A STRATEGIC SUPPLY CHAIN MANAGEMENT MODEL FOR WASTE BIOMASS NETWORKS

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1 A STRATEGIC SUPPY CHAIN MANAGEMENT MODE FOR WASTE BIOMASS NETWORKS D. Vlachos, E. Iaovou, A. Karagiannidis, A. Toa. aboratory of Quantitative Analysis, ogistics and Supply Chain Management, Department of Mechanical Engineering, Aristotle University of Thessalonii, Greece.. aboratory of Heat Transfer and Environmental Engineering, Department of Mechanical Engineering, Aristotle University of Thessalonii, Greece. ABSTRACT The development of renewable energy sources appears as a meaningful response for enhancing the fragile global energy system with its limited fossil fuel resources, as well as for addressing various environmental problems. More specifically, biomass utilization emerges as a viable alternative for energy production. The rising demand for biomass and the increasing complexity of the involved supply systems outline the need for comprehensive biomass supply chain management methodologies. In this wor, we present a quantitative-based approach that taes into account all major aspects in the design of waste biomass supply chains developed for energy production. To that effect, we first present a generalized biomass supply chain optimization model for the strategic allocation of its nodes and its related flows. We conclude by illustrating the application of the proposed methodology on a test case study for a biomass supply networ for the Region of Central Macedonia, Greece. KEYWORDS: Biomass ogistics, Biomass Supply Chain Management, Bio-energy, Waste Management, Energy Production.. INTRODUCTION The continuous growth of global energy consumption raises an important challenge as the larger portion of mineral oil reserves resides within a small number of countries, thus forming a fragile energy supply that is expected to reach its limit within the foreseeable future. Additionally, the usage of fossil fuels causes numerous environmental problems, such as atmospheric pollution, acidification and the emission of greenhouse gases /, /. The development of cleaner and renewable energy sources appears as a meaningful intervention for addressing these problems. More specifically, biomass (including vegetation and trees, energy crops, as well as biosolids, animal, forestry and agricultural residues, the organic fraction of municipal wastes and certain types of industrial wastes) emerges as a promising option, mainly due to its potential worldwide availability, its conversion efficiency and its ability to be produced and consumed on a CO - neutral basis. Biomass is a versatile energy source, generating not only electricity but also heat, while it can be further used to produce biofuels. The requirements with respect to biomass supply in terms of quality and quantity can differ considerably, depending on the energy production technology, the size of the conversion plants, the end use of the power generated and, at the same time, on the cost-efficiency and feasibility of its logistics operations. Biomass supply chain management bears the challenge to develop solutions adapted to local and inter-regional conditions and constraints, such as the existing infrastructure, geographical allocation of collection areas or competition among several consumers. In this paper, we propose a new quantitative-based modelling approach that could be employed Proceedings of the 3 rd International Conference on Manufacturing Engineering (ICMEN), -3 October 008, Chalidii, Greece Edited by Prof. K.-D. Bouzais, Director of the aboratory for Machine Tools and Manufacturing Engineering (ΕΕΔΜ), Aristoteles University of Thessalonii and of the Fraunhofer Project Center Coatings in Manufacturing (PCCM), a joint initiative by Fraunhofer-Gesellschaft and Centre for Research and Technology Hellas, Published by: ΕΕΔΜ and PCCM 797

