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Supply Chain Design and Management for Syngas Production Mohammad Marufuzzaman, Xiaopeng Li, Fei Yu, and Fang Zhou ACS Sustainable Chem. Eng., Just Accepted Manuscript • DOI: 10.1021/ acssuschemeng.5b00944 • Publication Date (Web): 30 Dec 2015 Downloaded from http://pubs.acs.org on January 11, 2016
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ACS Sustainable Chemistry & Engineering
Supply Chain Design and Management for Syngas Production 1 1
Department of Industrial and Systems Engineering, Mississippi State University, Starkville, MS 39762, United States 2
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Mohammad Marufuzzaman*, 2Xiaopeng Li, 3Fei Yu, 4Fang Zhou
Department of Civil and Environmental Engineering, University of South Florida, Tampa, FL 33620, United States
Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS 39762, United States 4
Department of Civil and Environmental Engineering, Mississippi State University, Starkville, MS 39762, United States
ABSTRACT The objective of this study is to develop an optimization model that aids the design and management of a logistics network for syngas production. The model identifies the optimal location of bio-gasification facilities and chipping terminals and the transportation cost of delivering wood chips from feedstock supply points to bio-gasification facilities so that the overall supply chain cost of producing syngas is minimized. We then extend the model to a case where both cost and carbon emissions are considered. We use the entire Southeast region of U.S. as the testing ground for this model and employ ArcGIS to visualize and validate the results from the optimization model. Numerical results show that the unit cost of syngas increases from $0.96/Nm3 to $1.04/Nm3 and then eventually to $1.18/Nm3 if the operating mode of the biogasification facilities decreases from three to two and then one shift. Finally, a number of sensitivity analysis are performed which will help the decision makers to design a cost effective syngas supply chain network. KEYWORDS: Syngas, bio-gasification, chipping terminals, woody biomass, logistics network
INTRODUCTION The potential of using biomass gasification technology is gaining traction day by day due to its ability to convert renewable resources to fossil fuel alternatives. Other benefits include neutral CO2 emission, producing high thermal efficiency, and the ability to locate facilities close to the feedstock supply sites to promote rural economic development1. Additionally, the resulting gases derived from the bio-gasification process serve as intermediates in the production of highefficiency power or the synthesis of chemicals and fuels2,3. Synthetic gas (syngas) is such a
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resulting gas which is produced by the combustion of biomass feedstock through the process of bio-gasification. Syngas can be considered as a viable option of replacing natural gas if the unit cost of syngas would be lower that the market price of natural gas. Therefore, a thorough investigation of the syngas supply chain network is required to identify the key factors that contribute most in determining the unit cost of syngas. To accomplish this objective, in this study we develop an optimization model that aids the design and management of a syngas supply chain network so that the overall cost of producing syngas is minimized. Syngas consists of hydrogen (H2), carbon monoxide (CO), and smaller amounts of carbon dioxide (CO2), water (H2O), methane (CH4), and nitrogen (N2)1. It can be used to produce methanol and hydrogen where each of them have the potential to meet the future fuel demand for transportation as well as can be used in power generation. However, the biggest challenge in commercializing syngas is its economic viability. Literatures such as Dornburg and Faaij4, McKendry5, and Caputo et al.6 identify few factors such as diverse biomass sources, conversion techniques, determining optimal size and location of a bio-gasification facility that hinders against syngas commercialization. Till now, a number of researchers investigate to assess the production cost of syngas in a bio-gasification facility1,7,8. Craig and Mann9, Bian et al.10, and Wei et al.1 identify that the production cost of syngas vary greatly depending on the type and quantity of feedstock, condition of the produced syngas, conversion method, type and capacity of the gasifier, and the operating procedures used. Li et al.3 observe that the syngas production cost can be lowered by increasing the handling capacity of the gasifier, decreasing material cost, and improving system operating cost. Most recently, Wei et al.1, Deng11, and Kim et al.12 estimate the syngas production cost for a micro-scale bio-gasification facility. The authors identify the key factors that contribute most in calculating the unit cost of syngas. Other studies conduct economic analysis for a large-scale gasification-based electric power generation facility8,9,13,14 and a pulp and paper industry15. Most of the prior studies attempted to find the key factors that contribute most in calculating the unit cost of syngas for a micro-scale bio-gasification facility. However, one of the biggest challenge in syngas commercialization is the high cost associated with collecting biomass from supply sites to the bio-gasification facilities received less attention. This is because biomass is bulky and difficult to transport, impacted by seasonality, and biomass is widely dispersed geographically. For these reasons, biomass collection and transportation costs are usually high. This raises a number of questions such as: (a) should the woody biomass be chipped in an on-site location, in a centralized location, or in a bio-gasification facility? (b) what will be the size and location of the chipping terminals? and (c) what will be the size and location of the biogasification facilities? These questions can be answered if a rigorous supply chain network for syngas is developed. Till now a number of studies consider design decisions in biomass supply chain network in order to deliver biomass at a more competitive price to the end users. Studies conducted by Zamboni et al.16, Eksioglu et al.17, Eksioglu et al.18, Huang et al.19, An et al.20, Bai et al.21, Wang et al.22, Marufuzzaman et al.23, and Tong et al.24 analyze plant location and transportation issues in biofuel supply chain networks under a deterministic setting. Kim et al.25, Chen and Fan26, Gebreslassie et al.27, and Marufuzzaman et al.28 further extended those formulations by considering the impact of uncertainty (e.g., feedstock supply, demand, technology) in the design and management of a biofuel supply chain network. Most recently, Marufuzzaman et al.29, Bai et
