Design Of Biofuel Supply Chain Research Articles

Applications of Machine Learning Technologies for Feedstock Yield Estimation of Ethanol Production

Biofuel has received worldwide attention as one of the most promising renewable energy sources. Particularly, in many countries such as the U.S. and Brazil, first-generation ethanol from corn and sugar cane has been used as automobile fuel after blending with gasoline. Nevertheless, in order to continuously increase the use of biofuels, efforts are needed to reduce the cost of biofuel production and increase its profitability. This can be achieved by increasing the efficiency of a sequential biofuel production process consisting of multiple operations such as feedstock supply, pretreatment, fermentation, distillation, and biofuel transportation. This study aims at investigating methodologies for predicting feedstock yields, which is the earliest step for stable and sustainable biofuel production. Particularly, this study reviews feedstock yield estimation approaches using machine learning technologies that focus on gradually improving estimation accuracy by using big data and computer algorithms from traditional statistical approaches. Given that it is becoming increasingly difficult to stably produce biofuel feedstocks as climate change worsens, research on developing predictive modeling for raw material supply using the latest ML techniques is very important. As a result, this study will help researchers and engineers predict feedstock yields using various machine learning techniques, and contribute to efficient and stable biofuel production and supply chain design based on accurate predictions of feedstocks.

A biofuel supply chain design considering sustainability, uncertainty, and international suppliers and markets

A biofuel supply chain design considering sustainability, uncertainty, and international suppliers and markets

Optimizing the Design of a Biomass-to-Biofuel Supply Chain Network Using a Decentralized Processing Approach

When designing biomass-to-biofuel supply chains, the biomass uncertainty, seasonality and geographical dispersion that affect economic viability need to be considered. This work presents a novel methodology that can optimize the design of biofuel supply chains by adopting a decentralized network structure consisting of a mix of fixed and mobile processing facilities. The model considers a variable biomass yield profile and the mobile fast pyrolysis technology. The mixed-integer linear programming model developed identifies the optimal biofuel production and biomass harvesting schedule schemes under the objective of profit maximization. It was applied in the case study of marginal lands in Scotland, which are assumed to be planted with Miscanthus. The trade-offs observed between economies of scale against the transportation costs, the effect of the relocation costs and the contribution of storage capacity were investigated. The results showed that, in most cases, harvesting is most concentrated during the month of the highest biomass yield, provided that storage facilities are available. Storage capacity plays an important role to widen the operational time window of processing facilities since scenarios with restricted or costly storage resulted in facilities of higher capacity operating within a narrower time window, leading to higher investment costs. Relocation costs proved to have a minor share in the total transportation costs.

Bi-objective stochastic model for the design of biofuel supply chains incorporating risk

The design of a biofuel supply chain network involves multiple uncertainty sources such as the biomass supply and quality. Most of the previous works took a risk-neutral approach by modeling this problem as two-stage stochastic formulation. However, most decision makers are not risk-neutral and a better understanding of the risk involved is germane. This study proposes a bi-objective two-stage stochastic optimization model that considers the expected total cost and the CVaR as objective functions. The first stage determines the location of biorefineries, and the second stage decides the flow between suppliers (counties) and biorefineries. We assume as non deterministic two features of switchgrass (biomass), the ash and moisture contents. To solve this problem, a hybrid method based on a Simulated Annealing algorithm and the augmented ε-constraint method is developed and tested using a realistic large-scale case study in the state of Texas. Based on the experimental results, we observe that the CVaR is not a suitable risk measure when the problem involves a third party because the cost of third-party does not imply risk for the decision makers (biorefinery owner). Therefore, the PVaR model is more suitable for the nature of this problem since PVaR includes the risk inherent to the third party in case this entity is not able to fulfill the demand of biofuel not covered by the supply chain network. As a result, we reformulate the original model by replacing the CVaR by PVaR to create a tailor-made formulation for the bi-objective two-stage stochastic programming problem including third party biofuel suppliers. Compared to existing solution procedures, the proposed hybrid method has merits in terms of computational burden and solution quality. Computational experiments show that the lower the confidence level in the third party (that is, a less reliable supplier needed to meet the bioethanol demand), the higher the expected costs and the PVaR (risk) for the supply chain design.

Integrated spatially explicit landscape and cellulosic biofuel supply chain optimization under biomass yield uncertainty

This paper proposes an integrated stochastic mixed-integer linear programming model for biofuel supply chain and landscape design optimization that considers the interactions between uncertainty in biomass yield, spatially explicit feedstock availability, supply chain configuration, operational decisions, and the system's environmental impact. By modeling crop establishment and fertilization as strategic decisions made before the realization of uncertainty, model solutions identify integrated supply chain configurations better suited to mitigate uncertain biomass yields. Importantly, the paper presents an approach for gathering and processing the large amount of necessary real-world data, including an accurate accounting of soil carbon sequestration. A case study located in Michigan, USA, demonstrating the capabilities of the integrated model with realistic data, is presented. Results at a variety of harvesting site resolutions and number of uncertainty scenarios, show that large-scale instances, at fine spatial resolutions, identify attractive environmental solutions and that solving the stochastic problem leads to an economic benefit.

