Energy supply chain optimization of hybrid feedstock processes: a review.

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Annu. Rev. Chem. Biomol. Eng. 2014.5:147-179. Downloaded from www.annualreviews.org by Universitat Zurich- Hauptbibliothek Irchel on 07/06/14. For personal use only.

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Energy Supply Chain Optimization of Hybrid Feedstock Processes: A Review Josephine A. Elia and Christodoulos A. Floudas Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08544; email: fl[email protected]

Annu. Rev. Chem. Biomol. Eng. 2014. 5:147–79

Keywords

First published online as a Review in Advance on March 12, 2014

biomass, coal, natural gas, liquid fuels, heat, power

The Annual Review of Chemical and Biomolecular Engineering is online at chembioeng.annualreviews.org

Abstract

This article’s doi: 10.1146/annurev-chembioeng-060713-040425 c 2014 by Annual Reviews. Copyright All rights reserved

The economic, environmental, and social performances of energy systems depend on their geographical locations and the surrounding market infrastructure for feedstocks and energy products. Strategic decisions to locate energy conversion facilities must take all upstream and downstream operations into account, prompting the development of supply chain modeling and optimization methods. This article reviews the contributions of energy supply chain studies that include heat, power, and liquid fuels production. Studies are categorized based on specific features of the mathematical model, highlighting those that address energy supply chain models with and without considerations of multiperiod decisions. Studies that incorporate uncertainties are discussed, and opportunities for future research developments are outlined.

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INTRODUCTION


The global energy sector is driven primarily by fossil fuels, such as petroleum, coal, and natural gas. For the United States, the Energy Information Administration projects that petroleum will remain a major energy source up to 2035 (1). With the recent discoveries of shale gas reserves and the development of fracking technology, natural gas will also play a significant role in the energy portfolio. However, there is a pressing need to address the environmental impacts caused by consuming fossil fuels and to increase the development of renewable energy sources. Currently, solar, wind, geothermal, hydropower, and nuclear energy sources make up a small percentage of the US energy portfolio, and major advances are still needed for these alternatives to play a significant role in replacing fossil fuels. Uncertainties in the development of geological storage for CO2 sequestration also limit the potential abatement in greenhouse gas (GHG) emissions through this approach. To address some of these challenges, the use of biomass as an energy source has emerged as a focus of interest because it provides emissions reduction through the uptake of atmospheric CO2 during photosynthesis (2, 3). Biomass conversion to energy products can potentially result in net negative CO2 balance provided that the biofuels and bioenergy sector is expanded and that the biomass is cultivated sustainably. In the transportation sector, corn-based ethanol and soybeanbased diesel currently make up a majority of the manufactured biofuels. However, concern about the impact on the price and availability of these feedstocks as sources of food has been raised (2), and as a result, efforts to use lignocellulosic biomass (e.g., corn stover, forest residue, wastes) have grown considerably (4). In academic literature, Floudas et al. (5) recently reviewed various studies investigating biomass-based process designs and synthesis, either as a single feedstock stream or in combination with one or two fossil-based sources, such as coal and natural gas. Process-synthesis frameworks for energy plants that postulate a process superstructure with several possible topologies and then use an optimization framework to examine the economic trade-offs between each topology have become more popular (6–20). Hybrid processes that combine multiple feedstocks can be a viable alternative owing to potential synergistic effects from the advantages of each feedstock. For example, combining coal or natural gas with biomass as feedstocks can reduce the cost of fuel production compared with a pure biomass process, and using biomass as a feedstock can reduce the emissions of coal- and natural gas–based processes. Kokossis & Yang (21) and Daoutidis et al. (22) outlined the advantages of applying a systems perspective to biomass conversion processes, including the process chemistry, design, synthesis, and supply chain of biomass-based systems, and Liu et al. (23) have addressed the challenges and opportunities in energy research and advances in energy process systems engineering. In the decision to construct new energy plants, whether fed by a single feedstock or a combination of feedstocks, logistical aspects related to the upstream and downstream operations of the plants must be considered. Profitability of such plants depends on their geographical locations with respect to the feedstock locations and product delivery destinations. The logistics of producing and transporting biomass are especially important owing to the diffused nature of biomass resources and their low energy density. Compared with carbon-based fossil fuels, such as coal and natural gas, which are produced in a centralized manner, biomass production occurs on smaller scales and in a dispersed manner (24, 25). The management of the biomass supply chain will play a significant role in the strategic placement and economic performance of biomass-based facilities. The facility must receive a steady stream of biomass for a long time horizon that will come from multiple farms and forests in neighboring counties or states. Inefficient management of these operations may be costly, and the environmental benefit of using biomass may be reduced. These challenges have prompted various studies that simultaneously consider multiple factors to maximize the supply chain profitability using optimization frameworks. Trade-offs between

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different strategic decisions are compared and investigated, and in hybrid processes where biomass feedstock is combined with coal and/or natural gas, the facility locations are optimized with respect to the centralized production of coal and natural gas and the distributed production of biomass. A series of reviews on biomass supply chain studies have been published. Gold (26) and Gold & Seuring (27) identified the main issues and the key stakeholders in a given biomass supply chain and highlighted aspects of coordination that must take place between these stakeholders. An et al. (28) reviewed the literature on biofuel supply chain research and classified studies based on the decision time frame and level in the supply chain (i.e., upstream, midstream, or downstream). Iakovou et al. (29) reviewed the features of waste biomass supply chains and potential conversion technologies for heat and power production. Scott et al. (30) highlighted the problems that attracted the most attention and the popular means of developing decision-making methods for bioenergy systems in papers published from 2000–2010. They concluded that technology selection and policy decisions were the most notable problems and that optimization was the most popular approach. Nikolopoulou & Ierapetritou (31) reviewed studies that developed green supply chain methods, taking environmental and sustainability measures into account. These studies were not limited to energy processes and included chemical processes and supply chains. Awudu & Zhang (32) highlighted the uncertainties associated with biofuel supply chain systems and the sustainability measures that must be incorporated into supply chain decision making. Sharma et al. (33) provided an extensive review of the mathematical modeling techniques for bioenergy supply chains up to the year 2011. They classified the literature into types of supply chain structures (i.e., convergent, divergent, conjoined, or general supply chain), decision levels (i.e., strategic, tactical, or operational), and the specific decisions made in each of the reviewed studies. Shabani et al. (34) reviewed supply chain contributions to the conversion of forest biomass to energy, highlighting the proposed deterministic and stochastic models. This paper reviews the development of energy supply chain optimization frameworks in the literature up to September 2013 that included production of heat, power, and transportation fuels from coal, biomass, natural gas, or a combination of those feedstocks. The characteristics of the energy supply chain problems are described below in Problem Categories. A distinct set of supply chain studies that evaluated regional potentials of biomass resources for an energy supply chain and the use of life cycle analysis (LCA) as a metric to measure supply chain performances are discussed in the section on Regional Evaluation. We then review the studies considering a supply chain formulation that did not consider the temporal aspect, as well as the contributions that did. We discuss the studies that incorporated uncertainty considerations, outline the future opportunities for energy supply chain developments, and draw conclusions.

PROBLEM CATEGORIES The supply chain optimization problems in the literature are categorized into several identifiers, as listed in Tables 1–5. The highlighted aspects of the problems include (a) the supply chain scope; (b) the supply chain structure; (c) whether environmental considerations are included in the study; (d ) the type of optimization model; (e) the type of the objective function; ( f ) what the integer and binary variables represent; and ( g) whether the formulation solved the strategic, tactical, or operational aspects of the supply chain.

Supply Chain Scope The scope of the supply chain problems can be categorized into those that investigate the upstream, downstream, or full supply chain (i.e., upstream and downstream). The upstream level is defined www.annualreviews.org • Hybrid Feedstock Processes

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as the supply chain structure that supports processes and logistic distribution prior to feedstock conversion to energy products. For biomass, the upstream processes include the harvesting and collection of feedstocks, storage, pretreatment processes, and transportation between any of those steps. The conversion into energy products can include the production of liquid fuels, heat, or electricity via a single or multiple conversion technology options. The downstream processes include the distribution of the energy products from the plants to distribution centers, storage facilities, fuel terminals, or end-use customers. The transportation of liquid fuels via one or several modes and the transmission lines for electricity are included in the downstream operations as well.

Supply Chain Structure Annu. Rev. Chem. Biomol. Eng. 2014.5:147-179. Downloaded from www.annualreviews.org by Universitat Zurich- Hauptbibliothek Irchel on 07/06/14. For personal use only.

The structure of the supply chain problem can be converging, diverging, or multinodal. A converging supply chain structure means that multiple originating nodes, such as biomass resources or pretreatment facilities, deliver materials to a single facility, and the overall supply chain problem considers only this single facility. This converging structure commonly applies to an upstream supply chain problem, such as the biomass supply logistics to a single fuels or bioenergy plant. A diverging supply chain structure begins with one node and delivers materials to multiple destinations, for example, in the downstream allocation of fuels from one refinery to multiple fuel terminals. A multinodal supply chain considers multiple sites and locations for each node in the supply chain. A node is defined as a process that requires a geographical location, whether it is fixed (e.g., locations of resources) or must be determined (e.g., location of a biorefinery determined by the supply chain solution). The multinodal formulation is the most general case for the supply chain superstructure, and it describes the structure of most supply chain optimization problems in the literature. Any upstream, downstream, or full supply chain structures can also be multinodal. Under the supply chain structure column in Tables 1–5, the specification of the nodes is included for every supply chain study.

Environmental Considerations The supply chain problem may include environmental considerations, such as carbon accounting and calculations on the emissions of GHG and/or pollutants in a LCA, in addition to economic considerations. Studies that completed LCA included factors such as emissions from feedstock acquisition, feedstock and product transportation, and emissions from the energy conversion process. Each study determines the scope of the LCA, for example, on a well-to-tank basis or a wellto-wheel/consumption basis. A well-to-tank analysis calculates the emissions from the feedstock acquisition to the delivery of energy products, but it does not include emissions from consumption of the energy products, which is included in a well-to-wheel/consumption analysis. Biomass feedstock is beneficial for improved environmental performance owing to its CO2 uptake from the atmosphere during cultivation. These analyses may also include the effect of land-use change owing to expansion in biomass production and indirect effects from replacing fossil fuel energy products with bioenergy products. Several studies have also included water considerations in the environmental accounting on top of the carbon accounting.

