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Blending biomass materials of different physical or chemical properties provides an opportunity to adjust the quality of the feedstock to meet the specifications of the conversion platform. We propose a model which identifies the right mix of biomass to optimize the performance of the thermochemical conversion process at the minimum cost. This is a chance-constraint programming (CCP) model which takes into account the stochastic nature of biomass quality. The proposed CCP model ensures that process requirements, which are impacted by physical and chemical properties of biomass, are met most of the time. We consider two problem settings, a centralized and a decentralized supply chain. We propose a mixed-integer linear program to model the blending problem in the centralized setting and a bilevel program to model the blending problem in the decentralized setting. We use the sample average approximation (SAA) method to approximate the chance constraints, and propose solution algorithms to solve this approximation. We develop a case study for South Carolina using data provided by the Billion Ton Study. Based on our results, the blends identified consist mainly of pine and softwood residues. The cost of the centralized supply chain is 2 to 6% lower, which shows that the assumption of centralized decision making leads to underestimating costs in the supply chain., 33 pages, 2 figures

Inventory management of surgical instruments and material handling decisions of perioperative services are critical to hospitals’ and operating rooms’ (ORs) service levels and costs. However, efficiently coordinating these decisions is challenging due to their interdependence and the uncertainties faced by hospitals. These challenges motivated the development of this study to answer the following research questions: (R1) How does the inventory level of surgical instruments, including owned, borrowed and consigned, impact the service level provided by ORs? (R2): How do material handling activities impact the service level provided by ORs? (R3): How do integrating decisions about inventory and material handling impact the service level provided by ORs? Three discrete event simulation models are developed here to address these questions. Model 1, Current, assumes no coordination of material handling and daily inventory management operations. Model 2, Two Batch, assumes partial coordination, and Model 3, Just-In-Time (JIT), assumes full coordination. These models are verified and validated using real life-data from a partnering hospital. A thorough numerical analysis indicates that, in general, coordination of inventory management of surgical instruments and material handling decisions has the potential to improve the service level provided by ORs. More specifically, a JIT delivery of instruments used in short-duration surgeries leads to lower inventory levels without jeopardizing the service level provided.

Several studies have shown that biomass cofiring is a viable short-term option for coal-fired power plants to reduce their emissions if supported by appropriate tax incentives. These results sugges...

A feasible alternative to the production of fossil fuels is the production of biofuels. In order to minimize the costs of producing biofuels, we developed a stochastic programming formulation that optimizes the inbound delivery of biomass. The proposed model captures the variability in the moisture and ash content in the biomass, which define its quality and affect the cost of biofuel. We propose a novel hub-and-spoke network to take advantage of the economies of scale in transportation and to minimize the effect of poor quality. The first-stage variables are the potential locations of depots and biorefineries, and the necessary unit trains to transport the biomass. The second-stage variables are the flow of biomass between the network nodes and the third-party bioethanol supply. A case study from Texas is presented. The numerical results show that the biomass quality changes the selected depot/biorefinery locations and conversion technology in the optimal network design. The cost due to poor biomass quality accounts for approximately 8.31$$\%$$ of the investment and operational cost. Our proposed L-shaped with connectivity constraints approach outperforms the benchmark L-shaped method in terms of solution quality and computational effort by 0.6$$\%$$ and 91.63$$\%$$ on average, respectively.

We develop a multi-period stochastic optimization framework for identifying operating reserve requirements in power systems with significant penetration of renewable energy resources. Our model captures different types of operating reserves, uncertainty in renewable energy generation and demand, and differences in generator operation time scales. Along with planning for reserve capacity, our model is designed to provide recommendations about base-load generation in a non-anticipative manner, while power network and reserve utilization decisions are made in an adaptive manner. We propose a rolling horizon framework with look-ahead approximation in which the optimization problem can be written as a two-stage stochastic linear program (2-SLP) in each time period. Our 2-SLPs are solved using a sequential sampling method, stochastic decomposition, which has been shown to be effective for power system optimization. Further, as market operations impose strict time requirements for providing dispatch decisions, we propose a warm-starting mechanism to speed up this algorithm. Our experimental results, based on IEEE test systems, establish the value of our stochastic approach when compared both to deterministic rules from the literature and to current practice. The resulting computational improvements demonstrate the applicability of our approach to real power systems.

