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A STRATEGIC SUPPLY CHAIN MANAGEMENT MODEL FOR WASTE BIOMASS NETWORKS D. Vlachos1, E. Iakovou1, A. Karagiannidis2, A. Toka1 1. Laboratory of Quantitative Analysis, Logistics and Supply Chain Management, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Greece. 2. Laboratory of Heat Transfer and Environmental Engineering, Department of Mechanical Engineering, Aristotle University of Thessaloniki, 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 work, we present a quantitative-based approach that takes 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 network for the Region of Central Macedonia, Greece. KEYWORDS:

1.

Biomass Logistics, 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 /1, 2/. 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 CO2neutral 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 3rd International Conference on Manufacturing Engineering (ICMEN), 1-3 October 2008, Chalkidiki, Greece Edited by Prof. K.-D. Bouzakis, Director of the Laboratory for Machine Tools and Manufacturing Engineering (ΕΕΔΜ), Aristoteles University of Thessaloniki 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

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for the design and evaluation of sustainable waste biomass supply chains, taking into account the collection, storage, and transport operations for supplying energy production units. Specifically, in Section 2, 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 framework on a simplified yet realistic, biomass supply network 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.

2. 2.1

WASTE BIOMASS SUPPLY 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 markets 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. Taking 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 1.100 EJ, exceeding the current global energy use of 410 EJ /4/. Berndes et al. discuss the contribution of biomass in the future global energy supply based on a review of 17 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/. 2.2

Generic System Description

Biomass supply chain networks 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 1). The development of biomass supply chains for energy production appears to display the complexity of the design of the most known 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 works of Hamelinck et al. /9, 10/ 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

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Information flow to the ‘upper’ supply chain levels (upstream)

Trasportation

Biomass Production

Storage

Storage

Pre-treatment

Energy Production

Transportation

Pre-treatment Logistics

Information flow to the ‘lower’ levels of the supply chain (downstream)

Figure 1: Graphical Representation of a Biomass Supply Chain. import) and the energy plant. Caputo et al. /11/ investigate the economic profitability of biomass utilization for the direct production of electric energy taking 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. McCormick et al. /12/ study the key 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 network building and supply chain coordination.

3. A STRATEGIC SUPPLY CHAIN OPTIMIZATION MODEL 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 (CO2 emissions) point of view, whereas only a part of the reviewed literature body addresses the relevant and highly critical supply chain management issues /13/. Assessing waste biomass supply chains for bio-energy production involves a complex hierarchy of decision-making processes under uncertainty. For the optimal design, planning and coordination of these supply chain networks, decisions have to be made according to the natural hierarchy of the decision-making 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-makers need to identify the nodes of the supply chain network (such as collection and storage points) and the flows of biomass among the various modes of the network. In this section, we present the development of a generic strategic mixed integer linear programming model, for supporting this strategic decision-making process by identifying the optimal location of the chain’s nodes along with the associated network flows.

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Echelon 1

Echelon 2

1

Echelon L

1

1

x 12 21 2

… …

. . . .

2



nl

n2

2



nL

Collection Node

Final Conversion Node

Figure 2: Biomass Supply Chain of the Generalized Model. Specifically, we consider a supply chain of a specific biomass type that includes L echelons. Each echelon k (k=1,…,L) includes nk nodes (Figure 2). 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 k (k=1,…,L-1) to any node of the downstream echelons. We employ as optimization criterion the minimization of total system cost: L

nk

L −1

nk nm

L

∑∑ C y + ∑ ∑ ∑ ∑ C k =1 i=1

k i

k i

m=1 k =m+1 j=1 i=1

mk mk ij x ij

+

L

n1

nk

∑∑∑ C x k =2 i=1 j=1

i

1k ij

(1),

where the decision variables are defined as follows: 1 when node i is created in echelon k ,

y ki :

otherwise.

x mk ij :

biomass product quantity that is transferred from node i of echelon m to node j of echelon k.