Model Type and Decision Variables A large portion of the supply chain optimization problems are formulated as mixed-integer linear optimization (MILP) models. Binary or integer variables are used for discrete decisions, such as the existence and selection of a biorefinery or a preprocessing facility, the transportation link between 150

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two locations, and the number of transportation units required in a given link. Continuous variables commonly represent the flow rates of materials, cost figures, and utility inputs and outputs. Few studies formulated linear optimization (LP) models or nonlinear [i.e., mixed-integer nonlinear optimization (MINLP), nonlinear optimization (NLP)] problems, and some studies performed linearization of the nonlinearities in the problem to ease the solving strategy. The optimization models aim to determine an optimal supply chain configuration that gives the highest performance, whether it be measured in economic, environmental, or social terms. A distinction is made for studies that considered a multiperiod supply chain problem. These studies solved for the optimal supply chain network with decisions specified at a given time point. The planning horizon may span from a year to multiple years, and a smaller time step (e.g., yearly, monthly) is adopted for the strategic or tactical decisions. Operational decisions may be made daily or hourly. The temporal aspect of the decisions may be strategic and tactical or only tactical. Strategic decisions include the installation of new plants of a given size and location or the decision to expand the capacity of a plant that exists from the previous time point and usually involve investment decisions. If these strategic decisions can be made at the specified time points in the formulation, the binary variables are indexed over time periods. Tactical decisions involve the logistical aspects of the problem, such as where the feedstock should be purchased from and how to deliver it to the plant. In multiperiod problems, these decisions are represented by continuous variables that signify material flow rates, and they are indexed over time. The formulations may be multiperiod in the tactical decisions but not in the strategic decisions. Typical examples are formulations that consider the seasonality of biomass supply during harvesting and nonharvesting periods for a bioenergy plant.

Objective Function The objective function of the optimization model may be economic, environmental, social, or a combination of the aforementioned factors. An economic objective function may be minimization of total cost, maximization of profit, or maximization of the net present value (NPV) of the supply chain. The environmental aspect may be included in the total cost via carbon tax; thus, the total emissions from the process or from the supply chain are multiplied by a factor for the tax, and the emissions cost is included in the objective function. The economic objective function may also be coupled with an environmental or social objective function in a multiobjective optimization framework, or the environmental component may be introduced in the set of constraints.

Strategic, Tactical, or Operational The strategic, tactical, or operational aspect of the supply chain problem depends on the time scale of the decision. Strategic decisions involve long-term decisions, such as investments for facility construction or capacity expansion, and the time scale is in the order of years. Tactical decisions consider the logistics of existing facilities, such as the optimal allocation of feedstocks and products or storage time, and the time scale may be monthly or yearly. Operational decisions involve smaller time scales, in the order of days or weeks, and deal with the detailed operations of a given facility. A large portion of the supply chain optimization problems are strategic and tactical.

REGIONAL EVALUATION Several studies evaluated the availability of biomass supply in specific regions and investigated the opportunities for biofuel and bioenergy production. This group of studies includes those that www.annualreviews.org • Hybrid Feedstock Processes

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calculated the amounts of biomass available in an area and the estimated energy production from that biomass and those that evaluated transportation options if an energy plant were built at a given location. The methods used included simulations, geographic information systems (GIS)-based models, and transportation cost calculations based on scenarios. Tatsiopoulos & Tolis (35) minimized the cost of a cotton stalk supply chain under scenarios incorporating the temporal aspect of biomass storage in a warehouse. Kumar et al. (36) proposed a ranking methodology for biomass collection and transportation systems. Aksoy et al. (37) evaluated the best biorefinery location with respect to the availability and transportation costs of poultry litter in Alabama. Ravula et al. (38) developed a biomass transportation system based on cotton logistics and compared the costs of two scenarios of management policies for scheduling truck delivery of biomass to a bioprocessing plant in Virginia using discrete event simulation. Panichelli & Gnansounou (39) used a GIS-based decision-support system to allocate forest wood residues and identify locations for two new torrefaction plants among nine candidate locations. The supply chain was solved by calculating the lowest cost of purchasing and delivering biomass to the plants. Uslu et al. (40) evaluated the role of pretreatment technologies, such as torrefaction, pelletization, and pyrolysis, on bioenergy chains, particularly in increasing the energy density of transported materials. Cundiff et al. (41) discussed the logistical constraints in developing bioenergy systems in the southeastern region of the United States. Leduc et al. (42) evaluated the supply chain costs associated with two scenarios of methanol production in Germany. Given a specified target of methanol produced, they calculated the costs of producing and transporting biomass, as well as methanol production. Yu et al. (43) developed a discrete mathematical model for mallee biomass production and transportation logistics in Western Australia. Charlton et al. (44) evaluated the potential of biomass energy systems in Wales. Marti & Gonzalez (45) combined GIS spatial studies with linear optimization for the design of a bioenergy chain. Windisch et al. (46) conducted a cost-benefit analysis for two forest owners in Finland for a 10-year time horizon to maximize the NPV of the system. Meehan & McDonnell (47) analyzed biomass availability and delivery for the University College Dublin under minimum land-use impact, maximum energy scenario, and minimum environmental impact. Sultana & Kumar (48) evaluated the transportation costs of multiple biomass feedstocks to a biorefinery and found that delivery of multiple types of biomass incurred less costs than single-biomass delivery. Zhang et al. (49) developed a geographically explicit model using GIS technology for woody biofuel production in the Upper Peninsula of Michigan. They determined optimal locations by using a transportation cost calculation methodology. Leboreiro & Hilaly (50) proposed a transportation cost model for the upstream collection, transportation, and storage of biomass. Tyndall et al. (51) analyzed the supply of woody biomass in the US Cornbelt. Gonzalez et al. (52) studied the supply and delivered cost of seven biomass feedstocks in the southern region of the United States. Hacatoglu et al. (53) performed a feasibility study on bioenergy systems for the Great Lakes region based on lignocellulosic biomass with minimal adverse impacts on food and fiber production. Thanarak (54) evaluated the potential of biomass feedstock for energy use in Thailand. Vimmerstedt et al. (55) analyzed a downstream ethanol supply chain using a system dynamics model, named the Biomass Scenario Model, developed by the National Renewable Energy Laboratory, which incorporated policy scenarios in the analysis. Zhang et al. (56) developed a simulation model for the upstream biomass supply chain that included biomass harvesting, transportation, and storage. Sacchelli et al. (57) evaluated the potential of biomass use in Italy using spatial analysis under different scenarios. Grisso et al. (58) evaluated four scenarios of switchgrass


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harvesting schedules for a year with the goal of determining the number of harvesting machines and the storage capacity required. In addition to evaluating the feedstock potential for the energy sector, several studies also evaluated the environmental performances of the supply chain and used tools such as the LCA method to evaluate and rank supply chain scenarios. Odeh & Cockerill (59) compared the LCA emissions from three systems of fossil fuel–based power plants, namely plants that included supercritical pulverized coal, natural gas combined cycle, and integrated gasification combined cycle technologies. The emissions resulting from the fuel acquisition were included in the calculations. Koornneef et al. (60) completed the LCA calculations on three pulverized coal electricity generation plants with and without carbon capture and sequestration (CCS). Slade et al. (61) calculated the LCA emissions for a cellulosic ethanol supply chain in the United Kingdom and Sweden. The full supply chain scope was considered in the calculation. Smeets et al. (62) evaluated the economic and environmental performance of miscanthus and switchgrass production in five European countries. The environmental impacts included the GHG emissions, fossil energy use during conversion and transportation, direct and indirect nitrogenous emissions, impact on water reserves, soil erosion, and biodiversity. Jaramillo et al. (63) presented results on the LCA calculations for coal-to-liquid (CTL) systems, plug-in hybrids, and hydrogen pathways. Nasiri & Zaccour (64) proposed a game-theory analysis between three players, namely the farmer, the producer of electricity, and the electric utility. Wu et al. (65) evaluated the economic feasibility of a bioslurry supply chain under different scenarios. The chain started at the production sites of mallee biomass, proceeded to the bioslurry production facilities, and ended at the bioenergy plants. Yu & Wu (66) evaluated LCA performance based on the energy and carbon footprints of the bioslurry system. The slurry from mallee biomass could also be cofired with coal. Choo et al. (67) completed a LCA calculation on an oil palm supply chain for biodiesel production. Valente et al. (68) performed a LCA calculation on the forest resources in Norway on a cradle-to-gate basis (i.e., from feedstock locations to the bioenergy plant). Tahvanainen & Anttila (69) compared different transportation scenarios for the wood supply chain in Finland using a GIS model and scenario-based calculations. The supply chain proceeded from the forests to storage facilities and ended at chipping facilities. Chiueh et al. (70) evaluated the impact of torrefaction on overall supply chain performance on a cost and GHG emissions basis. Svanberg et al. (71) evaluated the costs of torrefaction and pelletization supply chains under multiple scenarios. Den Herder et al. (72) evaluated and compared the sustainability impact assessment between forest wood chains and oil chains for heat and electricity production that included economic, environmental, and social indicators. Godard et al. (73) proposed an improved LCA method to evaluate the environmental impacts of a bioenergy supply chain for a boiler in France. The LCA included local characteristics, such as soil climate and crop management, and estimated pesticide emissions and soil organic carbons. The method was applied to six scenarios. Parish et al. (74) presented a comparative analysis of ethanol and gasoline production processes from literature based on their environmental effects. Jäppinen et al. (75) evaluated the LCA of two case studies of the biomass delivery logistics for two locations of combined heat and power (CHP) plants in Finland and concluded that train transportation could reduce emissions if the infrastructure were available. Clark et al. (76) evaluated the potential role of the Conservation Reserve Program (CRP) in growing the biofuel industry and its environmental impacts using simulations with the Biomass Scenario Model. They evaluated seven land-use scenarios and five economic scenarios but found that no large benefit was gained by using the CRP land. Acreche & Valeiro (77) calculated the GHG emissions and energy balance of an integrated sugar and ethanol supply chain, including emissions from pesticides.