Generating electricity by co-combusting biomass and coal, known as biomass cofiring, is shown to be an economically attractive option for coal-fired power plants to comply with emission regulations. However, the total carbon footprint of the associated supply chain still needs to be carefully investigated. In this study we propose a stochastic biobjective optimization model to analyze the economic and environmental impacts of biopower supply chains. We use a life cycle assessment approach to derive the emission factors used in the environmental objective function. We use chance constraints to capture the uncertain nature of energy content of biomass feedstocks. We propose a cutting plane algorithm which uses the sample average approximation method to model the chance constraints and finds high confidence feasible solutions. In order to find Pareto optimal solutions we propose a heuristic approach which integrates the $$\epsilon $$ -constraint method with the cutting plane algorithm. We show that the developed approach provides a set of local Pareto optimal solutions with high confidence and reasonable computational time. We develop a case study using data about biomass and coal plants in North and South Carolina. The results indicate that, cofiring of biomass in these states can reduce emissions by up to 8%. Increasing the amount of biomass cofired will not result in lower emissions due to biomass delivery.

In the last century, many infectious diseases have been completely eradicated or significantly reduced because of childhood vaccinations. Ample evidence suggests that low vaccination coverage in developing countries is caused by vaccine stockout and high rates of vaccine wastage. Wastage occurs when a vaccine vial is physically damaged or exposed to extreme temperatures, or when doses from an open vial are discarded after their safe-use time expires. The latter is referred to as open vial wastage (OVW). Clinics can use single-dose vials to reduce OVW; however, such an approach is more expensive than using multi-dose vials. The focus of this research is to develop new policies that support vaccine administration and inventory replenishment. These policies are expected to reduce OVW, reduce the cost of vaccinations, and improve vaccination coverage levels in developing countries. This paper proposes a two-stage stochastic programming model that identifies an optimal combination of differently sized vaccine vials and the corresponding decisions that clinics make about opening vials in the face of uncertain patient arrivals. This work develops a case study with data gathered from Bangladesh. Experimental results indicate that using a combination of vials of different sizes reduces OVW, as opposed to the current practice of using single-sized multi-dose vials. Experimental results also point to simple and economic vaccine administration policies that health care administrators can use to minimize OVW. The model is solved using an extension of the stochastic Benders decomposition algorithm, the L-shaped method (LS). This algorithm uses Gomory mixed integer and mixed-integer rounding cuts to address the problem’s non-convexity. Computational results reveal that the solution approach presented here outperforms the standard LS method.

This paper is motivated by a power system with one main grid (arbiter) and multiple microgrids (MGs) (agents). The MGs are equipped to control their local generation and demands in the presence of uncertain renewable generation and heterogeneous energy management settings. We propose an extension to the classical two-stage stochastic programming model to capture these interactions by modeling the arbiter’s problem as the first-stage master problem and the agent decision problems as second-stage subproblems. To tackle this problem formulation, we propose a sequential sampling-based optimization algorithm that does not require a priori knowledge of probability distribution functions or selection of samples for renewable generation. The subproblems capture the details of different energy management settings employed at the agent MGs to control heating, ventilation, and air conditioning systems, home appliances, industrial production, plug-in electrical vehicles, and storage devices. Our computational experiments conducted on the U.S. western interconnect (WECC-240) data set illustrate that the proposed algorithm is scalable and the solutions are statistically verifiable. Our results also show that the proposed framework can be used as a systematic tool to gauge: 1) the impact of energy management settings in efficiently utilizing renewable generation and 2) the role of flexible demands in reducing system costs.

