Production cost and supply chain design for advanced biofuels

The U.S. government encourages the development of biofuel industry through policy and financial support since 1978. Though first generation biofuels (mainly bio-based ethanol) expand rapidly between the early 1980s and late 2000s, more attention has turned to second generation biofuels, such as cellulosic biofuels, due to the `food-versus-fuel\u27 debate, and potential impact on land use and climate change caused by the development of first generation biofuel production. Over the last few years, a rich literature has arisen on lignocellulosic crops or crop residues being used as biomass feedstock for second generation biorefineries. In this thesis, two types of assessments on cellulosic biofuel production have been conducted: techno-economic analysis of the fast pyrolysis fractionation pathway and supply chain design for the advanced biofuel production. Firstly, the economic feasibility of a fast pyrolysis fractionation facility is examined. The facility takes lignocellulosic biomass feedstock, goes through the pyrolysis process, recovers pyrolysis oil into different fractions, and upgrades the fractions into two main products: commodity chemicals and liquid transportation fuels. The Internal Rate of Return (IRR) of this production pathway is evaluated to be 8.78%. Secondly, mixed integer linear programming models are used to optimize locations and capacities of distributed fast pyrolysis facilities. The supply chain optimization framework is implemented in a case study of Iowa with the goal of minimizing total annual production cost. Comparisons are carried out to investigate the two choices for the centralized refining facility: outsourced to Louisiana or build a refining facility in Iowa. An extension of the supply chain design model to sequential facility location-allocation analysis is also performed for Iowa, taking budget availability and revised Renewable Fuel Standard (RFS2) goal into consideration. The objective is to maximize the net present value (NPV) of the profits over the next 10 years

Development of an optimization model for biofuel facility size and location and a simulation model for design of a biofuel supply chain

To mitigate greenhouse gas (GHG) emissions and reduce U.S. dependence on imported oil, the United States (U.S.) is pursuing several options to create biofuels from renewable woody biomass (hereafter referred to as “biomass”). Because of the distributed nature of biomass feedstock, the cost and complexity of biomass recovery operations has significant challenges that hinder increased biomass utilization for energy production. To facilitate the exploration of a wide variety of conditions that promise profitable biomass utilization and tapping unused forest residues, it is proposed to develop biofuel supply chain models based on optimization and simulation approaches. The biofuel supply chain is structured around four components: biofuel facility locations and sizes, biomass harvesting/forwarding, transportation, and storage. A Geographic Information System (GIS) based approach is proposed as a first step for selecting potential facility locations for biofuel production from forest biomass based on a set of evaluation criteria, such as accessibility to biomass, railway/road transportation network, water body and workforce. The development of optimization and simulation models is also proposed. The results of the models will be used to determine (1) the number, location, and size of the biofuel facilities, and (2) the amounts of biomass to be transported between the harvesting areas and the biofuel facilities over a 20-year timeframe. The multi-criteria objective is to minimize the weighted sum of the delivered feedstock cost, energy consumption, and GHG emissions simultaneously. Finally, a series of sensitivity analyses will be conducted to identify the sensitivity of the decisions, such as the optimal site selected for the biofuel facility, to changes in influential parameters, such as biomass availability and transportation fuel price. Intellectual Merit The proposed research will facilitate the exploration of a wide variety of conditions that promise profitable biomass utilization in the renewable biofuel industry. The GIS-based facility location analysis considers a series of factors which have not been considered simultaneously in previous research. Location analysis is critical to the financial success of producing biofuel. The modeling of woody biomass supply chains using both optimization and simulation, combing with the GIS-based approach as a precursor, have not been done to date. The optimization and simulation models can help to ensure the economic and environmental viability and sustainability of the entire biofuel supply chain at both the strategic design level and the operational planning level. Broader Impacts The proposed models for biorefineries can be applied to other types of manufacturing or processing operations using biomass. This is because the biomass feedstock supply chain is similar, if not the same, for biorefineries, biomass fired or co-fired power plants, or torrefaction/pelletization operations. Additionally, the research results of this research will continue to be disseminated internationally through publications in journals, such as Biomass and Bioenergy, and Renewable Energy, and presentations at conferences, such as the 2011 Industrial Engineering Research Conference. For example, part of the research work related to biofuel facility identification has been published: Zhang, Johnson and Sutherland [2011] (see Appendix A). There will also be opportunities for the Michigan Tech campus community to learn about the research through the Sustainable Future Institute