2 for the design and evaluation of sustainable waste biomass supply chains, taing into account the collection, storage, and transport operations for supplying energy production units. Specifically, in Section, the potential contribution of biomass in the future global energy supply is discussed, while generic system components are presented. In the following Section, we present a generalized biomass supply chain optimization model for the strategic allocation of its nodes and the related flows. In Section 4, we illustrate the application of the proposed framewor on a simplified yet realistic, biomass supply networ for a wood industry located within the Region of Central Macedonia of Greece. Finally, we sum up with conclusions and suggest promising areas for future research.. WASTE BIOMASS SUPPY CHAINS. Potential of Waste Biomass for Energy Production In order to understand the future role of energy from waste biomass on a global level, it is important to investigate the drivers for its utilization against competitive options for substrate resources, such as energy crops for energy production. Despite the attention that the production of biofuels from energy crops has attracted, a number of issues have emerged recently that question the feasibility of this practice. According to a report published by OECD and the United Nations Food and Agriculture Organization, the increased demand for biofuels is causing fundamental changes to agricultural marets that drive up world prices for many farm products /3/. On the other side, second-generation biofuels obtained by waste biomass are not plagued by the same negative attributes, while at the same time they could support effectively waste management policies. Taing all the above into consideration, maximizing value of waste biomass and organic substrates for energy production emerges as an ever increasing priority. Many past and recent research efforts document both the already existing and potential contribution of biomass in the future global energy supply. Theoretically, the total bio-energy contribution (combined of theoretical potential by agricultural, forest, animal residues and organic wastes) could be as high as.00 EJ, exceeding the current global energy use of 40 EJ /4/. Berndes et al. discuss the contribution of biomass in the future global energy supply based on a review of 7 earlier studies on the subject, including residue generation and recoverability /5/. Finally, addressing the issue at a European level, only a handful of papers focus on biomass availability /6, 7, 8/.. Generic System Description Biomass supply chain networs for energy production encompass five general system components: biomass collection (from single or several locations), pre-treatment (in one or more stages), storage (in one or more intermediate locations), transport (using one or multiple transportation means across a number of consequent echelons) and energy conversion (Figure ). The development of biomass supply chains for energy production appears to display the complexity of the design of the most nown supply chains for consumable products. Certain parameters can limit the effectiveness of biomass production systems including spatial/localized agricultural capacities and seasonality. Moreover, due to interdependencies between supply chain levels, there is a limited degree of freedom in choosing feasible alternatives. Thus, it is important to obtain insights about the effects of all these variables on total cost and energy consumption of supply chains; this would allow the identification of optimal configurations for bioenergy supply systems, as well as the identification of improvement options ( what-if analysis). To address some of these issues, producers have opted for developing global supply chains importing and transporting biomass over long distances. The research wors of Hamelinc et al. /9, 0/ constitute the first effort in studying systematically the influence of such parameters on the performance of complete transport chains, analyzing a scenario that assumes five possible transfer points: the production site, a central gathering point, two transport terminals (export and rd ICMEN 008

3 Information flow to the upper supply chain levels (upstream) Trasportation Storage Biomass Production Storage Transportation Energy Production Pre-treatment Pre-treatment ogistics Information flow to the lower levels of the supply chain (downstream) Figure : Graphical Representation of a Biomass Supply Chain. import) and the energy plant. Caputo et al. // investigate the economic profitability of biomass utilization for the direct production of electric energy taing into account the critical logistics aspects related to the overall bio-energy chain as well as the impact of the main logistics variables on the economics of such systems. McCormic et al. // study the ey barriers for bio-energy in Europe concluding that technological barriers are not the main challenge. The authors stipulate that the most important issue is to first understand the processes and then optimize the entire system operations. To this direction, they propose strategies including policy measures to alter the economics of bio-energy, pilot projects to stimulate the learning processes and guidance for networ building and supply chain coordination. 3. A STRATEGIC SUPPY CHAIN OPTIMIZATION MODE Biomass usage for bio-energy production is a rapidly evolving research field as indicated by the plethora of scientific journal and conference proceedings papers. The vast majority of the relevant studies examine the system from either a purely technological (pre-treatment and conversion technologies) or ecological (CO emissions) point of view, whereas only a part of the reviewed literature body addresses the relevant and highly critical supply chain management issues /3/. Assessing waste biomass supply chains for bio-energy production involves a complex hierarchy of decision-maing processes under uncertainty. For the optimal design, planning and coordination of these supply chain networs, decisions have to be made according to the natural hierarchy of the decision-maing process, namely at the strategic, tactical and operational levels. A major portion of the cost in biomass energy generation originates from its logistics operations. Thus, several attempts have been made to analyze, simulate and optimize biomass logistics and supply chains, but most of them focus on specific case studies and not generalized models. At the first level of the hierarchy, namely the strategic level, investors and decision-maers need to identify the nodes of the supply chain networ (such as collection and storage points) and the flows of biomass among the various modes of the networ. In this section, we present the development of a generic strategic mixed integer linear programming model, for supporting this strategic decision-maing process by identifying the optimal location of the chain s nodes along with the associated networ flows. ogistics in Manufacturing 799