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Critical Analysis


The methods used in this section identify the main and most significant factors that influence the profitability of an energy supply chain. One category of conclusions in these studies is the validation of the regional energy supply, which is the premise of the energy supply chain. If the supply does not exist, then no supply chain can be built. Meehan & McDonnell (47), Tyndall et al. (51), Thanarak (54), and Sacchelli et al. (57) validated the biomass availabilities in their respective geographical regions, calculated the energy products that could be produced by the supply, and concluded that these regions could benefit from using the biomass supply as an energy source. Meehan & McDonnell (47) highlighted that the amount of biomass available also depends on the collection scheme adopted and whether the objective is to maximize collection, minimize environmental impact, or limit the collection to a certain radius. Gonzalez et al. (52) concluded that forest biomass is cheaper than agricultural biomass, and Hacatoglu et al. (53) concluded that although biomass incurs higher costs than coal for bioenergy production, its environmental impact is lower. However, additional factors must be considered to evaluate the viability of the supply chain. The transportation of raw materials to energy conversion facilities can incur high costs, especially for biomass, due to its dispersed locations and relatively high moisture content compared with coal or natural gas, and can significantly affect the supply chain cost. Leboreiro & Hilaly (50), Ravula et al. (38), and Zhang et al. (49) found that the tools they developed improved the current transportation scheme by increasing use of the available infrastructure or locating the conversion facilities strategically. Due to a combination of the supply factor and transportation cost, Aksoy et al. (37) contended that the biorefinery should be located near the feed supply. The seasonality of biomass and the management of harvest and storage times will influence the price of the biomass supply. Biomass producers must determine the desired trade-offs between the amount of equipment used, the amount of biomass harvested and stored, and the revenue to be profitable (46, 58). Biomass is generally transported by truck, but Kumar et al. (36) and Tahvanainen & Anttila (69) suggested that train transportation would be more viable for large volumes or long distances. The transportation cost of biomass is a function not only of distance but also of energy density and moisture content (48), and this is more important in long-distance transportation. Lower energy density will require larger volume for a conversion process. To improve the energy density, which in turn may potentially reduce the transportation and total supply chain cost, methods of pretreatment and storage of biomass have been studied (40, 50). Out of the technologies considered, Uslu et al. (40) concluded that pelletization and torrefaction are economically and energetically superior to pyrolysis as pretreatment methods due to the comparatively lower efficiency in pyrolysis. Wu et al. (65) suggested that bioslurry is advantageous for long-distance transportation and for bioenergy plants to receive a uniform feedstock. It can also potentially be fed with coal. However, these pretreatment methods incur high capital costs, and good economies of scale are important in achieving economic viability (71). Chiueh et al. (70) preferred a more centralized supply chain configuration to lower transportation cost and suggested not adapting torrefaction as a pretreatment method. Thus, pretreatment should be an optional step depending on the supply chain configuration, specifically, the volume of biomass that must be processed, the distances traveled, and the locations of the energy conversion facilities. Because biomass is believed to be crucial in improving the environmental performance of energy systems, LCA and environmental analysis are important studies to validate this assumption and to compare biomass and fossil fuel systems. For fossil fuel systems, Odeh & Cockerill (59) found that methane leakage is a major source of emissions in natural gas combined cycle systems and that natural gas and coal systems must incorporate CCS to control their emissions. Further, Koornneef et al. (60) emphasized that 1 tonne of carbon captured and stored does not necessarily equal

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1 tonne of carbon avoided due to indirect emissions, such as from the production of amines for CO2 separation, the disposal of the reclaimer bottoms, and leakage from CO2 storage. Jaramillo et al. (63) reported that CTL fuels will increase emissions. Biomass, however, can mitigate GHG emissions, but only if the harvesting, handling, and processing steps are done sustainably. The upstream processes of biomass systems can still emit a high amount of carbon, and the transportation of biomass can also consume high amounts of fuel and energy (68). At sites of biomass cultivation, researchers found that fertilizer and pesticide usage contributes significantly to emissions (61, 67, 77). Choo et al. (67) also reported emissions from biogas produced during the milling of oil palm. Smeets et al. (62) highlighted that although switchgrass is an attractive energy crop to cultivate, plantation sizes should be limited to avoid seriously impacting water resources. Water impact, although not directly accounted for in LCA calculations, is a major environmental factor in biomass production and should not be ignored. Given the upstream steps of biomass processing, the conversion of biomass to fuels or energy must be evaluated in an integrated manner. Low emissions from the conversion process does not necessarily guarantee that the supply chain will have low emissions as well. Further, Slade et al. (61) highlighted that a high-yield process, such as enzymatic conversion for cellulosic ethanol, can produce high emissions owing to high electricity usage, and a lower-yield process may be chosen to lower the life cycle emissions.

SUPPLY CHAIN OPTIMIZATION FOR NOMINAL OPERATION Upstream Supply Chain Rentizelas et al. (78, 79) developed a hybrid optimization method to address an optimal location problem for the upstream supply chain for bioenergy (i.e., heat and power) generation facilities. The NLP supply chain problem was first solved by using a genetic algorithm to generate good quality starting points and subsequently by using a sequential quadratic programming method. Bauen et al. (80) modeled the availability of coppice and Miscanthus biomass species in the United Kingdom and determined the optimal feedstock allocation for a fixed plant using a LP model. Suh et al. (81) compared the transportation options to deliver corn stover in the state of Minnesota based on total cost and emissions. The supply chain went from the biomass production site to the storage locations to the conversion facilities. Bowling et al. (82) formulated a MILP to optimize the upstream biomass supply chain, including optional preprocessing hubs, with both centralized and distributed preprocessing options in the supply chain superstructure. They used binary variables to determine the locations of the biorefineries and preprocessing locations and to select a particular segment of the linearized cost functions. Dos Santos et al. (83) developed a method for natural gas transportation logistics that combined the maximization of the sales of available gas supply to fulfill the demand while mitigating the risk of contractual penalties. The key players were the shippers, suppliers, transmission companies, and local distribution companies. Thermohydraulic simulation was used for the gas flow through pipelines, Monte Carlo simulation was used for the compressor station availabilities, and a LP was used to maximize income and minimize contractual penalties. The method was named the Management System for Natural Gas Transportation Logistic. Bai et al. (84) proposed game-theory models to incorporate the farmers’ decisions for land usage (e.g., producing biomass for either food or energy) and the biofuel manufacturers’ strategic decisions (e.g., number of biorefineries and locations). Pérez-Fortes et al. (85) developed a multiobjective optimization model with economic, environmental, and social objective functions for an upstream biomass supply chain for an electricity www.annualreviews.org • Hybrid Feedstock Processes

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generation facility. The downstream part of the supply chain was the grid line connected to the facility. Ng et al. (86) developed a MILP for the upstream supply chain of rubber seed for biodiesel production in Malaysia. The binary variables indicated the existence of the processing facility between the collection points and the biodiesel production plants. Foo et al. (87) developed a LP and MILP for the supply chain of empty fruit bunch to CHP plants in Southeast Asia with the objective function to minimize the carbon footprint from the supply chain. Table 1 provides a summary and classifications of the discussed contributions, according to the definitions outlined in Problem Categories, above. Whether each optimization model addressed the strategic (S), tactical (T), or operational (O) decisions is indicated in the last column (i.e., S/T/O).


Full Supply Chain Rozakis et al. (88) developed a bilevel MILP model for decision makers in France to determine tax credit policies. The bilevel model was optimized for farmers and industry earnings given the imposed budget, social, and environmental constraints. For farmers, the decision variables were the prices at farm gate and the area allocated for biomass species. For industry, the decisions were the amount of tax credits, the set of product prices, the number of conversion units, and the quantities of products. Binary variables were used to determine the set of prices/scenarios. Dunnett et al. (89) analyzed a lignocellulosic bioethanol supply chain with pretreatment produced via the fermentation route using hypothetical demand and supply scenarios based on the UK and EU data. The spatial distribution of the material flows was represented by a grid in which urban demand centers could be located at the center or at the corner. Zamboni et al. (90, 91) studied a bioethanol supply chain system based on cost minimization (90) and simultaneous minimization of environmental impact in a multiobjective optimization framework (91). The whole set of Pareto optimal solutions was analyzed, and the preferred solution set was identified using a dedicated algorithm. The framework was applied to a Northern Italy region by dividing the region into homogeneous squares. Zamboni et al. (92) considered the minimization of GHG impact with the maximum NPV objective function, and binary variables were used if taxation at a given time was applied and if nitrogen dosage and distiller’s dried grains with solubles end use were adopted. The taxation was applied only when positive gross profit was achieved. Tittmann et al. (93) developed a mixed-integer linear, biorefinery (power and biofuels)–citing model that determined the location, size, and type of the biorefinery using GIS technology for spatial configurations in the state of California given the state’s geographical profile, demand, and transportation infrastructure. The fuel products could be transported to the distribution terminals and/or refueling stations, with the mode of transportation depending on the destination, and the model optimized the overall profit of the system. Parker et al. (94) completed a resource assessment for the western United States identifying the optimal locations, capacities, and technology options for a network of biorefineries. Lam et al. (95) proposed a two-step optimization approach to solve a supply chain problem, first through a LP model that minimized the carbon footprint objective function, then through clustering of regions, and finally by using a P-graph method. Leduc et al. (96) solved a MILP problem for the locations of gasification-based methanol production plants in Austria. Binary variables were used to determine the existence of the methanol plant and the gas stations to distribute the fuel product. Leduc et al. (97) addressed the optimal locations for lignocellulosic ethanol facilities with CHP production, with binary decisions for the existence of the plant and gas stations. Kim et al. (98) studied the biomass supply chains that produced gasoline and biodiesel via a two-step conversion process (i.e., fast pyrolysis and gasification with bio-oil intermediate) for the southeast United States in a MILP model. The 156

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Upstream Upstream Upstream

Upstream

Upstream

Upstream

Upstream

Upstream

Bauen et al. (80)

Suh et al. (81)

Bowling et al. (82)

dos Santos et al. (83)

Bai et al. (84)

Pérez-Fortes et al. (85)

Ng et al. (86)

Foo et al. (87)

Multinodal: resources-bioplant

Multinodal: collectionpreprocessingbiorefinery

Multinodal: resourcespretreatment-storagebioplant

Multinodal: resourcesmarket/biorefinery

Multinodal: shipperssuppliers-transmissiondistribution

LCA

Emissions calculation

consideration

Environmental

LP, MILP

MILP

MILP

MINLP

LP Monte Carlo simulation

MILP

LP

NLP

Model

Min. carbon footprint

Max. profit

Multiobjective max. NPV min. environmental and social impact

Max. profit

Max. income min. penalty

Max. profit

Min. cost

Min. cost

Max. NPV

function

Objective

Transportation link, bioplant location, size

Preprocessing and biorefinery location, size

Facility location, capacity, storage time

Number and location of biorefinery

Refinery and preprocessing locations, cost segment

Refinery location

Integer

Decision variables Binary

S/T/O

S/T

S/T

S/T

S/T

T

S/T

S/T

T

S/T

3 May 2014

Multinodal: resourcespreprocessingbiorefinery

Multinodal: resourcesstorage-biorefinery

Converging: resources-biorefinery

Multinodal: resourceswarehouse/biorefinery

Supply chain structure

ARI


157

Abbreviations: LCA, life cycle analysis; LP, linear optimization; MILP, mixed-integer linear optimization; MINLP, mixed-integer nonlinear optimization; NLP, nonlinear optimization; NPV, net present value; S/T/O, strategic/tactical/operational.

