Biofuel Supply Chain Research Articles

Farmer characteristics and decision-making: A model for bioenergy crop adoption

The commercial development of biofuels and bioproducts depends on whether renewable biomass feedstock is available while not directly competing with the production of food. Farmers are one of the most important stakeholders in the biofuel supply chain and confront a range of uncertainties while entering the bioenergy market. Their decision-making process is extremely complex and rarely purely rational. Modeling farmer behavior requires considering a wide range of individual-level factors, socio-temporal dynamics, institutional settings, and their interactions. These characteristics make agent-based modeling a suitable framework for evaluating such systems. We developed a model to simulate farmer bioenergy crop adoption behavior across a 50-county study region in Nebraska, Kansas, and Colorado. The analysis considers adoption decisions for two bioenergy feedstocks, crop residues and energy crops. We examine the influence of individual and farm characteristics, market structure, social networks, and media influence on farmer adoption decisions. Our results indicate that different factors can have varied impacts on the speed of adoption for the crop residues and energy crops. Identifying levers that have the most impact on grower adoption can inform the design of interventions both from policy and private sector standpoints with important implications for the future the bioenergy industry.

A study of subsidizing a biofuel supply chain to incentivize the production of advanced biofuel: An equilibrium problem with equilibrium constraints approach

Energy independence and greenhouse gas emissions have led governments to enact regulations to support renewable energy production. The renewable fuel standard mandate issued by the US Environmental Protection Agency (EPA) requires the production of 36 billion gallons of biofuel by 2022, out of which 21 billion gallons should be non-corn based. This paper addresses the problem of how a government should adopt a subsidy policy that benefits all engaging parties and how the subsidy plan can enhance advanced biofuel production and meet the EPA's mandate. We find in the research that, as more refineries are built, the US government needs more subsidies to achieve the goal, and the total social welfare increases as the biofuel market consumer surplus increases from a lower fuel price. We also find that the budget required to meet the US government's goal is higher than what other similar works have estimated. We perform a sensitivity analysis on a few critical industry and market parameters and find that the advanced biofuel technology improvement is the most important factor among the considered parameters for the same percentage change of the parameter values.

Pricing decisions in a decentralized biofuel supply chain with RIN mechanism considering environmental impacts

This study develops pricing models in a decentralized biofuel supply chain focusing on both economic and environmental aspects. Environmental impacts are used as a measure to reflect the environmental objective function calculated based on ReCiPe method. A bi-level multi-objective stackelberg game model considering farmers and biorefineries as followers and the blender as leader is proposed. An ε-constraint method is utilized to convert the multi-objective model to a single-objective one. The bi-level model is then transformed to a solvable integrated model. Finally, a real case study of switchgrass bioethanol is presented to illustrate the performance of the proposed model. Results show that focusing on environmental goals results in the increasement of selling prices and profits of farmers and biorefineries and decreasment of about %7 in economic profit of the blender. Therefore, tradeoff analyses are performed for objective functions leading to 10 Pareto optimal solutions which give managerial insights to the blender. Moreover, sensitivity analyses are provided with respects to price elasticity and final fuel's price and results show logical trends in selling prices.

A detailed MILP formulation for the optimal design of advanced biofuel supply chains

The optimal design of a biomass supply chain is a complex problem, which must take into account multiple interrelated factors (i.e the spatial distribution of the network nodes, the efficient planning of logistics activities, etc.). Mixed Integer Linear Programming has proven to be an effective mathematical tool for the optimization of the design and the management strategy of Advanced Biofuel Supply Chains (ABSC). This work presents a MILP formulation of the economical optimization of ABSC design, comprising the definition of the associated weekly management plan. A general modeling approach is proposed with a network structure comprising two intermediate echelons (storage and conversion facilities) and accounts for train and truck freight transport. The model is declined for the case of a multi-feedstock ABSC for green methanol production tested on the Italian case study. Residual biomass feedstocks considered are woodchips from primary forestry residues, grape pomace, and exhausted olive pomace. The calculated cost of methanol is equal to 418.7 €/t with conversion facility cost accounting for 50% of the fuel cost share while transportation and storage costs for around 15%. When considering only woodchips the price of methanol increases to 433.4 €/t outlining the advantages of multi-feedstock approach.