4 Echelon Echelon Echelon n l x n.... n Collection Node Final Conversion Node Figure : Biomass Supply Chain of the Generalized Model. Specifically, we consider a supply chain of a specific biomass type that includes echelons. Each echelon (=,,) includes n nodes (Figure ). We assume that: (a) Transportation is not allowed among nodes at the same echelon, and (b) the product can be transported from each node of echelon (=,,-) to any node of the downstream echelons. We employ as optimization criterion the minimization of total system cost: m m + ij ij + i y i C x = i= m n n n n n m= = m+ j= i= = i= j= i ij C C x (), where the decision variables are defined as follows: y i : when node i is created in echelon, otherwise. m x ij : biomass product quantity that is transferred from node i of echelon m to node j of echelon. The problem s parameters are defined as follows: C i : fixed cost ( ) of creating node i at echelon, =,, and i=,,n m C ij : transportation cost ( /biomass unit) from node i of echelon m to node j of echelon, i=,,n m, j=,,n, m=,,-, =,, C i : biomass purchase cost ( /biomass unit) in node i of the first echelon, i=,,n Z j : demand of node j of the last echelon (units of biomass), j=,,n K j : capacity of node j of echelon (units of biomass), =,,, j=,,n. The constraints of the mathematical model are then: Demand Constraints: m n n n m ij = m= j= i= j= x Z, j j =,...,n () rd ICMEN 008

5 Capacity Constraints: m n m= i= x m ij K j y j Flow Constraints: m m n n m m ij = x ji m= i= m= + i=, j =,...,n, =,..., (3) x, j =,...,n, =,..., - (4) ogical Constraints: x m ij 0 and y i = { 0,}. (5) The mathematical model (P) which is defined as: min (), subject to: (), (3), (4) and (5), is a mixed integer linear programming model which consists of = n = m= + n m primal variables and + n binary variables, while the number of the constraints is n n + n n +. = = = m= + Thus, indicatively, model (P) for a particular supply chain realization that is comprised of 30 collection points, 4 pre-treatment stations, storage nodes and final destination point, has 4 primal variables, 37 binary variables and 68 constraints and can be solved easily using any readily available optimization software pacage. m 4. NUMERICA EXPERIMENTATION IN A REA-WORD CASE In this section, we provide a test case motivated by an industry located within the Region of Central Macedonia in order to illustrate the application of the proposed mathematical model. Although the case is simplified to be suitable for presentation in this paper, all quantitative estimates are accurate and reflect the current state of the system. The Region of Central Macedonia (Figure 3) is one of the thirteen administrative districts of Greece. It is situated in Central Northern Greece and is divided in seven prefectures (Prefecture of Thessalonii, Imathia, Kilis, Pieria, Serres, Pella and Chalidii). It covers an area of 9.47 m (4.5% of the country), thus being the largest (spatially) district in the country. We consider a supply chain of a specific biomass type that includes three echelons. The first echelon consists of seven (7) collection points (namely Prefectures of Thessalonii, Imathia, Kilis, Pieria, Serres, Pella and Chalidii), the second one includes two potential warehouse nodes that are located on specific road junctions and the third echelon is comprised of a single wood industry s facility placed in the Prefecture of Thessalonii as a final destination point. We consider a wood industry placed in the Prefecture of Thessalonii that produces woodbased particle boards and plans to substitute petroleum with fuel derived from biomass, and specially wheat straw, to cover its annual energy needs. The facility has an installed capacity of 8.4 W and assuming a woring period of 350 days per year on a 4h basis, it is estimated that the energy demand of the plant will be Wh per year. Wheat straw has a Higher Heating Value (HHV) of 7. MJ/g, which corresponds to Wh/g; thus, it can be estimated that the annual demand reaches a quantity of Z = g (or tn) of straw per year. An estimation of the available quantity of wheat straw at the seven Prefectures of RCM was conducted, using bibliographical sources and statistical data in conjunction with field research, in order to estimate the aggregate biomass potential of the selected area under investigation. Taing into consideration the alternative uses of wheat straw, and mainly its utilization as animal ogistics in Manufacturing 80