Upstream

Scope

Rentizelas et al. (78, 79)

Authors

Table 1 Supply chain problem descriptions: upstream supply chain for nominal operation


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system consisted of two conversion plants: a pyrolysis plant that produces bio-oil, char, and fuel gas and a second gasification-based plant that produces gasoline and biodiesel. Binary variables were used for the existence of the two conversion facilities. Corsano et al. (99) considered an ethanol supply chain with simultaneous optimization of the plant design. The supply chain included sugar plants that produced molasses, which were transported to fermentation plants that produced ethanol, as well as to sugar and ethanol warehouses for storage. The MINLP model maximized the total profit, with binary decisions for the existence of ethanol plants and ethanol and sugar warehouses, as well as the number of parallel units in the sugar and ethanol plants. Akgul et al. (100) developed a MILP model for the optimal design of a corn-based bioethanol supply chain to minimize its total cost, applied to the Northern Italy region. The mathematical model incorporated a neighborhood flow representation that governed the delivery route of materials. Binary variables were used for the plant, and integer variables were used for the number of transportation units between two locations. Akgul et al. (101) considered a similar problem formulation for a UK bioethanol case study. Bai et al. (102) proposed a formulation that identified the optimal locations of biorefineries considering traffic congestion and minimizing transportation cost. The route selections for the feedstock and product shipments were taken into account, and the framework was applied to an Illinois case study. The model was solved using a Lagrangian relaxation-based heuristic algorithm to achieve near-optimal solutions, and the optimality was improved using a branch-and-bound framework. Kocoloski et al. (103) analyzed a cellulosic ethanol supply chain in Illinois and examined the effect of facility size and location on the overall cost. The solutions generated by the mixed integer programming (MIP) problem were compared with those generated by a sequential facility siting algorithm, clustering algorithm, and uninformed or random facility placement. Natarajan et al. (104) studied the optimal locations of methanol plants in Eastern Finland using geographically explicit information and minimized the costs, including costs due to CO2 emissions. Wetterlund et al. (105) formulated a MILP for a biofuel supply chain in the European Union. The cost was minimized, including the cost of the supply chain and the cost of emitting CO2 , multiplied by a cost factor, depending on the adopted policy. Marvin et al. (106) developed a facility-location framework for the Midwest region of United States to fulfill the Renewable Fuel Standard mandate. Eight types of biomass and seven technologies were considered that produced various fuels, taking into account the existing ethanol facilities in the area. They estimated the nonlinear cost functions using piecewise linear functions. Wang et al. (107) developed a MILP model for the biomass supply chain for an electricity generation (CHP) facility in Great Britain and calculated emissions using the model. Ayoub et al. (108, 109) developed a model to optimize the biomass use from a superstructure of sources, pretreatment technologies, conversion technologies, postprocessing steps, and final products. The model was solved using a genetic algorithm to select the production pathways that minimized the cost and emissions. Ayoub et al. (110) incorporated multiple objective functions into the model, including minimization of cost, emissions, energy, and labor number. Cucek et al. (111) developed a MILP approach for a regional supply chain with a four-layer superstructure, including harvesting, preparation, processing, and the distribution of products. Cucek et al. (112) introduced a multiobjective optimization model that maximized the economic performance of the supply chain and minimized the environmental and social footprints. On top of the carbon footprint directly generated from the supply chain, they calculated indirect effects caused by product substitutions. The supply chain included the agricultural sector (harvest and storage locations), preprocessing centers, conversion facilities, and distribution sites of fuel products. The MINLP model was solved on two levels, the first to maximize the profit and the second using the multicriteria objective function. Cucek et al. (113) proposed a method to reduce


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the dimensionality of the multiobjective optimization by grouping similar footprints, including the energy, water usage, agricultural land, and water pollution footprints. Lam et al. (114) proposed a technique to reduce the model size of biomass supply chain problems by removing connections, variables with zero values, and merging zones. The approach was implemented on the model based on Cucek et al. (111). In one study, Elia et al. (24) formulated a coal, biomass, and natural gas–to-liquid supply chain problem to identify the optimal locations of facilities with the lowest cost of fuel production. The supply chain started at the feedstock source locations and ended at the demand locations. Binary variables were used to select the location, capacity, and feedstock types of the facilities. In another study, Elia et al. (25) extended the framework to include water supply, electricity need, and projected CO2 sequestration capacities. Elia et al. (115) further applied the framework to a hardwood biomass-to-liquid (BTL) supply chain and developed a ranking methodology to identify top locations for each plant type. They also studied a GTL supply chain that included nationwide, regional, and statewide supply chain scopes (116). Table 2 summarizes and classifies the contributions in full supply chain problems for nominal operation.

Critical Analysis Compared with the calculation and simulation methods described in Regional Evaluation, above, optimization methods can provide a rigorous comparison of many more scenarios. Thus, they can elucidate and quantify more trade-offs for supply chain planning, especially in cases that combine features from extreme or discrete scenarios. Optimization frameworks also can integrate economic, environmental, and social evaluations in a single framework. For example, Suh et al. (81) reported that pipeline transportation incurred less cost than rail transportation but emitted more CO2 due to the electricity required in its operation. However, in relation to the total supply chain cost, larger transportation volumes increased the transportation cost but decreased the total cost. Pérez-Fortes et al. (85) established the trade-off between the economic and environmental performance of the supply chain: In general, the decision maker must be willing to compromise approximately 60% of the NPV to maximize environmental and social performances. With more rigorous combinations of scenarios, more accurate calculations of the supply chain capacities can be achieved. A common question answered by the results is how much product can be produced with what is available, given the economic, environmental, and social constraints in the supply chain (24, 25, 94, 96, 106, 115, 116). Comparisons between different industries were also completed (93), with studies concluding that fuel production from biomass is more profitable than heat production (104) and that heat production is more profitable than food or animal feed production (90, 91, 111). Ultimately, facilities that can produce multiple products, such as fuel, power, and heat, can increase their economic performance (24, 25, 96, 105, 115, 116). Several studies compared the centralized versus decentralized supply chain configurations and examined the trade-offs between them. Centralized supply chains generally have fewer and larger processing facilities but incur higher transportation distances. However, costs are reduced from the economies of scale. For biomass, the transportation distances may incur high costs that counteract the gain from economies of scale. Decentralized supply chains have more and smaller facilities, which would lessen the transportation cost of biomass with low energy density. In the literature, centralized supply chains are generally more profitable, meaning the economies of scale are more significant than the transportation costs (90, 91, 103, 108, 109). However, to minimize the environmental impact, decentralized supply chains may perform better. Kim et al. (98) reported that the decentralized scheme was more favorable when “decentralized” meant that the pyrolysis and the Fischer-Tropsch conversions were in different locations and “centralized” meant they were in www.annualreviews.org • Hybrid Feedstock Processes

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160

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·

Floudas

Full

Full

Full

Full

Full

Full

Full

Full

Zamboni et al. (92)

Tittmann et al. (93)

Parker et al. (94)

Leduc et al. (96, 97)

Lam et al. (95)

Kim et al. (117)

Corsano et al. (99)

Multinodal: sugar supply-ethanol plant-warehouse-customer

Multinodal: resources-preprocessingbiorefinery-distribution

Multinodal: resources-biorefinery-fuel station

Multinodal: resources-biorefinery-fuel station

Multinodal: resources-biorefinery-fuel terminal

Multinodal: resources-biorefinery-fuel terminal-refueling station

Multinodal: resourcesbiorefinery-demand



Carbon footprint

LCA

LCA

Max. profit

Min. carbon footprint

Min. cost

Max. profit

Max. profit

Multiobjective max. NPV min. GHG impact

Multiobjective min. cost min. impact

Min. cost

Min. cost

Max. profit

function

Objective

MINLP Max. profit

MILP

LP

MILP

MILP

MILP

MILP

MILP

MILP

MILP

MILP

Model

Facility location, size, units

Facility location, size

Facility and gas station locations, size



Facility location, size, transportation link, tax

Facility location, size, transportation link

Facility location, size, transportation link

Facility location, size, process units

Set of prices, number of units

Integer

Number of transportation units




S/T/O

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

T

3 May 2014

Zamboni et al. (91)

Full

Emissions constraint

consideration

Environmental

ARI

Zamboni et al. (90)


Full

Dunnett et al. (89)

Supply chain structure Multinodal: farmers-industry

Scope

Rozakis et al. (88)

Authors

Table 2 Supply chain problem descriptions: full supply chain for nominal operation



Full

Full

Wang et al. (107)

Ayoub et al. (108–110)


161

Full

Multinodal: resources-refinery-demand

Multinodal: resources-preprocessingbiorefinery-demand



Multinodal: resourcespreprocessing-biorefinerypostprocessing-terminals

Multinodal: resourcesbiorefinery-terminals





LCA

Carbon footprint

LCA

Carbon footprint

Emissions calculations




Max. profit

Min. cost, emissions

Min. cost

Max. NPV

Min. cost, emissions cost

Min. cost, emissions cost

Min. cost

Min. cost

MILP

MILP

Min. cost

Max. profit

MINLP Multiobjective max. profit, social min. environmental footprints

MILP

MILP

MILP

MILP

MILP

MIP

MILP

MIP

Min. cost

Location, size, feed type, technology type

Location, size of collecting and preprocessing centers, technology type







Facility location, size, technology type


Facility location, size, travel path



S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

Abbreviations: GHG, greenhouse gas; LCA, life cycle analysis; LP, linear optimization; MILP, mixed-integer linear optimization; MINLP, mixed-integer nonlinear optimization; NPV, net present value; S/T/O, strategic/tactical/operational.

Elia et al. (24, 25, 115, 116)

Full

Full

Marvin et al. (106)

Lam et al. (114)

Full

Wetterlund et al. (105)

Full

Full

Natarajan et al. (104)

Cucek et al. (113, 118)

Full

Kocoloski et al. (103)


MILP

3 May 2014

Full

Full

Bai et al. (102)


ARI

Cucek et al. (111)

Full

Akgul et al. (100, 101)



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the same location. In the assumptions, however, the candidate locations for the centralized scheme were fewer than those in the decentralized scheme, which restricted the facility locations, possibly forcing a significant increase in the transportation cost.

SUPPLY CHAIN OPTIMIZATION FOR MULTIPERIOD OPERATION To account for the seasonal variability of biomass feedstock supply and the planning of biomass supply chains, several studies have proposed multiperiod supply chain optimization models (119–135).