A mathematical model for optimizing a biofuel supply chain with outsourcing decisions under the carbon trading mechanism

Carbon trading is a market-based mechanism for controlling carbon emissions by providing economic incentives to reduce emissions. In recent years, there has been an increasing interest in modeling supply chain networks under this scheme; however, to date, only a limited number of researchers have investigated the implication of this mechanism for biofuel supply chains. The optimization model presented in this paper examines a trade-off between the cost of trading carbon credits and costs associated with outsourcing of the biomass pretreatment process when carbon emissions exceed the predetermined carbon cap in a biofuel supply chain. To demonstrate the applicability of the model, we analyzed challenges in supplying different sources of biomass to two biorefinery plants and shipping the produced biofuels to multiple demand zones. The results showed that carbon emission reductions have a relatively nonlinear pattern when the carbon credit price increases linearly. Furthermore, we presented significant managerial and policy insights on the impact of different carbon emission caps on total costs and total emissions. Moreover, we analyzed the cost adjustment between trading carbon credits and outsourcing decisions for different carbon cap settings. This paper ends with suggestions for further development of the presented model for future researches.

Life cycle thinking in sustainability assessment of bioenergy systems

Increasing population and affluence have had a direct influence on increasing the energy demand of nations across the globe. Energy from non-renewable fossil resources has associated emissions of greenhouse gases contributing to climate change, a major challenge facing us today. The governments of many countries have tried to address the twin issues of limited local availability of fossil resources and greenhouse gas emissions by promoting the use of bioenergy. Bioenergy is not automatically sustainable unlike popular belief. Assessing its sustainability using a life cycle thinking approach reveals many caveats, not only regarding greenhouse gas emissions but also other environmental impacts that are often ignored. The environmental assessment of palm oil-based biodiesel shows the trade-offs when considering all the life cycle stages of the biofuel supply chain and also when multiple impact categories are considered. The so-called carbon neutrality becomes questionable and other impacts from agriculture arising due to the use of land and agrochemicals are also seen to be very significant. Ignoring these in policymaking could result in serious unintended consequences. Thus, the importance of life cycle thinking in sustainability assessment is illustrated. This will be critical in addressing national needs while also moving towards the United Nations’ Sustainable Development Goals.

Using participatory system dynamics modelling to quantify indirect land use changes of biofuel projects

ABSTRACT The use of biomass to produce biofuels can lead to both direct and indirect Land Use Change (LUC). While the causes underlying LUCs are complex their quantification is a scientific challenge that hinders decision-making. Here we demonstrate the application of participatory modelling in combination with System Dynamics techniques to the analysis of the land-change dynamics associated with biofuel supply chains. The ambition is to provide decision-makers with useful and credible knowledge of direct and indirect LUCs. We illustrate the application of the approach by applying it to a real‐world project for the production of advanced biofuels in Sardinia (Italy). The results show that the land use displacements vary in intensity and persistence depending on the crop management regime applied and the future development of the market of sheep cheese. The results were considered credible by actors with direct knowledge of the ‘real’ system and useful by decision makers .

An Application of GIS-Linked Biofuel Supply Chain Optimization Model for Various Transportation Network Scenarios in Northern Great Plains (NGP), USA

Industrial oilseed and lignocellulosic crops can be produced on marginal lands. These advanced biofuel feedstock crops are critical to increase the production of renewable liquid fuels. However, rural areas with significant, existing amounts of available marginal cropland have spartan infrastructure, and it is costly to transport feedstocks and bioproducts within and from these regions. Therefore, it is important to consider varied feedstock and co-product transportation network options and supply chain optimization. This study applied a supply chain optimization model linked with geographical information system (GIS) developed using mixed-integer linear programming (MILP) for the northern Great Plains (NGP) region. An investigation on the influence of petroleum product pipeline usage and/or new infrastructure development on supply chain optimization was performed, and optimum routes were suggested. Four different multimodal transport routes of oilseed, biodiesel, livestock meal, and the capacities of biodiesel plants were evaluated for cost optimization. Selective rural rail infrastructure redevelopment of abandoned lines and strategically improving connectivity by adding 380 km of line could reduce biofuel transportation costs by up to 38%. Using existing petroleum pipeline in Montana reduced costs of biofuel transport; however, pipelines had little effect within the Dakotas. Railroad construction was the most effective mechanism to reduce transportation costs in both North and South Dakota. Variables such as transloading and construction costs, topographical constraints, and transportation network boundary could be used to further improve the optimization model and results.