This research is motivated by the opportunities for retailers to reduce waste and increase profitability of perishables using pricing. This paper proposes a two-stage stochastic optimization model that selects suppliers, identifies a replenishment schedule for a periodic-review inventory system with non-stationary demand and supply, and determines the timing and size of a price markdown in order to maximize retailer’s profits. In this model, the first-stage problem is bilinear since it captures the additive relationship between price and demand. Therefore, we develop a solution approach which extends the Benders decomposition algorithm via a piecewise linear approximation method to solve the first-stage problem. A case study is presented to validate the model. Numerical experiments suggest that supply chain profits are enhanced by integrating inventory management with pricing decisions.

Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US's cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system's reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. The Sample Average Approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory's pilot biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution increase utilization of the reactor and reduce operational costs.

This study extends classic economic lot-sizing problems to permit the replenishment of age-dependent perishable inventories via multiple transportation modes. Inventory replenishment costs include ...

This article examines the impacts of governmental incentives for coal-fired power plants to generate renewable energy via biomass cofiring technology. The most common incentive is the Production Tax Credit (PTC), a flat-rate reimbursement for each unit of renewable energy generated. The work presented here proposes PTC alternatives, incentives that are functions of plant capacity and the biomass cofiring ratio. The capacity-based incentives favor plants of small capacity, whereas the ratio-based incentives favor plants that cofire larger percentages of biomass. Following a resource allocation perspective, this article evaluates the impacts of alternative PTC schemes on biomass utilization and power plants’ profit-earning potentials. The efficiency of these incentive schemes is evaluated by comparing with a reference profit optimization model that finds a distribution of credits that maximizes the total profits in the system. To evaluate the fairness of the proposed schemes, the results of the max–min fairness solution are used as a basis. A realistic case study, developed with data pertaining to the southeastern. United States, suggests how total system costs and efforts to generate renewable energy are impacted by both the existing and proposed incentives. The observations presented in this study provide helpful insights to policymakers in designing effective incentive schemes that promote biomass cofiring.

This study proposes a novel disruption prevention model that considers both long-term expansion decisions and short-term operational decisions to design and manage an electric vehicle charging station network under power demand uncertainty. A non-linear term is introduced into the model to prevent the evolution of excessive temperature on a power line under different exogenous factors (e.g., outside temperature, air velocity). We first linearize the model using extensions of McCormick relaxation technique and then solve using a combined Sample Average Approximation with a Scenario Decomposition algorithm. A real life case study is presented to draw a several key managerial insights. It is observed that the disruption prevention model is able to reduce 16% overall system cost upon a power outage. The results of the analysis help decision-makers achieving a more reliable and cost-effective electricity supply network.

This paper presents a cost-efficient and reliable supply chain network design model for biomass to be delivered to biofuel plants. Biomass is bulky, so transportation modes such as rail and barge can be used to deliver this product. For this reason, this study focuses on multimodal supply chain designs for biofuel. Biomass supply is highly seasonal, but the high production seasons for biomass in the Southeast United States often coincide with or are followed by hurricanes, and drought seasons, both of which impact transportation. The dynamic multimodel transportation network design model this paper presents enables this supply chain to cope with biomass supply fluctuations and to hedge against natural disasters. The mixed-integer nonlinear programming model proposed is an 𝒩𝒫-hard problem, and we develop an accelerated Benders decomposition algorithm and a hybrid rolling horizon algorithm to solve this problem. We tested the performance of the algorithm on a case study using data from the Southeast United States. The numerical experiments show that this proposed algorithm can solve large-scale problem instances to a near optimal solution in a reasonable time. Numerical analyses indicate that, under normal conditions, the minimum cost model outperforms the reliable models. However, under disaster scenarios, the minimum cost model is 2.65% to 9.20% more expensive than the reliable and static model and 6.28% to 17.73% more expensive than the reliable and dynamic model. Thus, the reliable and dynamic multimodal network design decisions can aid biofuel supply chain management decisions, especially when considering the potential impacts of natural disasters.