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al.30, and Marufuzzaman and Eksioglu31 studies the impact of failure risks at bio-refineries and at intermediate transportation hubs in the bio-fuel supply chain network. Finally, You et al.32, Xie and Huang33, Marufuzzaman et al.23, and Marufuzzaman et al.28 extended the cost driven optimization models to multi-objective optimization models that not only minimizes cost but also minimizes emissions from the biofuel supply chain network. To the best of our knowledge, no prior studies assess the unit cost of syngas from the supply chain point of view. To address this gap in the literature, this study attempts to answer these questions by developing an optimization model for the design and management of a syngas supply chain network. The model identifies the optimal size and location of the chipping terminals and bio-gasification facilities and the amount of biomass procured, transported, and converted to syngas from the supply chain network. The model further identifies the threshold beyond which production of syngas can be considered profitable. Note that the produced syngas can be used for methanol and hydrogen production, each of which has high potential as a future fuel for transportation as well as in power generation5,34. Therefore, in this study we did not consider the downstream supply chain network since the network will vary significantly depending on the type of fuel produced from syngas. The aim is to focus on designing a costeffective logistics network for the upstream supply chain network so that the cost of producing syngas is minimized. Another important contribution of this paper is applying the model to a real world case study. The key technical and economic data are obtained from the bio-gasification facility at Mississippi State University11. These data further scaled and used to test the economic feasibility of developing syngas supply chain network in the entire southeast region of U.S. Finally, we showed how the profitability margin of syngas production is affected by parameters such as operating mode of bio-gasification facilities, procurement cost of feedstock, conversion rate of syngas production, and irregularity in biomass supply chosen by the decision maker. The remainder of this paper is organized as follows: the following section introduces the problem and formulates the mathematical model; the next section presents numerical results and draws managerial insights; and the final section concludes this paper. PROBLEM DESCRIPTION AND MODEL FORMULATION This main objective of this study is to develop an optimization model that aids the design and management of a logistics network to minimize the location and transportation cost of delivering wood chips to bio-gasification facilities in order to produce syngas. Figure 1 presents the structure of the syngas logistic network consisting of logging residue suppliers, potential location of chipping terminals and bio-gasification facilities. The network designing problem consists of locating a set of chipping terminals and bio-gasification facilities among candidate locations so that the overall cost of delivering syngas is minimized. Consider a logistics network G = (N, A), where N is the set of nodes and A is the set of arcs. Set N consists of the set of logging residue supply sites I, the set of candidate chipping terminal locations J, and the set of candidate bio-gasification facility locations K, i.e., N = I ∪ J ∪ K. Set of arcs A is partitioned into two disjoint subsets, i.e., A = A1 ∪ A2, where A1 represents the set of arcs joining logging residue supply sites with chipping terminals and A2 represents the set of arcs
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joining chipping terminals with bio-gasification facilities. Each supply site produces si unit of logging residues in a given time horizon. The unit cost of procuring the logging residues at each supply site i ∈ I is represented by ηi. These logging residues are transported to the chipping terminals through arcs (i, j) ∈ A1 by incurring a unit transportation cost of tij. Locating a chipping terminal of capacity l ∈ L at each location j ∈ J costs a fixed set-up cost ψlj. We represent qlj as the unit cost of chipping wood from logging residues at each chipping terminal location j ∈ J of capacity l ∈ L. The processed wood chips are now transported to the bio-gasification facilities through arcs (j, k) ∈ A2 which incurs a unit transportation cost tjk. Note that depending on the size of the arcs A1 and A2 the unit transportation cost between the source and destination nodes vary significantly. Locating a bio-gasification facility of capacity l ∈ L at each location k ∈ K costs a fixed set-up cost ψlk. We represent plk as the unit cost of producing syngas at each biogasification facility k ∈ K of size l ∈ L. Finally, we set demand d as the minimum amount of syngas that needs to be produced from this entire supply chain system. The sets, input parameters, and decision variables used in this section are summarized in the Nomenclature section.
Figure 1. Network representation of syngas supply chain.
We now introduce the following location and allocation decision variables in our optimization model. The primary decision variables Y:= ∈,∈ ⋃ determine the size and location to open chipping terminals and bio-gasification facilities, i.e., /
1 if a chipping terminal of size " is opened at location $ 0 &'()*+,-);
1 if a biogasification facility of size " is opened at location 2 0 &'()*+,-);
The second set of decision variables P:= 3/ ∈,/∈ decide the amount of syngas to produce in a bio-gasification facility k of size l. The remaining decisions are how to route the biomass from
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its origin to destination. Let X:= 456 76∈8,∈ and Z:= 49/ 7∈ ,/∈ be the flow of biomass along
each link (e, r) ∈ A of the network.
The problem is to identify: where to locate bio-gasification facilities and chipping terminals among the candidate locations r ∈ J ⋃ K; what should be the production capacity of each biogasification facility and chipping terminal; how much syngas to produce at each bio-gasification facility; and how much logging residue/woodchips to deliver to a bio-gasification plant in a given time horizon. The goal is to minimize the overall system costs for the entire logistics network. The following is a mixed-integer linear programming (MILP) model formulation of the problem referred to as model [SC]. :;