A GIS Approach Land Suitability and Availability Analysis of Jatropha Curcas L. Growth in Mexico as a Potential Source for Biodiesel Production

Jatropha curcas L. (JCL) commercial plantations in Mexico, one of the most important JCL origin centers, have failed due to a variety of biological, political and technical factors affecting their productivity. This study explores feasible sites of JCL cultivation as a potential source for biodiesel production in Mexico, given agroclimatic and agroecological considerations. We propose a GIS-based approach for estimating suitable and available lands to grow JCL by integrating an Analytical Hierarchy Process (AHP) in the ArcGIS software. Spatial analysis combined multiple data, different evaluation criteria, three land availability classes (high, medium and low potential) and took into account ecological, ethical, and political restrictions, and considering two scenarios with different restriction levels. Suitability and availability maps were generated using agroclimatic information (climatic, land use/soil, and climate change and extreme weather events risk) together with other socioeconomic factors. Approximately 15.3% of Mexican territory is available for JCL production yielding a biodiesel production of 9.683 Mm3/year. Amelioration of the available land is necessary to improve land selection. GIS-based analysis represents a first approach to establish a successful biodiesel project that avoids, competition with food or feed production, maintains biodiversity conservation, and promotes biofuel supply chain development. This procedure would also be applicable to other energy crops such as oil palm and Ricinus communis.

Modeling and economic optimization of cellulosic biofuel supply chain considering multiple conversion pathways

Cellulosic biofuel is considered a promising alternative to traditional fossil fuels. Currently, the commercialization of cellulosic biofuel is proceeding at a slower pace than expected due to the high cost of the cellulosic biofuel supply chain (CBSC), especially in the manufacturing process. In this paper, a new model for a four-echelon CBSC incorporating 12 conversion pathways is proposed to minimize total supply chain cost by considering multiple time periods, various biomass types and spatial distributions, biomass and biofuel inventories, logistics, and different demand levels. Case studies from the state of Illinois in the U.S. are used to demonstrate the effectiveness of the proposed model. It is manifested that the model can select appropriate conversion pathways for biorefineries given the abundance of local resources to minimize the overall cost. Compared to the two cases adopted a single conversion pathway, the optimal result achieves 14.6% and 4.6% reductions on the unit biofuel cost, respectively. In addition, the results of sensitivity analysis indicate that the biorefinery construction cost and the biofuel throughput are the main economic drivers in the current supply chain design.

Geospatial modelling of environmental and socioeconomic impacts of large-scale production of advanced biofuel

Biofuels have become in many countries an alternative energy source for transportation. This has led to the construction of large-scale biofuel refineries. However, this process is raising concern among local stakeholders where the refineries are being sited. Here, we used geospatial techniques to implement a landscape design approach to address societal questions and local concerns regarding the impacts of introducing large-scale production of advanced biofuel (2G). Spatially-explicit landscape design was used to integrate various components in the biofuel supply chain and understand environmental and socioeconomic effects at various levels. Different techniques and modelling approaches were brought together with active participation of local stakeholders to provide answers to varying questions of community relevance. The application of the approach to the analysis of the case of cellulosic ethanol production from dedicated crops in Sardinia (Italy) shows that the introduction of a large-scale biofuel refinery could lead to positive outcomes at local level in terms of habitat quality, employment, energy production and reduction of GHG emissions. However, negative effects are expected regarding water availability and the production of food and animal feed in the landscape. This form of integrated knowledge about benefits and trade-offs can be used by project developers and policymakers to steer the deployment of biofuel projects to advance high level policy goals while respecting local priorities and concerns.