The problem’s parameters are defined as follows:

Cki : fixed cost (€) of creating node i at echelon k, k=1,…,L and i=1,…,nk Cmk transportation cost (€/biomass unit) from node i of echelon m to node j of echelon k, ij : i=1,…,nm, j=1,…,nk, m=1,…,L-1, k=2,…,L

Ci : biomass purchase cost (€/biomass unit) in node i of the first echelon, i=1,…,n1

Z j : demand of node j of the last echelon (units of biomass), j=1,…,nL K kj :

capacity of node j of echelon k (units of biomass), k=1,…,L, j=1,…,nk.

The constraints of the mathematical model are then: Demand Constraints: L −1 nL nm

∑∑ ∑ x m=1 j=1 i=1

800

mL ij

=

nL

∑Z j=1

j

, ∀j = 1,..., nL

(2)

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Capacity Constraints: k −1 nm

∑∑ x m =1 i=1

mk ij

≤ K kj y kj , ∀j = 1,..., nk , ∀k = 1,..., L

(3)

Flow Constraints: k −1 nm

∑∑ m =1 i=1

x mk = ij

L

nm

∑ ∑x

m =k +1 i=1

km ji

, ∀j = 1,..., nk , ∀k = 2,..., L - 1

(4)

Logical Constraints: k x mk ij ≥ 0 and y i = {0,1} .

(5)

The mathematical model (P) which is defined as: min (1), subject to: (2), (3), (4) and (5), is a L −1

mixed integer linear programming model which consists of L

∑n

k

binary variables, while the number of the constraints is

k =1

L

∑ ∑n n

k m

k =1 m=k +1 L L −1 k

primal variables and L −1

L

∑n + ∑n + ∑ ∑n n k =1

k =2

k

k m

+ 1.

k =1 m=k +1

Thus, indicatively, model (P) for a particular supply chain realization that is comprised of 30 collection points, 4 pre-treatment stations, 2 storage nodes and 1 final destination point, has 224 primal variables, 37 binary variables and 268 constraints and can be solved easily using any readily available optimization software package.

4.

NUMERICAL EXPERIMENTATION IN A REAL-WORLD 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 Thessaloniki, Imathia, Kilkis, Pieria, Serres, Pella and Chalkidiki). It covers an area of 19.147 km2 (14.51% 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 Thessaloniki, Imathia, Kilkis, Pieria, Serres, Pella and Chalkidiki), 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 Thessaloniki as a final destination point. We consider a wood industry placed in the Prefecture of Thessaloniki 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 8.141 kW and assuming a working period of 350 days per year on a 24h basis, it is estimated that the energy demand of the plant will be 68.384.400 kWh per year. Wheat straw has a Higher Heating Value (HHV) of 17.1 MJ/kg, which corresponds to 4.750 kWh/kg; thus, it can be estimated that the annual demand reaches a quantity of Z1 =14.395.564 kg (or 14.395 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. Taking into consideration the alternative uses of wheat straw, and mainly its utilization as animal

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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 1. Supply costs for each collection point were also recorded, ranging from 60 € to 100 € per tone of biomass and are presented in the same Table. Table 1: Capacity (tn/year) and Supply Costs (€/tn) per Biomass Collection Point. Node

1

2

3

4

5

6

7

Prefecture

Pieria

Imathia

Pella

Chalkidiki

Thessaloniki

Serres

Kilkis

Capacity K 1j

1828

610

1017

2906

9433

4402

6072

80

90

85

76

60

70

63

(tn/year) Supply Cost Ci (€/tn)

Additionally, the following costs were estimated: • Capacity of Warehouse 1 K 12 = 9.000 tn of wheat straw • Capacity of Warehouse 1 K 22 = 9.000 tn of wheat straw • Capacity of Plant’s Warehouse K 13 = 15.000 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 trucks 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 2. Table 2: Transportation Costs (€/tn). Node i

1 Pieria

2 Imathia

3 Pella

4 Chalkidiki

5 Thessaloniki

6 Serres

7 Kilkis

Warehouse 1

65

58

62

72

37

74

70

Warehouse 2

69

68

69

67

53

70

59

Plant

65

65

64

70

37

67

65

Node j

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Additionally, transportation costs between Warehouse 1 and the plant’s facility and between Warehouse 2 and the plant’s facility were estimated as 61 €/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 Cki 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 25.000 €. Solving the minimization problem of total cost using the optimal objective function (1) value was calculated as 671.618 € per year. The optimal solution is:

y11 = 0 , y12 = 0 , y13 = 0 , y14 = 0 , y15 = 1, y16 = 0 , y17 = 1, y12 = 0 , y 22 = 0 , y13 = 1 , x 12 11 = 0 , 12 12 12 12 12 12 12 12 12 x12 12 = 0 , x 21 = 0 , x 22 = 0 , x 31 = 0 , x 32 = 0 , x 41 = 0 , x 42 = 0 , x 51 = 0 , x 52 = 0 , x 61 = 0 ,

