 2 ) A it 40,000 50,000 1,000 5,000 10,000 Yield of land for raw material i (kg/1000m 2 ) e it 5,500 1,250 7,000 6,700 1,172 Conversion factor of raw material i to Heat (kwh th /kg) cf i Power (kwh el /kg) cf i Bioethanol (m 3 /kg) cf i x10-3 x10-3 x10-3 Value of Heat produced ( /kwh th ) p Value of Power produced ( /kwh el ) p Value of Bioethanol produced ( /m 3 ) p Cost of Heat produced ( /kwh th ) c i Cost of Power produced ( /kwh el ) c i Cost of Bioethanol produced ( /m 3 ) c i The optimization results are illustrated in Table 3. Over a set of available feedstock raw materials, the model selects (in the case of heat and power production) to exploit fully the most cost effective one, like fiber sorghum, and then use cynara, in order to meet the power needs of the consumer. The same takes place with sweet sorghum and sugar beet accordingly for bioethanol. Corn is not exploited mainly due to its high cost. The total resulting profit of the integrated biomass SC exploitation refers to the total income minus the total costs of the considered SCs, but it does not take into account the

5 Modelling biomass and biofuels supply chains 1777 Specific Investment Cost that would refer to the development of the associated infrastructure, possibly necessary in the case of bio-heat and bio-power exploitation. Table 3. Optimization model results Raw material Land avail Heat prod. H it Power Bioethanol Total profit (1000m 2 ) (kwh th ) P it (kwh el ) prod.b it (m 3 ) ( ) Fiber sorghum 40, ,000, ,167,442 16,689,859 Cynara -(dry) cardunculus 48,027 58,832,558 Sweet sorghum 1, Sugar Beet 4,671 1, In a further economic analysis, for a different time horizon, a more concise costparameters estimation / forecasting, along to a detailed SC network consideration should be made in order to be able to accommodate future trends, new production technologies and more accurate economic results. Furthermore the decision of creating new infrastructures might be regarded as an extension of the problem under contemplation. In that case, decision-making capability should be incorporated in the model through the definition of binary variables expressing whether a certain investment, should be made or not. 6. Conclusions The success of biomass based energy sector depends critically on efficient, costeffective and sustainable biomass exploitation. An integrated system level analysis is necessary to coordinate various production related tasks depending on the availability and the type of the feedstock. Such an analysis should incorporate not only planning level but also operational level aspects. The present optimization model attempts to provide a rational solution to various issues, such as the selection of raw materials for the production of each of the heat, power and biofuels output, the selection of the final output for each type of raw material, the decision on the feasibility of potential investments in the field. Further development of the model will also accommodate the possibility for conversion route selection. The system can provide valuable solutions for the strategic and/or operational planning of the biomass supply chain. References I. Boukis, N. Vassilakos, G. Kontopoulos, S. Karellas, 2009, Policy plan for the use of biomass and biofuels in Greece: Part II: Logistics and economic investigation, Renewable and Sustainable Energy Reviews, Vol.13(4), pp A. Dunnett, C. Adjiman, N. Shah, 2007, Biomass to Heat Supply Chains: Applications of Process Optimization, Process Safety and Environmental Protection, Vol.85(5), pp Y. Huang, C.-W. Chen, Y. Fan, 2010, Multistage optimization of the supply chains of biofuels, Transportation Research Part E: Logistics and Transportation Review, Vol.46(6), pp A. Hugo, E.N. Pistikopoulos, 2003, Environmentally conscious and design of supply chain networks, Computer Aided Chemical Engineering, Vol.15(Part 1), pp Ch. Papapostolou, E. Kondili (supervisor), 2008, Modelling and optimization of biofuels supply chain. Case study: Biofuels in Greece, MSc in Energy Dissertation, TEI of Piraeus Greece. S. Van Dyken, B.H. Bakken, H.I. Skjelbred, 2010, Linear mixed-integer models for biomass supply chains with transport, storage and processing, Energy, Vol.35(3), pp A. Zamboni, F. Bezzo, N. Shah, 2009, Supply Chain Optimization for Bioethanol Production System in Northern Italy: Environmentally Conscious Strategic Design, Computer Aided Chemical Engineering, Vol.27(C), pp

6 COMPUTER-AIDED CHEMICAL ENGINEERING, st EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING PART B Edited by E.N. Pistikopoulos Imperial College London, UK M.C. Georgiadis Aristotle University of Thessaloniki, Greece A.C. Kokossis National Technical University of Athens, Greece Amsterdam Boston Heidelberg London New York Oxford Paris San Diego San Francisco Singapore Sydney Tokyo