Novel resilient-sustainable strategies for second-generation biofuel network design considering Neem and Eruca Sativa under hybrid stochastic fuzzy robust approach

Nowadays, concerns about fossil fuel depletion, ecological crisis, and energy security lead to renewable energy generation. The present study provides a multi-objective, multi-period, multi-product integrated sustainable-resilient mixed-integer linear programming model for the optimal design of second-generation biofuel supply chain networks under uncertainty. A novel hybrid stochastic fuzzy-robust approach was utilized to tackle the uncertainty of parameters, and the weighted sum method was applied to deal with the multi-objective problem. Several resiliency measures are introduced to enhance the resiliency of the network and resist against any disruption. Furthermore, strategies to fulfill social impacts are proposed to promote sustainable development of the considered network. Then, a real case study is implemented to investigate the validation of the proposed model. The results indicated that simultaneously considering three measures, including resilience, social impacts, and cost and setting a trade-off between them by decision-makers can boost biofuel supply chain network design in an optimal manner.

Impact of government subsidies on a cellulosic biofuel sector with diverse risk preferences toward feedstock uncertainty

Investment risk and uncertainty about the availability of biomass feedstock hinders the development of a mature cellulosic biofuel sector. The Biomass Crop Assistance Program (BCAP) is a federal program designed to subsidize farmers to establish, produce and deliver biomass feedstock to biorefineries. This study evaluated the impacts of BCAP on the optimal biofuel supply chain decisions considering feedstock yield uncertainty and associated investment risk given diverse risk preferences of the biofuel sector. The expected cost for a risk-neutral biofuel sector was minimized using a two-stage stochastic mixed integer linear program, whereas the Conditional Value-at-Risk of the supply chain was optimized for a risk-averse sector. Ex-ante analysis of a switchgrass-based biofuel sector in west Tennessee indicates BCAP payments could lower expected cost and investment risk for both risk-averse and risk-neutral biofuel sectors. However, the cost saving and risk reduction resulting from BCAP incentives for the risk-averse biofuel sector were higher than the risk-neutral biofuel sector. In addition, BCAP payments may drive more cropland to be converted for switchgrass, which potentially mitigates water-induced soil erosion and reduces greenhouse gas emissions associated with net carbon sequestration, but may also create unintended consequence of competition for land between food and fuel use.

An economic analysis for integrated bi-objective biofuel supply chain design using support vector machine

Using second-generation biomass and biofuel deal with environmental pollution and CO2 emissions. Therefore, this paper design an integrated multi-period bi-objective biofuel supply chain network using support vector machine (SVM) and economic analysis to reduce the cost of generating biofuels and CO2 emissions. The economic analysis consists of three scenarios for supplying biomass. The SVM method specifies the potential place to build the bio-refinery. The next step solves the model with the augmented e-constraint method. Finally, results show that biomass production and imports simultaneously reduce costs by 24.5% compared to the production scenario and 4.3% compared to the import scenario. According to the results obtained, despite the increase in cost, it reduces the amount of CO2 emissions. So, the Pareto solution resulted from the augmented e-constraint method for the problem is determined as one of the most effective techniques to help the decision-makers.

Life cycle environmental and cost evaluation of renewable diesel production

Computer simulations are used to study the production of renewable diesel through the biochemical transformation of biomass sorghum [Sorghum bicolor (L.) Moench] to free fatty acids using a genetically modified strain of Escherichia Coli. We evaluate select environmental and economic metrics using life cycle assessment (LCA) and techno-economic analysis (TEA). The biofuel supply chain includes feedstock production, handling, pretreatment and hydrolysis, fermentation to free fatty acids, saponification of the free fatty acids, wax production on an electrochemical synthesis reactor, and hydrocracking to convert the wax to renewable diesel. The TEA model developed uses experimental data from pretreatment to wax synthesis steps and literature for the conversion of wax to diesel. The TEA model is integrated into a life cycle inventory model to estimate life cycle greenhouse gas and non-renewable energy consumption. Considering both environmental and economic factors we find that the performance of this pathway to produce renewable diesel returns lower product yield, higher cost, and higher GHG and energy impacts compared to biomass-to-ethanol pathways, primarily due to poor process yields (mainly fermentation). This limitation in the metabolic pathway constrains the maximum yield potential of FFA and therefore renewable diesel and other potential co-products from biomass derived sugars.