Optimization Model for a Thermochemical Biofuels Supply Network Desig n

This research focuses on the supply chain network design of a fast pyrolysis and hydroprocessing production pathway by utilizing corn stover as feedstock to produce gasoline and diesel fuel. A mixed integer linear programming (MILP) model was formulated to optimize fast pyrolysis and hydroprocessing facility locations and capacities to minimize total system cost, including the feedstock collecting costs, capital costs of facilities, and transportation costs. The economic feasibility of building a new biorefinery in Iowa was analyzed based on the optimal supply chain configuration and savings in bio-oil logistic costs to the centralized upgrading facility

Supply chain design under uncertainty for advanced biofuel production based on bio-oil gasification

An advanced biofuels supply chain is proposed to reduce biomass transportation costs and take advantage of the economics of scale for a gasification facility. In this supply chain, biomass is converted to bio-oil at widely distributed small-scale fast pyrolysis plants, and after bio-oil gasification, the syngas is upgraded to transportation fuels at a centralized biorefinery. A two-stage stochastic programming is formulated to maximize biofuel producers\u27 annual profit considering uncertainties in the supply chain for this pathway. The first stage makes the capital investment decisions including the locations and capacities of the decentralized fast pyrolysis plants as well as the centralized biorefinery, while the second stage determines the biomass and biofuels flows. A case study based on Iowa in the U.S. illustrates that it is economically feasible to meet desired demand using corn stover as the biomass feedstock. The results show that the locations of fast pyrolysis plants are sensitive to uncertainties while the capacity levels are insensitive. The stochastic model outperforms the deterministic model in the stochastic environment, especially when there is insufficient biomass. Also, farmers\u27 participation can have a significant impact on the profitability and robustness of this supply chain

Modeling Bioenergy Supply Chains: Feedstocks Pretreatment, Integrated System Design Under Uncertainty

Biofuels have been promoted by governmental policies for reducing fossil fuel dependency and greenhouse gas emissions, as well as facilitating regional economic growth. Comprehensive model analysis is needed to assess the economic and environmental impacts of developing bioenergy production systems. For cellulosic biofuel production and supply in particular, existing studies have not accounted for the inter-dependencies between multiple participating decision makers and simultaneously incorporated uncertainties and risks associated with the linked production systems.This dissertation presents a methodology that incorporates uncertainty element to the existing integrated modeling framework specifically designed for advanced biofuel production systems using dedicated energy crops as feedstock resources. The goal of the framework is to support the bioenergy industry for infrastructure and supply chain development. The framework is flexible to adapt to different topological network structures and decision scopes based on the modeling requirements, such as on capturing the interactions between the agricultural production system and the multi-refinery bioenergy supply chain system with regards to land allocation and crop adoption patterns, which is critical for estimating feedstock supply potentials for the bioenergy industry. The methodology is also particularly designed to incorporate system uncertainties by using stochastic programming models to improve the resilience of the optimized system design.The framework is used to construct model analyses in two case studies. The results of the California biomass supply model estimate that feedstock pretreatment via combined torrefaction and pelletization reduces delivered and transportation cost for long-distance biomass shipment by 5% and 15% respectively. The Pacific Northwest hardwood biofuels application integrates full-scaled supply chain infrastructure optimization with agricultural economic modeling and estimates that bio-jet fuels can be produced at costs between 4 to 5 dollars per gallon, and identifies areas suitable for simultaneously deploying a set of biorefineries using adopted poplar as the dedicated energy crop to produce biomass feedstocks. This application specifically incorporates system uncertainties in the crop market and provides an optimal system design solution with over 17% improvement in expected total profit compared to its corresponding deterministic model

Economic and Environmental Optimization in the Supply of Switchgrass in Tennessee