The production of biofuels using second-generation feedstocks has been recognized as an important alternative source of sustainable energy and its demand is expected to increase due to regulations such as the Renewable Fuel Standard. However, the pathway to biofuel industry maturity faces unique, unaddressed challenges. To address this challenges, this paper presents an optimization model which quantifies and controls the impact of biomass quality variability on supply chain related decisions and technology selection. We propose a two-stage stochastic programming model and associated efficient solution procedures for solving large-scale problems to (1) better represent the random nature of the biomass quality (defined by moisture and ash contents) in supply chain modeling, and (2) assess the impact of these uncertainties on the supply chain design and planning. The proposed model is then applied to a case study in the state of Tennessee. Results show that high moisture and ash contents negatively impact the unit delivery cost since poor biomass quality requires the addition of quality control activities. Experimental results indicate that supply chain cost could increase as much as 27%–31% when biomass quality is poor. We assess the impact of the biomass quality on the topological supply chain. Our case study indicates that biomass quality impacts supply chain costs; thus, it is important to consider the impact of biomass quality in supply chain design and management decisions.

This paper manages congestion in the supply chain via dynamic freight routing and using multi-modal facilities in different time periods of a year. The proposed mixed integer non-linear program (MINLP) model captures the trade-offs that exists between investment, transportation, and congestion management decisions. A linear approximation of the proposed MINLP model is then solved using a hybrid Benders-based rolling horizon algorithm. The performance of the algorithm is tested on a case study that uses data from the Southeast USA biomass supply chain network. Extensive numerical experiments provide managerial insights to manage congestion from the biomass supply chain network.

Co-firing biomass is a strategy that leads to reduced greenhouse gas emissions in coal-fired power plants. Incentives such as the Production Tax Credit (PTC) are designed to help power plants overcome the financial challenges faced during the implementation phase. Decision makers at power plants face two big challenges. The first challenge is identifying whether the benefits from incentives such as PTC can overcome the costs associated with co-firing. The second challenge is identifying the extent to which a plant should co-fire in order to maximize profits. We present a novel mathematical model that integrates production and transportation decisions at power plants. Such a model enables decision makers to evaluate the impacts of co-firing on the system performance and the cost of generating renewable electricity. The model presented is a nonlinear mixed integer program that captures the loss in process efficiencies due to using biomass, a product that has lower heating value as compared with coal...

In this paper we propose a multi-objective, mixed integer linear programming model to design and manage the supply chain for biofuels. This model captures the trade-offs that exist between costs, environmental and social impacts of delivering biofuels. The in-bound supply chain for biofuel plants relies on a hub-and-spoke structure which optimizes transportation costs of biomass. The model proposed optimizes the $$\hbox {CO}_{2}$$ emissions due to transportation-related activities in the supply chain. The model also optimizes the social impact of biofuels. The social impacts are evaluated by the number of jobs created. The multi-objective optimization model is solved using an augmented $$\epsilon $$ -constraint method. The method provides a set of Pareto optimal solutions. We develop a case study using data from the Midwest region of the USA. The numerical analyses estimates the quantity and cost of cellulosic ethanol delivered under different scenarios generated. The insights we provide will help policy makers design policies which encourage and support renewable energy production.

Switchgrass is a perennial herbaceous plant regarded as a biomass energy crop in the United States for its high adaptability and yield potential. Processing and handling of switchgrass particles are challenging due to the erratic mechanical and flow behavior originating from their intrinsic particulate properties. In this work, we present a bonded-sphere discrete element model designed specifically for switchgrass particles. The model simultaneously captures three key particulate features, i.e., fibrous particle shapes, a wide range of particle sizes, and particle deformability. Realistic yet computationally efficient particle shape templates are created based on the image analysis data of switchgrass specimens. A fitting procedure is proposed to ensure both the particle width and length distributions are captured, a unique requirement for fibrous particles. Two full-scale numerical models, i.e., a uniaxial compression model and a Schulze ring shear model, are developed using information from physical experiments. The model is calibrated using experimental data of chopped-small switchgrass specimens, and then, is validated using data of chopped-large specimens in both compression and ring-shear tests. Numerical results show that the numerical models capture bulk densities accurately (with an error of 3%) while slightly underestimate the bulk friction angle. Furthermore, an extensive sensitivity analysis reveals that (1) switchgrass particles with rougher edges (due to different processing techniques) exhibit a higher shear strength and a lower flowability; (2) stiffer particles yield a lower bulk density (up to 21% lower) compared to more deformable particles, indicating particle deformability should be incorporated when modeling biomass flow in a preprocessing system.