Strategic Planning of Integrated Biofuel and Petroleum Fuel Supply Chains

Strategic Planning of Integrated Biofuel and Petroleum Fuel Supply Chains

A two-stage stochastic programming model for biofuel supply chain network design with biomass quality implications

Biofuel, an efficient alternative to fossil fuels, has gained considerable attention as a potential source to satisfy energy demands. Biomass collection and distribution typically incur a significant portion of the biofuel production cost. Thus, it is imperative to design a biofuel supply chain network that not only aims to minimize the delivery cost, but also incorporates biomass quality properties that make this raw material so unique yet challenging. This article proposes a novel two-stage stochastic programming model that captures different time- and weather-dependent biomass quality parameters (e.g., the moisture content, ash content, and dry matter loss) and their impact on the overall supply chain design. To efficiently solve this optimization model, we propose a parallelized hybrid decomposition algorithm that combines the sample average approximation with an enhanced progressive hedging algorithm. The proposed mathematical model and solutions are validated with a real-life case study. The numerical experiments reveal that the biomass quality variability impacts the supply chain design by requiring additional depots, and therefore, it increases the capital investment. The storage of unprocessed biomass at depots and biorefineries decreased by 88.5% and 97.9%, respectively, and the densified biomass inventory at biorefineries increased 17-fold when baseline quality considerations were taken into account.

Operational Management Implemented in Biofuel Upstream Supply Chain and Downstream International Trading: Current Issues in Southeast Asia

Bioenergy is one of the alternatives to secure energy demand, despite increasing debate on the sustainability of using bioenergy as a renewable source. As the source is disseminated over a large area and affected by seasonality, the potential benefit is highly dependent on other cost and benefit trade-offs along the supply chain. This review paper aims to assess operational management research methods used in biofuel supply chain planning, including both upstream production and international downstream trading. There have been considerable operational management studies done on upstream processes in biofuel production based on different strategic and tactical decision making of a single or multiple feedstocks, considering economic and environmental factor. However, the environmental consideration is often limited to carbon emission where the other environmental impact such as land-use change, biodiversity loss, irrigation and fertilisation are often being overlooked. Biofuel supply chain and trading at international level remain as an apparent research potential where only limited numbers of global energy models explicitly simulate international bioenergy trade. The leading biofuel producing countries in Southeast Asia: Malaysia, Indonesia and Thailand, are selected as a case study to investigate further on how the supply chain management model could be applied considering the existing biofuel support policies. This study is expected to contribute to the selection of operational management research methods used for decision making under robust policy context, followed by several recommendations.

Random Matrix Generators for Optimizing a Fuzzy Biofuel Supply Chain System

Complex industrial systems often contain various uncertainties. Hence sophisticated fuzzy optimization (metaheuristics) techniques have become commonplace; and are currently indispensable for effective design, maintenance and operations of such systems. Unfortunately, such state-of-the-art techniques suffer several drawbacks when applied to largescale problems. In line of improving the performance of metaheuristics in those, this work proposes the fuzzy random matrix theory (RMT) as an add-on to the cuckoo search (CS) technique for solving the fuzzy large-scale multiobjective (MO) optimization problem; biofuel supply chain. The fuzzy biofuel supply chain problem accounts for uncertainties resulting from fluctuations in the annual electricity generation output of the biomass power plant [kWh/year]. The details of these investigations are presented and analyzed.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.

Distributionally robust chance constrained programming with generative adversarial networks (GANs)

AbstractThis paper presents a novel deep learning based data‐driven optimization method. A novel generative adversarial network (GAN) based data‐driven distributionally robust chance constrained programming framework is proposed. GAN is applied to fully extract distributional information from historical data in a nonparametric and unsupervised way without a priori approximation or assumption. Since GAN utilizes deep neural networks, complicated data distributions and modes can be learned, and it can model uncertainty efficiently and accurately. Distributionally robust chance constrained programming takes into consideration ambiguous probability distributions of uncertain parameters. To tackle the computational challenges, sample average approximation method is adopted, and the required data samples are generated by GAN in an end‐to‐end way through the differentiable networks. The proposed framework is then applied to supply chain optimization under demand uncertainty. The applicability of the proposed approach is illustrated through a county‐level case study of a spatially explicit biofuel supply chain in Illinois.