Upstream Supply Chain Van Dyken et al. (136) studied the upstream biomass supply chain that incorporated the supply, pretreatment, storage, and demand locations of biomass feedstocks and developed a multiperiod MILP model that highlighted the change in biomass moisture and energy content during storage. Binary variables were used to indicate moisture pair and how long biomass was in storage. CO2 emissions from various parts of the supply chain were calculated and added to the costminimization objective function using a multiplicative factor that represented carbon tax. The model simultaneously considered decisions on investment with a five-year scope and operational decisions between one and three days with a one-hour time step. Zhu et al. (119) and Zhu & Yao (120) considered the seasonal variability of biomass feedstocks in a multicommodity network flow model. The model optimized the upstream operation of biomass supply chains and took into account the harvesting and nonharvesting seasons for switchgrass. Storage in warehouses was incorporated, the binary decisions indicated whether the warehouses or biorefineries were open at a time period, and the planning horizon was one year with a monthly time step. Shastri et al. (137) considered harvesting and nonharvesting periods in a one-year planning horizon for the optimal upstream allocation of biomass to a biorefinery. The decision variables were the equipment selection, operating schedule, biomass distribution, storage selection and size, and transportation units. The time step in the model was one day, and the model was solved for scenarios with mandatory or optional preprocessing steps. Acuna et al. (138) investigated the effect of moisture content in forest biomass supply chains with multiple species for a CHP plant in Finland. The model was a multiperiod LP with cost minimization as the objective function, solved to determine the amounts of biomass flow to the plant for a two-year planning horizon with a monthly time step. Alam et al. (139) optimized the woody biomass procurement for a generation station by using a nonlinear dynamic optimization model to determine the monthly harvest levels of the biomass feedstock. The model was solved for a one-year planning horizon divided into monthly decisions. Lim et al. (140, 141) developed a MILP to determine the product portfolio of a rice mill complex that can produce graded rice or by-products, such as rice husk or rice bran, that can be sold to the power industry. Discrete variables were used to determine the technology used, the capacities, and the location of the processing complex. The optimization of the plant design incorporated the logistic network of the biomass supply. Bernardi et al. (142) proposed a multiobjective optimization that incorporated the maximization of economic performance and minimization of carbon and water footprints. Direct and indirect effects from fuel replacement were accounted for, and LCA was done on a well-to-tank basis. The MILP model was solved for a bioethanol supply chain that included corn grain and corn stover, and the decision variables included the technology selection for bioethanol production, the by-products, and the end-use options. Palander (143) formulated a multiobjective linear model 162

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for the scheduling of a CHP plant in Finland. The model solved for the optimal procurement of fuels for the plant (e.g., fossil, peat, and wood wastes) for a twelve-month period in a one-month interval. Giarola et al. (122) formulated a multiobjective MILP for the selection of the bioethanol conversion technology and capacity planning of a single production plant that maximizes NPV and minimizes the total emissions. The LCA was done on a well-to-tank basis.


Downstream Supply Chain Kostin et al. (144) proposed a multiperiod model for the downstream supply chain of sugar cane for sugar and ethanol production in Argentina. The three-echelon supply chain consisted of the production facilities, storage, and final market for the products. A MILP model was formulated to optimize the supply chain configuration, with binary variables to represent the existence of transportation links between regions and integer variables for the number of production plants, storage facilities, and transportation modes. Continuous variables were introduced for material flow rates. A rolling-horizon strategy was implemented to reduce the computational burden. Copado-Méndez et al. (145) developed a means to combine the branch-and-cut method with large neighborhood search to solve the large-scale MILP associated with the supply chain problems. The method was applied to the supply chain problem in Kostin et al. (144). Table 3 summarizes and classifies the contributions in the upstream and downstream supply chain problems for multiperiod operation.

Full Supply Chain ˘ et al. (125, 146) proposed a mathematical model for a supply chain that consisted of Eks¸ioglu biomass harvesting sites, collection facilities, biorefineries, blending facilities, and fuel demand locations. Binary variables were used to determine the existence of collection facilities and biorefineries. The framework was applied to the state of Mississippi. The model considered the transfer of materials in storage for the next time step, including materials stored in the collection facilities, at the biorefineries, and at the blending facilities. The multiperiod consideration applied only to the stored materials. Hainoun et al. (147) determined a strategy for the long-term energy supply for Syria using the MESSAGE model (Model for Energy Supply Strategy Alternatives and their General Environmental Impacts), which optimized and evaluated alternative energy supply strategies, given the users’ constraints. The key nodes were the resources both at the primary level, including oil and gas fields, uraniums, and coal mines, and at the secondary level, including energy or electricity from oil and natural gas, as well as the demand sites. The goal was to find a distribution of energy production and use to fulfill the long-term demand. Unsihuay-Vila et al. (148) developed a multistage MILP model for the planning of electricity and natural gas systems. The supply chain included the natural gas sources, pipelines, power generation, and transmission, and the objective was to minimize cost. Leduc et al. (149) developed a multiperiod MILP model to identify the locations of methanol production plants in a county in Northern Sweden with demand scenarios up to 2025. The supply chain included the biomass locations, the plant, and the fuel station. The binary variables were used to determine whether a certain plant and fuel station was in operation at a given time point. Huang et al. (127) studied the waste biomass-to-ethanol supply chain systems in the state of California and formulated a multiperiod optimization model for the biofuels supply chain with decisions made annually. The capacity of previously existing refineries could be added into the formulation, and decision variables included the capacity expansion of the system each year. An et al. (126) analyzed a region in Central Texas and incorporated the variability in biomass availability, moisture content, and the demand www.annualreviews.org • Hybrid Feedstock Processes

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·

Upstream

Upstream

Upstream

Upstream

Acuna et al. (138)

Floudas

Alam et al. (139)

Lim et al. (140, 141)

Converging: fieldplant-distribution center/market

Converging: supply-storagepreprocessingCHP plant

Converging: supply-storagepreprocessingCHP plant

Converging: supply-storagepreprocessingbiorefinery

Multinodal: supply-storagebiorefinery

Multinodal: supplyproductionstorage-demand

structure

Supply chain

-

-

-

-

-

Emissions calculations, carbon tax

consideration

Environmental

Multisite resource efficient model multiperiod

Nonlinear dynamic optimization

LP multiperiod

MILP multiperiod

MILP multiperiod

MILP multiperiod

Model

Max. profit

Min. cost

Min. cost

Max. profit

Max. profit

Min. cost

function

Objective

Technology selection, capacity, location of complex

Equipment selection, storage location, size

Number, location, and size of biorefinery and warehouse

Moisture pair

Number of harvesting units

Integer


S/T/O

S/T

S/T

S/T

T/O

S/T

S/T/O

3 May 2014

Shastri et al. (137)

Upstream

Upstream

Scope

ARI

Zhu et al. (119, 120)

van Dyken et al. (136)

Authors

Table 3 Supply chain problem descriptions: upstream and downstream supply chain for multiperiod operation



Upstream

Downstream Multinodal: productionstorage-market

Downstream Multinodal: productionstorage-market

Giarola et al. (122)

Kostin et al. (144)

CopadoMéndez et al. (145)

-

-

MILP multiperiod

MILP multiperiod

MILP multiperiod

Max. NPV

Max. NPV

Multiobjective max. NPV min. emissions

Multiobjective min. cost and penalty for tardiness

Multiobjective max. NPV min. carbon and water footprint

Transportation links


Technology selection, capacity

Technology selection, by-products, end use

Number of plants, storage, transportation units

Number of plants, storage, transportation units

Abbreviations: CHP, combined heat and power; LCA, life cycle analysis; LP, linear optimization; MILP, mixed-integer linear optimization; NPV, net present value; S/T/O, strategic/tactical/operational.

LCA

Dynamic LP multiperiod

MILP multiperiod

S/T

S/T

S/T

T

S/T

3 May 2014

Converging: cultivationbiorefinery

-

LCA, carbon and water footprints

ARI

Converging: forest-biorefinery

Upstream

Palander (143)

Converging: supply-treatmentbiorefinery

Upstream

Bernardi et al. (142)




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profile of fuel by dividing a one-year planning horizon into four quarters. The nodes were the feed production sites, preprocessing sites, conversion sites, distribution sites, and consumption by end users. The binary decisions on the locations and the size of facilities were made once, and the flow rates and transportation connections were decided monthly. Sharma et al. (131) formulated a multiperiod MILP financial planning model to maximize stakeholder value in the design of a biorefinery with its supply chain configuration. Binary variables represented the technology selection, the decision of whether or not to expand the capacity, and the feedstock selection. Liu et al. (132) studied an energy supply chain network consideration for coal- and biomassto-liquid (CBTL) systems and determined the optimal locations of certain chemical production centers by solving a linearized MIP. The outlined case study investigated the optimal strategic planning for CBTL technologies in the United Kingdom, identifying the optimal locations for these plants that would result in high profitability over the entire planning horizon. Binary variables were used for decisions on material flow linkages, importation, sequester waste, and whether to expand or shrink transportation fleets and plants. Papapostolou et al. (133) examined a case study in Greece that considered water and land usage for a biodiesel supply chain. The decision to import or export was incorporated for biomass and fuels products. Andersen et al. (134) proposed a formulation for the biodiesel supply chain in Argentina considering land usage and crop competition. They used binary variables for transportation links and decisions for annual transportation contracts and integer variables for the number of warehouses and new plants built each period. The model was solved for a seven-year planning period divided into 84 periods. Avami (150) formulated a MILP model for the biofuel supply chain in Iran to minimize the total cost of capital and operations. At the resource node, the allocation of available farm lands was optimized for each biomass feedstock. The decision variables were the technology selection and the potential capacity of each region to cultivate specific biomass. Avami (150) applied the model to a bioethanol and bio-ETBE (ethyl-tert-butyl-ether) system from wastes, residues, and energy crops and applied the model for a biodiesel system (151). Bernardi et al. (152) considered a multiobjective optimization model with economic, carbon footprint, and water environmental footprint objective functions. The LCA was conducted on the supply chain, and the binary decisions were the technology selection and location of the corn stover for ethanol biorefineries in Italy. Varela et al. (153) proposed a bi-objective optimization approach to simultaneously maximize profit and minimize environmental impacts. The conflicting objectives were reformulated as a symmetry fuzzy linear program, whereas the supply chain problem was modeled as a MILP. They applied the method to the pulp and paper industry in Portugal. Giarola et al. (121) studied the corn grain– and stover-based bioethanol supply chain in a multiperiod formulation to maximize NPV and minimize emissions. The problem was solved for a 15-year planning horizon, and the LCA was done on a well-to-tank basis. You & Wang (129) proposed a multiobjective, multiperiod MILP model to simultaneously maximize the economic performance and minimize the environmental impacts of a BTL supply chain that produced hydrocarbons via gasification systems followed by Fischer-Tropsch conversions, or via fast pyrolysis followed by hydroprocessing steps, such as hydrotreating and hydrocracking. They investigated scenarios of distributed and centralized supply chain designs in which the biomass was either processed directly in one large or four small gasification plants or preprocessed in four fast pyrolysis plants and a centralized gasification plant. They used binary variables for the existence of conversion facilities, preconversion facilities, or intermediate upgrading facilities, and they used multiperiod decisions for the seasonality of the biomass supply. You et al. (154) proposed a multiobjective, multiperiod MILP model that incorporated the social impacts of a biorefinery by accounting for the number of jobs created and the improvement to the regional economy. The multiobjective MILP model included economic, environmental, and social impact


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objective functions and was applied to an Illinois case study. Binary decisions were made for the existence and location of the biorefineries and collection facilities. For a UK case study, Akgul et al. (155) introduced a multiobjective optimization formulation for bioethanol production via a hybrid of first- and second-generation conversion technologies. The life cycle emissions calculations were done on a well-to-tank basis. The formulation was based on Akgul et al. (100), and the formulation for the multiperiod model was included in the appendix. Binary variables were used for plant decisions, and integer variables were used for the number of transportation units between two locations. Table 4 summarizes and classifies the contributions to the full supply chain problems for multiperiod operations.