12 12 13 13 13 13 13 13 x12 62 = 0 , x 71 = 0 , x 72 = 0 , x 11 = 0 , x 21 = 0 , x 31 = 0 , x 41 = 0 , x 51 = 9.433 , x 61 = 0 , 23 23 x13 71 = 6.072 , x 11 = 0 , x 21 = 0 .

The above values of the optimal solution indicate that 9.433 tones of wheat straw should be procured from Prefecture of Thessaloniki and 6.072 tones should be procured from the Prefecture of Kilkis to cover the plant’s total demand of 14.395 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 package used for solving our mathematical programming model was that of AMPL (CPLEX). 5.

CONCLUSIONS

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 work 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-making process are clearly myopic if are made without taking 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 seeking myopically the optimality at each level of the hierarchy. Logistics 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-making modelling framework, will offer in its initial stage new directions for the design and execution of efficient biomass supply chain networks 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 networks. 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. Acknowledgement: The authors greatly appreciate the contribution of Mr. Apostolos Malamakis, PhD Candidate of the Laboratory of Heat Transfer and Environmental Engineering (of the Department of Mechanical Engineering, Aristotle University of Thessaloniki, Greece) who provided up-to-date estimates for the case study presented in Section 4.

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7. REFERENCES 1. Goldemberg J. World Energy Assessment. United Nations Development Programme, New York, USA, (2000), p.508. 2. Klass DL. Biomass for Renewable Energy and Fuels, Encyclopedia of Energy 2004:1; p.193. 3. OECD/FAO Agricultural Outlook 2007 – 2016, (2007), http:/www.oecd.org April 2008. 4. Hoogwijk M, Faaij APC, van den Broek R, Berndes G, Gielen D, Turkenburg WC., Exploration of the ranges of the global potential of biomass for energy, Biomass and Bioenergy 25 (2003)199-133. 5. Berndes G, Hoogwijk M, van den Broek R., The contribution of biomass in the future global energy supply - a review of 17 studies, Biomass and Bioenergy 25 (2003) 1-28. 6. Ericsson K, Nilsson LJ., Assessment of the potential biomass supply in Europe using a resource - focused approach, Biomass and Bioenergy 30 (2006) 1–15. 7. van Dam J, Faaij APC, Lewandowski I, Fischer G., Biomass production potentials in Central and Eastern Europe under different scenarios, Biomass and Bioenergy 31 (2007) 345–366. 8. Gielen DJ, de Feber MAPC, Bos, AJM, Gerlagh T., Biomass for energy or materials? A Western European systems engineering perspective, Energy Policy 29 (2001) 291-302. 9. Hamelinck C.N., Suurs R.A.A., Faaij, A.P.C. International bioenergy transport costs and energy balance, Biomass and Bioenergy 29(2) (2005)114-134. 10. Hamelinck C.N., Faaij A.P.C., den Uil H., Boerrigter H., System analysis of biomass derived FT liquids; Technical options, process optimisation and development potential, Utrecht University, Copernicus Institute, Science Technology and Society, Utrecht, (2003). 11. Caputo A.C., Palumbo M., Pelagagge P.M., Scacchia F., Economics of biomass energy utilization in combustion and gasification plants: Effects of logistic variables, Biomass and Bioenergy, 28 (1) (2005) 35-51. 12. McCormick K, Kaberger T., Key barriers for bioenergy in Europe: Economic conditions, know-how and institutional capacity, and supply chain co-ordination, Biomass and Bioenergy, 31(7) (2007), 443-452. 13. Iakovou E., Karagiannidis A., Vlachos D., Toka A. and Malamakis A., Waste Biomass Supply Chain Networks for Energy Production: A Conceptual Decision-Making Modeling Framework, Working Paper, (2008).

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