The low efficiency of collection, storage and transportation in the switchgrass supply chain has hindered the commercialization of a switchgrass-based biofuel industry, even given its ecological and environmental advantages in carbon sequestrate, soil quality, water use, and pollution pressure. Thus, designing a switchgrass-based supply chain balancing both environmental and economic performance is important to expedite the development of the cellulosic biofuel industry to meet the national energy plan. The objectives of this study are to 1) determine economic cost and multiple environmental outcomes in feedstock supply chains and 2) identify the relation between the economic and environmental performances. The first paper considers three objectives: minimization of economic cost, greenhouse gas (GHG) emissions, and soil erosions. The second paper focuses on the relation between economic cost and abated greywater footprint for industrialized supply of cellulosic biofuel in west Tennessee. The improved augmented epsilon method and compromise solution method were applied to high-resolution spatial data to determine the optimal placement of the feedstock supply chains. Results in the first paper indicated that land change into switchgrass production is crucial to both plant-gate cost and environmental impact of feedstock supply. Converting croplands to switchgrass incurred higher opportunity cost from land use change but stored more soil carbon and generated less soil erosion. Tradeoffs in higher feedstock costs with lower GHG emissions and lower soil erosion on the frontier were captured. Soil erosion was found more cost effective criterion than GHG emission in general. The compromise solution location for the conversion facility generated at 63% increase in feedstock cost but improved the environmental impact in lowering 27 % GHG emission and decreasing soil erosion by 70 times lower in the feedstock supply chain compared with cost minimization location. Results in the second paper showed that tradeoff between feedstock costs and greywater footprint was mainly associated with the changes of land use, while ambient water quality condition was also influential to the selection of feedstock production area. The average imputed cost of lowering grey water footprint in the most preferred feedstock supply chain in west Tennessee was $0.94 m-3 [per cubic meter]

Decision making under uncertainties for renewable energy and precision agriculture

In this dissertation, mathematical programming models and statistical analysis tools have been formulated and designed to study the strategic and optimal solutions to allocate the resources and manage the risk for the renewable energy and precision agriculture. The dissertation, which consists of four papers, lies at the interface of optimization, simulation, and statistical analysis, with a focus on decision making under uncertainty for biofuel process design, renewable energy supply chain management and precision agriculture. Bio-oil gasification which integrates fast pyrolysis and gasification processes is a relative new conversion technology and this integrated biofuel production pathway has been promoted to take advantage of economies of scale and logistic efficiency. The design of the supply chain networks, especially under uncertainties, is one of the most important decisions faced by the biofuel industry. In the first paper, we proposed a two-stage stochastic programming framework for the biofuel supply chain optimization problem considering uncertainties, including biomass supply availability, technology advancement, and biofuel market price. The results show that the stochastic factors have significant impacts on the decision on fast pyrolysis plant locations, especially when there is insufficient biomass. Also, farmers\u27 participation can have a significant impact on the profitability and robustness of this supply chain design. Another major challenge faced by the cellulosic biofuel industry is that investors are hesitant to take the risk to construct commercial scale production facilities. Techno- economic analysis (TEA) has been widely adopted to overcome this challenge. The optimal facility locations and capacities as well as the logistic flow decisions for biomass supply and biofuel distribution should be incorporated into techno-economic analysis as well. In the second paper, the author aims to provide a new method that integrated the supply chain design into the techno-economic analysis as well by evaluating the economic feasibility of an integrated pathway on biomass pyrolysis and bio-oil gasification. The results indicate that hybrid fast pyrolysis and bio-oil gasification pathway is more suitable for a decentralized supply chain structure while biomass gasification pathway is more suitable for a single centralized facility supply chain structure. Feeding millions of people throughout the world who face hunger every day is a formidable challenge. Precision agriculture has attracted increasing attention in the community of farmland management. Farmland management involves a sequence of planning and decision-making processes, including seed selection and irrigation schedule. In the third paper, a mixed integer programming optimization model is proposed to provide decision support on seed selection and irrigation water allocation for customized precision farmland management. The results show that significant increase of farmers’ annual profit can be achieved by carefully choosing irrigation schedule and type of seed. The proposed model can also serve as a risk analysis tool for farmers facing seasonal irrigation water limits as well as a quantitative tool to explore the impact of precision agriculture. The effect of limited water on corn grain yield is significant and management decisions are essential to optimize farmers’ profits, particularly under stochastic environment. The fourth paper takes uncertainties such as crop price, irrigation water availability and precipitation amount into consideration. A multi-stage stochastic programming is formulated to evaluate the effects of structure of decision making process on farmers’ income. The case study results indicate multi-stage stochastic programming is a promising way for farmland management under uncertainties and can increase farmers’ income significantly. In order to enhance the data utilization and results interpretation, statistical methods such as Monte-Carlo simulation considering parameter interactions, linear regression analysis, and moment matching method for scenario generation are also applied. The overarching goals of this dissertation is to quantify and manage the uncertainties along the modeling process and provide proper mechanisms that lead to optimal decisions. The outcomes of the research have the potential to accelerate the commercialization of second generation of biofuel and lead to sustainable utilization of water resources. The insights derived from the research contributed to the decision making process under uncertainties