Critical Analysis The multiperiod formulations of supply chain planning are especially useful for biomass management. Moisture content was found to be a significant factor in the operations of the supply chain, affecting storage time and drying periods (136, 138, 139). Zhu et al. (119), Zhu & Yao (120), and You et al. (154) found that multiple feedstock species can hedge seasonability, and Lim et al. (140, 141) showed results with periods of biomass harvesting and inventory building. Additionally, multiperiod formulations can address future projection of supply chain systems and provide estimates on whether certain energy systems will be viable in the long term and whether they will meet expected environmental policies. For example, Hainoun et al. (147) showed that in the case of Syria, oil and natural gas will continue to play a dominant role through 2030. Giarola et al. (121, 122) and Akgul et al. (100) found that second-generation bioethanol is more sustainable in terms of GHG emissions in the long term, even though it is presently less economical than first-generation technology, and incentives are required to promote its development.

UNCERTAINTY CONSIDERATIONS IN SUPPLY CHAIN The long-term scope of supply chain strategic decisions involves changes in model parameters, such as feedstock availabilities, product demands, materials prices, and government policies. To ensure the long-term performance of the supply chain, these decisions must be robust with respect to these uncertainties. The following studies incorporated uncertainty considerations in their model formulations. Marvin et al. (156) analyzed the upstream biomass-to-ethanol supply chain in the midwestern United States and identified the optimal locations of the biorefineries and the product capacities produced by the supply chain, taking into account the sales of ethanol product in the maximization of NPV objective function. They determined the robustness of the solution against uncertain parameters using Monte Carlo simulation. Kim et al. (117) incorporated uncertainties into the model from Kim et al. (98), and the biomass supply, fuel demands, prices, and conversion technologies were modeled in a two-stage mixed-integer stochastic programming model. The robustness of the solution was analyzed using Monte Carlo simulation. Dal-Mas et al. (124) formulated a strategic planning and investment capacity planning problem and considered uncertainties in biomass costs and product selling prices. The uncertainties were accounted for by incorporating the expected NPV and conditional value-at-risk (CVaR) into the objective function. Binary variables were used for the location and capacities of the plant and for transportation links between two regions. Chen & Fan (128) studied the waste biomass-to-ethanol systems in the state of California. They implemented a two-stage stochastic programming model to incorporate the uncertainties in fuel demands and feedstock supplies. Binary variables were used to decide the location and size of the biorefineries and the terminals, and the model was solved using Lagrangian www.annualreviews.org • Hybrid Feedstock Processes

167

168

Elia

· Multinodal: natural gas wells-liquefied natural gas terminals-storagehydro/wind/thermal plants

Full

Full

Full

Full

Full

Full

Full

UnsihuayVila et al. (148)

Leduc et al. (149)

Huang et al. (127)

Floudas

An et al. (126)

Sharma et al. (131)

Liu et al. (132)

Papapostolou et al. (133)

Multinodal: resources-biorefineryblend site

Multinodal: resourcesbiorefinery-consumption

Multinodal: resources-biorefinery-demand

Multinodal: resourcespreprocessing-conversiondistribution-consumption

Multinodal: resources-biorefinery-end users -

-

-

-

-

Waste, sequestration calculation

LCA

LCA

-

consideration

Environmental

MILP multiperiod

MIP multiperiod

MILP multiperiod

MILP multiperiod

MILP multiperiod

MILP multiperiod

MILP multiperiod

MILP multiperiod

MILP multiperiod

Model

Max. profit

Max. NPV

Max. stakeholder value

Max. profit

Min. cost

Min. cost

Min. cost

Min. cost

Min. cost

function

Objective

Technology type, capacity, location

Flow link, import, sequestration, size of transportation fleet, plant size

Technology type, expansion decision, feed type

Plant size, location

Refinery type, location, size, expansion in a given year

Location of plant or station in a given year

Location, technology selection

Combination of technology

Number of plants, size, location of collection facilities and biorefineries


Integer


S/T/O

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T/O

3 May 2014

Multinodal: resources-plant-fuel station

Multinodal: resources-demand

Multinodal: harvest sites-collection facilities-biorefinery-blend sites-demand

Full

Full

˘ Eks¸ioglu et al. (125, 146)


ARI

Hainoun et al. (147)

Scope

Authors

Table 4 Supply chain problem descriptions: full supply chain for multiperiod operation




169

Full

Akgul et al. (155)


Multinodal: resourcescollection-biorefinery-demand



LCA

LCA

LCA

LCA

LCA eco-indicator

MILP multiperiod

MILP multiperiod

MILP multiperiod

MILP multiperiod

MILP/SFLP multiperiod

MILP multiperiod

Multiobjective min. cost min. emissions

Multiobjective max. NPV min. impact max. social impact

Multiobjective min. cost min. emissions

Multiobjective max. NPV min. emissions

Multiobjective max. profit min. environmental impacts

Multiobjective max. NPV min. GHG, footprints

Min. cost

Max. NPV

Technology type, capacity, location

Technology type, capacity, location, number of biorefinery and collection site

Technology type, capacity, location of preprocessing and biorefinery

Technology type, capacity in a given period

Technology type, storage

Technology type, location

Technology type, land area

Transportation link, contract decision


Number of warehouses, plants

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

Abbreviations: GHG, greenhouse gas; LCA, life cycle analysis; MILP, mixed-integer linear optimization; NPV, net present value; SFLP, symmetric fuzzy linear programming; S/T/O, strategic/tactical/operational.

Full

You et al. (154)

Full


Full


Full

Varela et al. (153)

LCA

MILP multiperiod

MILP multiperiod

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You & Wang (129)


Full

Bernardi et al. (152)

-

-

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Multinodal: resources-storagebiorefinery-distribution site

Full

Avami (150, 151)

Multinodal: resources-storagebiorefinery-blend/distribution site

Full

Andersen et al. (134)



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relaxation–based decomposition. Gebreslassie et al. (130) considered the optimal hydrocarbon biorefinery supply chains under supply and demand uncertainties using a multiperiod, two-stage stochastic programming problem. The objective function minimized the annualized cost and the financial risk of the supply chain, measuring financial risk by conditional value-at-risk and downside risk. Walther et al. (135) incorporated the technology selection for second-generation biodiesel production in Northern Germany into the supply chain framework. Four criteria of risk attitudes were implemented to address uncertainty, resulting in four types of objective functions. Giarola et al. (123) incorporated a carbon-trading scheme into their multiobjective MILP (122) for the selection of the bioethanol conversion technology and capacity planning of a single bioethanol production plant. Uncertainties in the feedstock cost and carbon cost were incorporated via two-stage stochastic programming, and binary variables were used for the facility type and existence of tax in a certain time period. McLean & Li (157) proposed a method to address uncertainty in a two-stage stochastic programming framework that combined the scenario and robust approaches. The first-stage decision was the development of the supply chain (e.g., location, capacities, modes of transportation), and the second-stage decisions were the operational aspects of the supply chain. The objective was to maximize profit. They proposed three approximating formulations and two robust scenario formulations, namely, the naive robust scenario formulation and affinely adjustable robust scenario formulation, to solve the problem. The framework was applied to the four-layer bioenergy supply chain proposed by Cucek et al. (111). Table 5 summarizes and classifies the contributions to supply chain problems with uncertainty considerations.


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Critical Analysis The studies that incorporated uncertainty into supply chain planning concluded that the deterministic cases generally overpredict profit or NPV or underpredict total supply chain costs. To manage risk, trade-offs exist in which higher cost is incurred to hedge against risk, and vice versa (130). However, given the stochastic nature of the uncertainties, there is a chance that the supply chain will be unprofitable. In a case study, Dal-Mas et al. (124) compromised the demand fulfillment (i.e., less than 100% fulfillment) and suffered economic loss in exchange for lower risks. The CVaR method resulted in a conservative decision to suggest not entering the market with the given supply and infrastructure. Marvin et al. (156) reported a 21.5% chance that no industry will be developed with the given uncertainties, in which case a tax credit or government policies may help. Giarola et al. (123) reported that carbon trading schemes may improve profit, especially for second-generation bioethanol technology that produces less total emissions than first-generation technology. In essence, the policy may promote the development of more sustainable technology in the market. Ideally, the design of energy supply chains should be economically viable without government subsidies or tax credits. Although these policies may assist in the development of an emerging industry, the supply chain should not rely on these policies for its long-term profit generation. Studies showed that coproduction of several materials is beneficial and will improve the viability of the supply chains without policy incentives.

OPPORTUNITIES Based on the literature, the following opportunities are highlighted for the future developments of energy supply chains: 170

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Converging: resources-preprocessingbiorefinery-demand

Converging: resources-biorefinery



Multinodal: resourcesbiorefinery-terminal-city


Multinodal: resources-preprocessingbiorefinery-distribution

LCA carbon trading

consideration

Environmental

MILP

MILP multiperiod

MILP multiperiod

MILP two-stage stochastic

MILP two-stage stochastic

MILP multiperiod

MILP two-stage stochastic Monte Carlo simulations

MILP Monte Carlo simulations

Model

Max. profit

Max. NPV min. GHG

Max. NPV

Min. cost min. risk CVaR and downside risk

Min. cost

Max. NPV, CVaR

Max. profit

Max. NPV

function

Objective

Biorefinery location, size, mode of transportation

Biorefinery location, size, technology selection, tax

Biorefinery location, size, technology selection each period

Biorefinery number, location, size, technology selected

Biorefinery and terminal location and size

Facility location, size, transportation links



Binary

Integer

Decision variables

Abbreviations: CVaR, conditional value-at-risk; GHG, greenhouse gas; LCA, life cycle analysis; MILP, mixed-integer linear optimization; NPV, net present value; S/T/O, strategic/tactical/operational.