This paper addresses a procurement issue facing a polystyrene packaging manufacturer considering its optimal purchasing strategies between two suppliers—one providing virgin material, the other offering recycled material. We model a single-period scenario where each supplier sells product with a known yield distribution at market pricing. The manufacturer must choose whether to sole-source or dual-source, as well as determine how much material to purchase from each supplier to meet deterministic demand. Our results indicate that there is a range of prices from the recycled material supplier where dual-sourcing will lead to higher manufacturer profits compared to sole-sourcing. We show, based on the procurement strategy, the optimal quantities to purchase to maximize manufacturer’s expected profit.

This paper proposes a hybrid policy for a stochastic inventory system facing regular demand and surge demand. The combination of two different demand patterns can be observed in many areas, such as healthcare inventory and humanitarian supply chain management. The surge demand has a lower arrival rate but higher demand volume per arrival. The solution approach proposed in this paper incorporates the level crossing method and mixed integer programming technique to optimize the hybrid inventory policy with both regular orders and emergency orders. The level crossing method is applied to obtain the equilibrium distributions of inventory levels under a given policy. The model is further transformed into a mixed integer program to identify an optimal hybrid policy. A sensitivity analysis is conducted to investigate the impact of parameters on the optimal inventory policy and minimum cost. Numerical results clearly show the benefit of using the proposed hybrid inventory model. The model and solution approach could help healthcare providers or humanitarian logistics providers in managing their emergency supplies in responding to surge demands.

Domestic and industrial sludge generated at wastewater treatment facilities is considered a potential biomass source for producing biodiesel. However, transportation of large amounts of sludge from wastewater treatment facilities to a biorefinery is expensive. The objective of this paper is to identify the proper transportation mode to use as a function of the volume shipped and transportation distances. Currently, sludge is mainly shipped by truck and pipeline. We estimated that the fixed and variable cost components of pipeline transportation for a volume such as 480 m3/day and a distance of 100 miles are $0.116/m3 and $0.089/m3/mile, respectively. We estimated the biomass (sludge) transportation cost per gallon of biodiesel, and observed the changes in these costs as a function of distance traveled and volume shipped. The outcomes of this study have the potential to help biofuel plants make better biomass transportation decisions, and consequently reduce the price of biodiesel significantly.

This study presents mathematical models that capture the impact of different carbon-emission-related policies on the design of the biodiesel supply chain. These mathematical models identify locations and production capacities for biocrude production plants by exploring the trade-offs that exist between transportation costs, facility investments costs, and emissions. The mathematical models capture the dynamics of biomass supply and transportation costs during a predefined time horizon. We analyze the behavior of the chain under different regulatory policies such as carbon cap, carbon tax, carbon cap and trade, and carbon offset mechanisms. A number of observations are made about the impact of each policy on the supply chain performance. The models we developed are solved by using a commercial software GAMS/CPLEX. We use the state of Mississippi as the testing grounds for these models, and employ ArcGIS to visualize and validate the results from the optimization models.

This article presents a Decision Support System (DSS) to aid managers with supply chain (SC) design and logistics management of biomass-for-biofuel production. These tools play a very important role in efficiently managing biomass-for-biofuel SCs and have the potential to reduce the cost of biofuels. The proposed model coordinates the long-term decisions of designing a SC with the medium term decisions of logistics management. This system has the ability to (a) identify locations and capacities for biorefineries, given the availability of biomass and costs; (b) estimate the minimum cost of delivering biofuels, which include transportation, investment, and processing costs; and (c) perform sensitivity analyses with respect to a number of parameters. Visual Basic for Applications (VBA) is used to create the interface of the DSS, and Excel's CPLEX Add-In is used to solve the mathematical models.