6 Figure 3: Map of the Region of Central Macedonia, Greece. food, the final technically exploitable quantity of straw substrates of each collection point was calculated for the seven Prefectures as depicted in Table. Supply costs for each collection point were also recorded, ranging from 60 to 00 per tone of biomass and are presented in the same Table. Table : Capacity (tn/year) and Supply Costs ( /tn) per Biomass Collection Point. Node Prefecture Pieria Imathia Pella Chalidii Thessalonii Serres Kilis Capacity K j (tn/year) Supply Cost C i ( /tn) Additionally, the following costs were estimated: Capacity of Warehouse K = tn of wheat straw Capacity of Warehouse K = tn of wheat straw 3 Capacity of Plant s Warehouse K = tn of wheat straw. As far as transportation costs are concerned, an empirical logarithmic regression model was used to capture the relationship between distance and transportation costs. The model was developed in our preliminary studies based on several price offers for specific routes for various Origin-Destination pairs. Transportation is considered to be conducted by trucs of a specific type of (40 foot) with a capacity of approximately 4 tones of wheat straw per vehicle. The estimated values for all possible routes were estimated as presented in Table. Table : Transportation Costs ( /tn). Node i Pieria Imathia 4 Chalidii Node j Pella Thessalonii Serres Kilis Warehouse Warehouse Plant rd ICMEN 008

7 Additionally, transportation costs between Warehouse and the plant s facility and between Warehouse and the plant s facility were estimated as 6 /tn and 59 /tn, respectively. Regarding fixed costs of setting up a node at any level, the following assumptions were made. As collection points already exist, supply cost values of wheat straw recorded for each Prefecture include all collection and loading costs (i.e. machinery and vehicle investment costs, labour costs, consumable expenses etc.), and thus, fixed costs C i for the nodes of the first echelon are set to zero. Moreover, as the plant utilizing biomass for energy production is considered to be pre-existent, thus there is no set up cost for this final node either. Finally, the fixed cost of creating the warehouses of the intermediary level was estimated at Solving the minimization problem of total cost using the optimal objective function () value was calculated as per year. The optimal solution is: y = 0, y = 0, y 3 = 0, y 4 = 0, y 5 =, y 6 = 0, y 7 =, y = 0, y = 0, y3 =, x = 0, x = 0, x = 0, x = 0, x 3 = 0, x 3 = 0, x 4 = 0, x 4 = 0, x 5 = 0, x 5 = 0, x 6 = 0, x 6 = 0, x 7 = 0, x 7 = 0, x3 = 0, x3 = 0, x3 3 = 0, x3 4 = 0, x3 5 = 9.433, x3 6 = 0, x 3 7 = 6.07, x 3 = 0, x 3 = 0. The above values of the optimal solution indicate that tones of wheat straw should be procured from Prefecture of Thessalonii and 6.07 tones should be procured from the Prefecture of Kilis to cover the plant s total demand of tones. According to the optimal solution, the total quantity should be transported directly to the plant s facility, and not warehoused at any node of the second level. The software pacage used for solving our mathematical programming model was that of AMP (CPEX). 5. CONCUSIONS The energy strategies of most developed countries have set specific strategic targets for the usage of renewable energy and bio-energy. However, the development and effective operation of bio-energy systems require the design and management of supply chains that meet the needs of all relevant actors. Our wor has demonstrated that current research focuses on specific, case-dependent biomass supply chain systems without providing more generalized strategic methodological approaches. Decisions at one level of the hierarchical decision-maing process are clearly myopic if are made without taing into account their impact on the other levels of the hierarchy. Thus, there is clearly value in evaluating different scenarios that could strive for system optimization rather than seeing myopically the optimality at each level of the hierarchy. ogistics and supply chain management have emerged as disciplines of critical importance for the energetic utilization of waste biomass and organic substrates. It is envisioned that the developed strategic decision-maing modelling framewor, will offer in its initial stage new directions for the design and execution of efficient biomass supply chain networs for energy production. By implementing the proposed mixed integer linear programming model on a realistic case of an industry based in the Region of Central Macedonia, we reveal the potential of the model on small-scale or large-scale optimization problems for the efficient design of biomass supply chain networs. Our future steps include extension of the provided model, applications on multi-level supply chains for different types of biomass, as well as investigation of relevant tactical and operational issues. Acnowledgement: The authors greatly appreciate the contribution of Mr. Apostolos Malamais, PhD Candidate of the aboratory of Heat Transfer and Environmental Engineering (of the Department of Mechanical Engineering, Aristotle University of Thessalonii, Greece) who provided up-to-date estimates for the case study presented in Section 4. ogistics in Manufacturing 803

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