Full

McLean & Li (157)

Full

Gebreslassie et al. (130)

Upstream

Full

Chen & Fan (128)


Full

Dal-Mas et al. (124)

Full

Full

Kim et al. (117)

Multinodal: resources-biorefinery


S/T/O

S/T

S/T

S/T

S/T

S/T

S/T

S/T

S/T

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Walther et al. (135)

Upstream

Scope

ARI

Marvin et al. (156)

Authors

Table 5 Supply chain problem descriptions: uncertainty



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Hybrid supply chains. A vast majority of the supply chain studies considered energy systems with a single type of feedstock, and several considered a combination of biomass species. However, studies and opportunities exist to address hybrid feedstock energy systems that combine (a) biomass and natural gas, (b) coal and natural gas, and (c) biomass and coal. The identification of facility locations and resource distribution for these systems may differ from the single-feedstock energy systems, and synergies may be gained by combining feedstocks. Downstream operations. Because biomass has been the focus of many studies, the upstream operations of energy supply chains have been emphasized. However, developments to refine the downstream operations of energy products are necessary, especially for systems that produce multiple products. For example, a polygenerational system that produces liquid fuels and electricity must consider both the layout of the fuel market and the grid transmission lines to distribute power. Expansions from the current infrastructure should also be considered and simultaneously optimized with the strategic locations of these plants. Nationwide supply chains. A large supply chain scope increases the computational complexity and creates challenges in solution strategies. However, nationwide studies on energy supply chain systems are significant in informing policies and future directions for the energy sector. Furthermore, a nationwide profile on the correlation between technology selection and feedstock usage should be investigated. Are first-generation biofuels preferred over second-generation biofuels in certain regions, and do natural gas conversions have better performance for the same regions? Methods to address these types of questions and solve large-scale optimization problems are opportunities for future research.

CONCLUSIONS Since the beginning of 2000, great interest has emerged in the development of decision-making tools to manage energy supply chains, especially from biomass resources. Due to the dispersed nature of biomass resources and their low energy content, the upstream logistical operations to supply biomass feedstock to energy conversion plants in a sustainable manner are a challenge. Methods to evaluate supply chain performances include simulations, calculations, and optimization, but optimization has become a dominant approach due to its ability to simultaneously consider multiple aspects of the supply chain. We have reviewed contributions that evaluated hybrid feedstock supply chains via the aforementioned methods, with special emphasis on optimization. Studies were categorized into those that solved for supply chain configurations in a single solve and those that formulated multiperiod supply chains. Studies were highlighted that incorporated uncertainty considerations in the supply chain. Future developments of energy supply chain studies should include systems that combine multiple feedstock inputs, such as biomass and natural gas, coal and natural gas, or biomass and coal; further refinement of the downstream operations of energy systems; and methods to address large-scale supply chain systems.

DISCLOSURE STATEMENT The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

ACKNOWLEDGMENTS The authors acknowledge partial financial support from the National Science Foundation (NSF EFRI-0937706, CBET-1158849, CBET-1263165), Lockheed Martin Corporation, Primus 172

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Green Energy Inc., and the Andlinger Center for Energy and the Environment at Princeton University.


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92. Zamboni A, Murphy RJ, Woods J, Bezzo F, Shah N. 2011. Biofuels carbon footprints: whole-systems optimisation for GHG emissions reduction. Bioresour. Technol. 102:7457–65 93. Tittmann PW, Parker NC, Hart QJ, Jenkins BM. 2010. A spatially explicit techno-economic model of bioenergy and biofuels production in California. J. Transp. Geogr. 18:715–28 94. Parker N, Tittmann P, Hart Q, Nelson R, Skog K, et al. 2010. Development of a biorefinery optimized biofuel supply curve for the Western United States. Biomass Bioenergy 34:1597–607 95. Lam HL, Varbanov PS, Klemeˇs JJ. 2010. Optimisation of regional energy supply chains utilising renewables: P-graph approach. Comp. Chem. Eng. 34:782–92 96. Leduc S, Schwab D, Dotzauer E, Schmid E, Obersteiner M. 2008. Optimal location of wood gasification plants for methanol production with heat recovery. Int. J. Energy Res. 32:1080–91 97. Leduc S, Starfelt F, Dotzauer E, Kindermann G, McCallum I, et al. 2010. Optimal location of lignocellulosic ethanol refineries with polygeneration in Sweden. Energy 35:2709–16 98. Kim J, Realff MJ, Lee JH, Whittaker C, Furtner L. 2011. Design of biomass processing network for biofuel production using an MILP model. Biomass Bioenergy 35:853–71 99. Corsano G, Vecchietti AR, Montagna JM. 2011. Optimal design for sustainable bioethanol supply chain considering detailed plant performance model. Comp. Chem. Eng. 35:1384–98 100. Akgul O, Zamboni A, Bezzo F, Shah N, Papageorgiou LG. 2011. Optimization-based approaches for bioethanol supply chains. Ind. Eng. Chem. Res. 50:4927–38 101. Akgul O, Shah N, Papageorgiou LG. 2012. Economic optimisation of a UK advanced biofuel supply chain. Biomass Bioenergy 41:57–72 102. Bai Y, Hwang T, Kang S, Ouyang Y. 2011. Biofuel refinery location and supply chain planning under traffic congestion. Transp. Res. B 45:162–75 103. Kocoloski M, Griffin WM, Matthews HS. 2011. Impacts of facility size and location decisions on ethanol production cost. Energy Policy 39:47–56 104. Natarajan K, Leduc S, Pelkonen P, Tomppo E, Dotzauer E. 2012. Optimal locations for methanol and CHP production in Eastern Finland. Bioenergy Res. 5:412–23 105. Wetterlund E, Leduc S, Dotzauer E, Kindermann G. 2012. Optimal localisation of biofuel production on a European scale. Energy 41:462–72 106. Marvin WA, Schmidt LD, Daoutidis P. 2013. Biorefinery location and technology selection through supply chain optimization. Ind. Eng. Chem. Res. 52:3192–208 107. Wang S, Hastings A, Smith P. 2012. An optimization model for energy crop supply. GCB Bioenergy 4:88–95 108. Ayoub N, Seki H, Naka Y. 2009. Superstructure-based design and operation for biomass utilization networks. Comp. Chem. Eng. 33:1770–80 109. Ayoub N, Yuji N. 2012. Demand-driven optimization approach for biomass utilization networks. Comp. Chem. Eng. 36:129–39 110. Ayoub N, Elmoshi E, Seki H, Naka Y. 2009. Evolutionary algorithms approach for integrated bioenergy supply chains optimization. Energy Convers. Manag. 50:2944–55 111. Cucek L, Lam HL, Klemes JJ, Varbanov PS, Kravanja Z. 2010. Synthesis of regional networks for the supply of energy and bioproducts. Clean Technol. Environ. Policy 12:635–45 112. Cucek L, Varbanov PS, Klemes JJ, Kravanja Z. 2012. Total footprints-based multi-criteria optimisation of regional biomass energy supply chains. Energy 44:135–45 113. Cucek L, Klemes JJ, Varbanov PS, Kravanja Z. 2013. Dealing with high-dimensionality of criteria in multiobjective optimization of biomass energy supply network. Ind. Eng. Chem. Res. 52:7223–39 114. Lam HL, Klemeˇs JJ, Kravanja Z. 2011. Model-size reduction techniques for large-scale biomass production and supply networks. Energy 36:4599–608 115. Elia JA, Baliban RC, Floudas CA, Gurau B, Weingarten MB, Klotz SD. 2013. Hardwood biomass to gasoline, diesel, and jet fuel: 2. Supply chain optimization framework for a network of thermochemical refineries. Energy Fuels 27:4325–52 116. Elia JA, Baliban RC, Floudas CA. 2014. Nationwide, regional, and statewide energy supply chain optimization for natural gas to liquid transportation fuel (GTL) systems. Ind. Eng. Chem. Res. 53:5366–97 117. Kim J, Realff MJ, Lee JH. 2011. Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty. Comp. Chem. Eng. 35:1738–51 www.annualreviews.org • Hybrid Feedstock Processes

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118. Cucek L, Klemes JJ, Varbanov PS, Kravanja Z. 2012. Reducing the dimensionality of criteria in multiobjective optimisation of biomass energy supply chains. Chem. Eng. Trans. 29:1231–36 119. Zhu X, Li X, Yao Q, Chen Y. 2011. Challenges and models in supporting logistics system design for dedicated-biomass-based bioenergy industry. Bioresour. Technol. 102:1344–51 120. Zhu X, Yao QZ. 2011. Logistics system design for biomass-to-bioenergy industry with multiple types of feedstocks. Bioresour. Technol. 102:10936–45 121. Giarola S, Zamboni A, Bezzo F. 2011. Spatially explicit multi-objective optimisation for design and planning of hybrid first and second generation biorefineries. Comp. Chem. Eng. 35:1782–97 122. Giarola S, Zamboni A, Bezzo F. 2012. Environmentally conscious capacity planning and technology selection for bioethanol supply chains. Renew. Energy 43:61–72 123. Giarola S, Zamboni A, Bezzo F. 2012. A comprehensive approach to the design of ethanol supply chains including carbon trading effects. Bioresour. Technol. 107:175–85 124. Dal-Mas M, Giarola S, Zamboni A, Bezzo F. 2011. Strategic design and investment capacity planning of the ethanol supply chain under price uncertainty. Biomass Bioenergy 35:2059–71 ˘ SD, Acharya A, Leightley LE, Arora S. 2009. Analyzing the design and management of biomass125. Eks¸ioglu to-biorefinery supply chain. Comp. Ind. Eng. 57:1342–52 126. An H, Wilhelm WE, Searcy SW. 2011. A mathematical model to design a lignocellulosic biofuel supply chain system with a case study based on a region in Central Texas. Bioresour. Technol. 102:7860–70 127. Huang Y, Chen CW, Fan Y. 2010. Multistage optimization of the supply chains of biofuels. Transp. Res. E 46:820–30 128. Chen CW, Fan Y. 2012. Bioethanol supply chain system planning under supply and demand uncertainties. Transp. Res. E 48:150–64 129. You F, Wang B. 2011. Life cycle optimization of biomass-to-liquid supply chains with distributedcentralized processing networks. Ind. Eng. Chem. Res. 50:10102–27 130. Gebreslassie BH, Yao Y, You F. 2012. Design under uncertainty of hydrocarbon biorefinery supply chains: multiobjective stochastic programming models, decomposition algorithm, and a comparison between CVaR and downside risk. AIChE J. 58:2155–79 131. Sharma P, Sarker BR, Romagnoli JA. 2011. A decision support tool for strategic planning of sustainable biorefineries. Comp. Chem. Eng. 35:1767–81 132. Liu P, Whitaker A, Pistikopoulos EN, Li Z. 2011. A mixed-integer programming approach to strategic planning of chemical centers: a case study in the UK. Comp. Chem. Eng. 35:1359–73 133. Papapostolou C, Kondili E, Kaldellis JK. 2011. Development and implementation of an optimisation model for biofuels supply chain. Energy 36:6019–26 134. Andersen F, Iturmendi F, Espinosa S, Diaz MS. 2012. Optimal design and planning of biodiesel supply chain with land competition. Comp. Chem. Eng. 47:170–82 135. Walther G, Schatka A, Spengler TS. 2012. Design of regional production networks for second generation synthetic bio-fuel—a case study in Northern Germany. Eur. J. Oper. Res. 218:280–92 136. van Dyken S, Bakken BH, Skjelbred HI. 2010. Linear mixed-integer models for biomass supply chains with transport, storage and processing. Energy 35:1338–50 137. Shastri YN, Rodriguez LF, Hansen AC, Ting KC. 2012. Impact of distributed storage and pre-processing on Miscanthus production and provision systems. Biofuels Bioprod. Bioref. 6:21–31 138. Acuna M, Anttila P, Sikanen L, Prinz R, Asikainen A. 2012. Predicting and controlling moisture content to optimise forest biomass logistics. Croat. J. Forest Eng. 33:225–38 139. Alam MB, Pulkki R, Shahi C, Upadhyay T. 2012. Modeling woody biomass procurement for bioenergy production at the Atikokan Generating Station in Northwestern Ontario, Canada. Energies 5:5065–85 140. Lim JS, Manan ZA, Hashim H, Alwi SRW. 2013. Optimal multi-site resource allocation and utility planning for integrated rice mill complex. Ind. Eng. Chem. Res. 52:3816–31 141. Lim JS, Manan ZA, Alwi SRW, Hashim H. 2013. A multi-period model for optimal planning of an integrated, resource-efficient rice mill. Comp. Chem. Eng. 52:77–89 142. Bernardi A, Giarola S, Bezzo F. 2012. Optimizing the economics and the carbon and water footprints of bioethanol supply chains. Biofuels Bioprod. Bioref. 6:656–72 143. Palander T. 2011. Modelling renewable supply chain for electricity generation with forest, fossil, and wood-waste fuels. Energy 36:5984–93