Optimization Models for Biorefinery Supply Chain Network Design under Uncertainty

Biofuels are attracting increasing attention worldwide due to its environmental and economic benefits. The high levels of uncertainty in feedstock yield, market prices, production costs, and many other parameters are among the major challenges in this industry. This challenge has created an ongoing interest on studies considering different aspects of uncertainty in investment decisions of the biofuel industry. The Renewable Fuel Standard (RFS) sets policies and mandates to support the production and consumption of biofuels. However, the uncertainty associated with these policies and regulations of biofuel production and consumption have significant impacts on the biofuel supply chain network. The goal of this research is first to determine the optimal design of supply chain for biofuel refineries in order to maximize the annual profit considering uncertainties in fuel market price, feedstock yield and logistic costs. In order to deal with the stochastic nature of the parameters in the biofuel supply chain, we develop two-stage stochastic programming models in which Conditional Value at Risk (CVaR) is utilized as a risk measure to control the amount of shortage in demand zones. Two different approaches including the expected value and CVaR of the profit are considered as the objective function. This study also aims to investigate the impacts of the governmental policies and mandates on the total profit in the biofuel supply chain design problem. To achieve this goal, the two-stage stochastic programming models are developed in which conditional value at risk is considered as a risk measure to control the shortage of mandate. We apply these models for a case study of the biomass supply chain network in the state of Iowa to demonstrate the applicability and efficiency of the presented models, and assess the results

Multistage Infrastructure System Design: An Integrated Biofuel Supply Chain against Feedstock Seasonality and Uncertainty

A biofuel supply chain consists of various interdependent components from feedstock resources all the way to energy demand sites. This study focuses on the design of an efficient biofuel supply chain system against seasonal variations and uncertainties of feedstock supply in an integrative manner. By integrating planning and operational decisions in a stochastic programming framework, we aim at finding an effective design strategy for biofuel supply chain that is economically viable and hedges well against a wide range of future uncertainties. A solution algorithm based on scenario decomposition is designed to overcome computational challenges involved in large-scale applications. A California case study is implemented to demonstrate the applicability of the proposed methods in evaluating the economic potential, the infrastructure needs, and the risk of wastes-based bioethanol production

Dedicated Energy Crop Supply Chain and Associated Feedstock Transportation Emissions: A Case Study of Tennessee

and Bradly Wilson This study minimizes total cost for single-feedstock supply chains of two dedicated energy crops, perennial switchgrass and biomass sorghum, in Tennessee using a spatial optimization model. Greenhouse gas emissions from the transport of feedstock to the conversion facility were estimated for respective feedstock supply chains. Results show that different demand for land types from two feedstocks and the geographically diverse landscape across the state affect the economics of bioenergy crops supply chains and feedstock transportation emissions. Switchgrass is more suitable than biomass sorghum for biofuel production in Tennessee based on the supply chains cost and feedstock hauling emissions