This paper presents a two-stage stochastic programming model used to design and manage biodiesel supply chains. This is a mixed-integer linear program and an extension of the classical two-stage stochastic location-transportation model. The proposed model optimizes not only costs but also emissions in the supply chain. The model captures the impact of biomass supply and technology uncertainty on supply chain-related decisions; the tradeoffs that exist between location and transportation decisions; and the tradeoffs between costs and emissions in the supply chain. The objective function and model constraints reflect the impact of different carbon regulatory policies, such as carbon cap, carbon tax, carbon cap-and-trade, and carbon offset mechanisms on supply chain decisions. We solve this problem using algorithms that combine Lagrangian relaxation and L-shaped solution methods, and we develop a case study using data from the state of Mississippi. The results from the computational analysis point to important observations about the impacts of carbon regulatory mechanisms as well as the uncertainties on the performance of biocrude supply chains.

This study presents a mathematical model that designs a reliable multi-modal transportation network for a biofuel supply chain system, where intermodal hubs are subject to site-dependent probabilistic disruptions. The disruption probabilities of intermodal hubs are estimated by using a probabilistic model which is developed using real world data. We developed an accelerated Benders decomposition algorithm to solve this challenging NP-hard problem. Numerical analysis show that the model selects to use intermodal hubs located in areas with low disruption probabilities. In case of a disaster, the reliable solution results in 6.21% savings over the minimum cost solution.

This paper analyzes the impacts of carbon regulatory mechanisms on replenishment decisions in a biofuel supply chain. We employ mathematical models for operations which integrate replenishment and supplier/transportation mode selection decisions. These models explicitly account for carbon emissions that may result from transportation and inventory storage activities. This research is motivated by observations indicating that nearly 19% of the energy consumption and 25% of the energy-related carbon dioxide emissions worldwide arise from transportation. Because freight transportation is expected to continue to grow, we consider the impacts of different carbon regulatory mechanisms on transportation and inventory replenishment decisions in a biofuel supply chain. A set of extensive numerical experiments uses the biofuel supply chain context to analyze the impacts of different regulatory mechanisms, including carbon cap, carbon tax, carbon cap and trade, and carbon offset, on performance. We use existing methodologies to calculate emissions as a function of distance traveled, load weight, and transportation mode used. We also use publicly available data to derive representative biomass transportation costs. As a consequence, our numerical results are meaningful, and give a realistic representation of the relationships between emissions from different transportation modes and the resulting costs. The results of our computational experiments indicate that carbon regulatory mechanisms have a non-trivial impact on replenishment schedules, and as a consequence, costs and emissions in the supply chain.

This article analyzes rail transportation costs of products that have similar physical properties as densified biomass and biofuel. The results of this cost analysis are useful to understand the relationship and quantify the impact of a number of factors on rail transportation costs of denisfied biomass and biofuel. These results will be beneficial and help evaluate the economic feasibility of high-volume and long-haul transportation of biomass and biofuel. High-volume and long-haul rail transportation of biomass is a viable transportation option for biofuel plants, and for coal plants which consider biomass co-firing. Using rail optimizes costs, and optimizes greenhouse gas (GHG) emissions due to transportation. Increasing bioenergy production would consequently result in lower GHG emissions due to displacing fossil fuels. To estimate rail transportation costs we use the carload waybill data, provided by Department of Transportation’s Surface Transportation Board for products such as grain and liquid type commodities for 2009 and 2011. We used regression analysis to quantify the relationship between variable transportation unit cost ($/ton) and car type, shipment size, rail movement type, commodity type, etc. The results indicate that: (a) transportation costs for liquid is $2.26/ton–$5.45/ton higher than grain type commodity; (b) transportation costs in 2011 were $1.68/ton–$5.59/ton higher than 2009; (c) transportation costs for single car shipments are $3.6/ton–$6.68/ton higher than transportation costs for multiple car shipments of grains; (d) transportation costs for multiple car shipments are $8.9/ton and $17.15/ton higher than transportation costs for unit train shipments of grains.