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Annual Review of Chemical and Biomolecular Engineering

Contents


Volume 5, 2014

Plans and Detours James Wei p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 1 Simulating the Flow of Entangled Polymers Yuichi Masubuchi p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p11 Modeling Chemoresponsive Polymer Gels Olga Kuksenok, Debabrata Deb, Pratyush Dayal, and Anna C. Balazs p p p p p p p p p p p p p p p p p p p35 Atmospheric Emissions and Air Quality Impacts from Natural Gas Production and Use David T. Allen p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p55 Manipulating Crystallization with Molecular Additives Alexander G. Shtukenberg, Stephanie S. Lee, Bart Kahr, and Michael D. Ward p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p77 Advances in Mixed-Integer Programming Methods for Chemical Production Scheduling Sara Velez and Christos T. Maravelias p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p97 Population Balance Modeling: Current Status and Future Prospects Doraiswami Ramkrishna and Meenesh R. Singh p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 123 Energy Supply Chain Optimization of Hybrid Feedstock Processes: A Review Josephine A. Elia and Christodoulos A. Floudas p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 147 Dynamics of Colloidal Glasses and Gels Yogesh M. Joshi p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 181 Rheology of Non-Brownian Suspensions Morton M. Denn and Jeffrey F. Morris p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 203 Factors Affecting the Rheology and Processability of Highly Filled Suspensions Dilhan M. Kalyon and Seda Akta¸s p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 229

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Continuous-Flow Differential Mobility Analysis of Nanoparticles and Biomolecules Richard C. Flagan p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 255


From Stealthy Polymersomes and Filomicelles to “Self ” Peptide-Nanoparticles for Cancer Therapy Nuria ´ Sancho Oltra, Praful Nair, and Dennis E. Discher p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 281 Carbon Capture Simulation Initiative: A Case Study in Multiscale Modeling and New Challenges David C. Miller, Madhava Syamlal, David S. Mebane, Curt Storlie, Debangsu Bhattacharyya, Nikolaos V. Sahinidis, Deb Agarwal, Charles Tong, Stephen E. Zitney, Avik Sarkar, Xin Sun, Sankaran Sundaresan, Emily Ryan, Dave Engel, and Crystal Dale p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 301 Downhole Fluid Analysis and Asphaltene Science for Petroleum Reservoir Evaluation Oliver C. Mullins, Andrew E. Pomerantz, Julian Y. Zuo, and Chengli Dong p p p p p p p p p p 325 Biocatalysts for Natural Product Biosynthesis Nidhi Tibrewal and Yi Tang p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 347 Entangled Polymer Dynamics in Equilibrium and Flow Modeled Through Slip Links Jay D. Schieber and Marat Andreev p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 367 Progress and Challenges in Control of Chemical Processes Jay H. Lee and Jong Min Lee p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 383 Force-Field Parameters from the SAFT-γ Equation of State for Use in Coarse-Grained Molecular Simulations Erich A. Muller ¨ and George Jackson p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 405 Electrochemical Energy Engineering: A New Frontier of Chemical Engineering Innovation Shuang Gu, Bingjun Xu, and Yushan Yan p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 429 A New Toolbox for Assessing Single Cells Konstantinos Tsioris, Alexis J. Torres, Thomas B. Douce, and J. Christopher Love p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 455 Advancing Adsorption and Membrane Separation Processes for the Gigaton Carbon Capture Challenge Jennifer Wilcox, Reza Haghpanah, Erik C. Rupp, Jiajun He, and Kyoungjin Lee p p p p p 479 Toward the Directed Self-Assembly of Engineered Tissues Victor D. Varner and Celeste M. Nelson p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 507 Ionic Liquids in Pharmaceutical Applications I.M. Marrucho, L.C. Branco, and L.P.N. Rebelo p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 527

Contents

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Perspectives on Sustainable Waste Management Marco J. Castaldi p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 547 Experimental and Theoretical Methods in Kinetic Studies of Heterogeneously Catalyzed Reactions Marie-Fran¸coise Reyniers and Guy B. Marin p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 563 Indexes Cumulative Index of Contributing Authors, Volumes 1–5 p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 595 Annu. Rev. Chem. Biomol. Eng. 2014.5:147-179. Downloaded from www.annualreviews.org by Universitat Zurich- Hauptbibliothek Irchel on 07/06/14. For personal use only.

Cumulative Index of Article Titles, Volumes 1–5 p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p p 598 Errata An online log of corrections to Annual Review of Chemical and Biomolecular Engineering articles may be found at http://www.annualreviews.org/errata/chembioeng

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Annual Reviews It’s about time. Your time. It’s time well spent.

New From Annual Reviews:

Annual Review of Statistics and Its Application Volume 1 • Online January 2014 • http://statistics.annualreviews.org


Editor: Stephen E. Fienberg, Carnegie Mellon University

Associate Editors: Nancy Reid, University of Toronto Stephen M. Stigler, University of Chicago The Annual Review of Statistics and Its Application aims to inform statisticians and quantitative methodologists, as well as all scientists and users of statistics about major methodological advances and the computational tools that allow for their implementation. It will include developments in the field of statistics, including theoretical statistical underpinnings of new methodology, as well as developments in specific application domains such as biostatistics and bioinformatics, economics, machine learning, psychology, sociology, and aspects of the physical sciences.

Complimentary online access to the first volume will be available until January 2015. table of contents:

• What Is Statistics? Stephen E. Fienberg • A Systematic Statistical Approach to Evaluating Evidence from Observational Studies, David Madigan, Paul E. Stang, Jesse A. Berlin, Martijn Schuemie, J. Marc Overhage, Marc A. Suchard, Bill Dumouchel, Abraham G. Hartzema, Patrick B. Ryan

• High-Dimensional Statistics with a View Toward Applications in Biology, Peter Bühlmann, Markus Kalisch, Lukas Meier • Next-Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data, Kenneth Lange, Jeanette C. Papp, Janet S. Sinsheimer, Eric M. Sobel

• The Role of Statistics in the Discovery of a Higgs Boson, David A. van Dyk

• Breaking Bad: Two Decades of Life-Course Data Analysis in Criminology, Developmental Psychology, and Beyond, Elena A. Erosheva, Ross L. Matsueda, Donatello Telesca

• Brain Imaging Analysis, F. DuBois Bowman

• Event History Analysis, Niels Keiding

• Statistics and Climate, Peter Guttorp

• Statistical Evaluation of Forensic DNA Profile Evidence, Christopher D. Steele, David J. Balding

• Climate Simulators and Climate Projections, Jonathan Rougier, Michael Goldstein • Probabilistic Forecasting, Tilmann Gneiting, Matthias Katzfuss • Bayesian Computational Tools, Christian P. Robert • Bayesian Computation Via Markov Chain Monte Carlo, Radu V. Craiu, Jeffrey S. Rosenthal • Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models, David M. Blei • Structured Regularizers for High-Dimensional Problems: Statistical and Computational Issues, Martin J. Wainwright

• Using League Table Rankings in Public Policy Formation: Statistical Issues, Harvey Goldstein • Statistical Ecology, Ruth King • Estimating the Number of Species in Microbial Diversity Studies, John Bunge, Amy Willis, Fiona Walsh • Dynamic Treatment Regimes, Bibhas Chakraborty, Susan A. Murphy • Statistics and Related Topics in Single-Molecule Biophysics, Hong Qian, S.C. Kou • Statistics and Quantitative Risk Management for Banking and Insurance, Paul Embrechts, Marius Hofert

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Pharmaceutical supply chain risks: a systematic review.

Optimizing energy for a 'green' vaccine supply chain.

Integrated strategic and tactical biomass-biofuel supply chain optimization.

Hybrid algorithms for fuzzy reverse supply chain network design.

Biological nitrate removal processes from drinking water supply-a review.

Integrating multimodal transport into cellulosic biofuel supply chain design under feedstock seasonality with a case study based on California.

Regionalized LCA-based optimization of building energy supply: method and case study for a Swiss municipality.

The global supply chain. Rethinking the global supply chain. Introduction.

Review of the 2015 Drug Supply Chain Security Act.

A hybrid approach for integrated healthcare cooperative purchasing and supply chain configuration.

Forecasting optimal solar energy supply in Jiangsu Province (China): a systematic approach using hybrid of weather and energy forecast models.

Sustainable supply chain design: a configurational approach.

Impact of forest biomass residues to the energy supply chain on regional air quality.

A Game Theoretic Optimization Method for Energy Efficient Global Connectivity in Hybrid Wireless Sensor Networks.

[Significance of brief interventions in the healthcare supply chain of eating disorders: a narrative review].

Impact of radio-frequency identification (RFID) technologies on the hospital supply chain: a literature review.

Microalgae as sustainable renewable energy feedstock for biofuel production.

A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under e-supply chain environment.

Developing a model for agile supply: an empirical study from Iranian pharmaceutical supply chain.

Rabies vaccine stockpile: fixing the supply chain.

Antiretroviral procurement and supply chain management.

Information flow in the pharmaceutical supply chain.

A reverse supply chain can enhance waste management programmes.

Neuromuscular disorder associated with a defect in mitochondrial energy supply.