We propose a framework for designing the supply chain network for biomass co-firing in coal-fired power plants. This framework is inspired by existing practices with products with similar physical characteristics to biomass. We present a hub-and-spoke supply chain network design model for long-haul delivery of biomass. This model is a mixed integer linear program solved using benders decomposition algorithm. Numerical analysis indicates that 100 million tons of biomass are located within 75 miles from a coal plant and could be delivered at $8.53/dry-ton; 60 million tons of biomass are located beyond 75 miles and could be delivered at $36/dry-ton.

Using densified biomass to produce biofuels has the potential to reduce the cost of delivering biomass to biorefineries. Densified biomass has physical properties similar to grain, and therefore, the transportation system in support of delivering densified biomass to a biorenery is expected to emulate the current grain transportation system. By analyzing transportation costs for products like grain and woodchips, this paper identifies the main factors that impact the delivery cost of densified biomass and quantifies those factors’ impact on transportation costs. This paper provides a transportation-cost analysis which will aid the design and management of biofuel supply chains. This evaluation is very important because the expensive logistics and transportation costs are one of the major barriers slowing development in this industry. Regression analysis indicates that transportation costs for densified biomass will be impacted by transportation distance, volume shipped, transportation mode used, and shipment destination, just to name a few. Since biomass production is concentrated in the Midwestern United States, a biorefinery’s shipments will probably come from that region. For shipments from the Midwest to the Southeast US, barge transportation, if available, is the least expensive transportation mode. If barge is not available, then unit trains are the least expensive mode for distances longer than 161 km (100 miles). For shipments from the Midwest to the West US, unit trains are the least expensive transportation mode for distances over 338 km (210 miles). For shorter distances, truck is the least expensive transportation mode for densified biomass.

Combined Heat and Power (CHP) production using renewable energy sources is gaining importance because of its flexibility and high-energy efficiency. Biomass materials, such as corn stover and forestry residues, are potential sources for renewable energy for CHP production. In Mississippi, approximately 4.0 MT dry tons of woody biomass is available annually for energy production. In this study, we collected and analyzed 10 years of corn stover data (2001–2010) and three years of forest logging residue data (1995, 1999, and 2002) in each county in Mississippi to determine the potential of these feed stocks for sustainable CHP energy production. We identified six counties, namely Amite, Copiah, Clarke, Wayne, Wilkinson and Rankin, that have forest logging residue feedstocks to sustain a CHP facility with a range of capacity between 8.0 and 9.8 MW. Using corn stover alone, Yazoo and Washington counties can produce 13.4 MW and 13.5 MW of energy, respectively. Considering both feedstocks and based on a conservative amount of 30% available forest logging residue and 33% corn stover, we found that 20 counties have adequate supply for a CHP facility with a capacity of 8.3 MW to 19.6 MW.

The objective of this study is to design and evaluate the performance of the supply chain for biocrude production from activated sewage sludge in wastewater treatment facilities. Experimental results indicate that feeding wastewater activated sludge with high sugar load and establishing a high carbon-to-nitrogen ratio in the wastewater can enhance biocrude yield and quality from the sludge. The biocrude will be further refined and converted to biodiesel. The optimization part, of the optimization-simulation framework that we propose, uses a mixed integer program to identify locations for sugar plants as well as the assignment of wastewater treatment plants to sugar plants and refineries. The objective is to minimize total supply chain related costs. We use the solution from the optimization model (the structure of the supply chain) to build a discrete-event simulation model. The simulation captures the seasonal and random nature of biomass supply. We use a case study that designs the supply chain for biocrude in Mississippi given the availability of lignocellulosic biomass, the locations of harvesting sites, location of wastewater treatment plants, and location of refineries. © 2011 American Institute of Chemical Engineers Environ Prog, 32: 139–147, 2013.