The energy-water-food-waste-land nexus in a GIS-based biofuel supply chain design: A case study in Fars province, Iran

The concentration on producing clean energy such as biofuels in recent years has had an impact on some of the natural resources that humans use, such as water, food, and land. In this paper, a two-phase approach to design and optimize a biofuel supply chain based on Jatropha Curcas L. oil and used cooking oil (UCO) is presented by considering the nexus between energy, water, food, waste, and land (EWFWL) in order to protect natural resources and create environmental security. In the first phase, the most appropriate sites for Jatropha cultivation are determined by considering the accessibility, geological, environmental and climatic criteria, using the Geographic Information System (GIS). In the second phase, a novel multi-objective model is designed considering the EWFWL nexus and solved using the Torabi-Hassini method. The model is validated using real data from Fars province, Iran, as well as a sensitivity analysis. In this study, some of main contributions are identifying suitable Jatropha cultivation sites in Fars province, Iran; development an innovative optimization model regarding EWFWL nexus; and considering financial parameters and global distribution along with EWFWL nexus. According to the results, the sale of biofuel to local and foreign consumers accounts for the largest proportion of network revenues, contributing for 59.25% of total revenue. Furthermore, increasing the use of UCOs for biofuel production will reduce Jatropha output, and consequently reduce water, land use, and increase food security. If UCO shortage occurs, more lands will be dedicated to Jatropha farms and food-loss and water consumption increase. By focusing on biofuel production over UCO, policymakers can produce less Jatropha and decrease the use of land for Jatropha cultivation. Furthermore, effluent-based irrigation can be more widely used to save fresh water by irrigating Jatropha farms.

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.

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.

Three-stage design of high-resolution microalgae-based biofuel supply chain using geographic information system

This research suggests a three-stage model framework for the design of a microalgae-based biofuel supply chain to meet the goal of economic commercialization. First, the design stage decides the spatial layouts and dimensions of each scale of biorefineries and economic analyses are done to estimate the capital costs and operating costs for different design options. Using the spatial dimensions determined in the first stage, the second stage selects the candidate locations for the biorefineries using an geographic information system (GIS) based site evaluation. This stage screens the available land area for the biorefineries, and also reduces the computational burden of the latter stage. In the mathematical optimization stage, a mixed-integer linear programming optimization model is formulated to make multi-period strategic and tactical decisions of the supply chain under the total cost minimization objective. Since the formulated problem is computationally intensive, a two-stage decomposition solution strategy is proposed to solve the problem in a reasonable time. The model framework is demonstrated through a case study cast in Texas, U.S., with a time horizon of ten years, using the suggested decomposition method. As a result, the minimum fuel selling price (MFSP) of microalgae-based biodiesel is calculated as $10.92/galbiodiesel, which is about three times higher than the current biodiesel price $3.51/galbiodiesel. To reduce the cost, the strategies of biomass storage and maximum delivery are investigated to deal with the productivity fluctuation. In the scenario analysis, the underutilization of production capacities is alleviated resulting in reduced MFSP of $7.90/galbiodiesel, $7.92/galbiodiesel, and $7.43/galbiodiesel respectively. Clearly, for economic feasibility, the production cost should be further reduced by developing more cost-efficient technologies and integrating high-value coproducts into the biorefinery portfolio.

Hybrid Multiobjective Robust Possibilistic Programming Approach to a Sustainable Bioethanol Supply Chain Network Design

This work proposes a multiobjective robust possibilistic programming (MORPP) model for the design and planning of a multiperiod multifeedstock lignocellulosic biofuel supply chain (LBSC) network under the epistemic uncertainty of the input data. The objective simultaneously considers economic, environmental, and social aspects. The proposed model determines the optimal location, capacity, and conversion technology of biorefineries, appropriate transportation modes, material flow, and production planning. In order to deal with the inherent uncertainty of input parameters, a hybrid robust possibilistic programming (HRPP) approach is applied. A real case study located in Iran is conducted to demonstrate the performance of the model. On the basis of the DMs’ preferences HRPP-I(c) version of the robust model is recognized as the most suitable one. Also, a number of scenarios are defined, and some valuable managerial implications are drawn.

Modeling framework and computational algorithm for hedging against uncertainty in sustainable supply chain design using functional-unit-based life cycle optimization

In this work, we address the life cycle economic and environmental optimization of a supply chain network considering both design and operational decisions under uncertainty. A modeling framework is proposed that integrates the functional-unit-based life cycle optimization methodology and the two-stage stochastic programming approach for sustainable supply chain optimization under uncertainty. We develop a stochastic mixed-integer linear fractional programming (SMILFP) model to tackle multiple uncertainties regarding feedstock supply and product demand. To address the computational challenge of solving the resulting large-scale SMILFP problems, an efficient solution algorithm is developed that takes advantage of the efficiency of parametric algorithm and the decomposition-based multi-cut L-shaped method. We present a case study based on a spatially explicit model for the optimal design and operations of a county-level hydrocarbon biofuel supply chain in Illinois to demonstrate the applicability of the proposed modeling framework and the efficiency of the solution algorithm.

Pb-304 自動車用バイオフュエルのサプライチェーンの最適設計(3.導入・システム,ポスターセッション2)

Pb-304 自動車用バイオフュエルのサプライチェーンの最適設計(3.導入・システム,ポスターセッション2)

A robust programming approach towards design and optimization of microalgae-based biofuel supply chain

Despite their inherent attractive characteristics, microalgae do not play a significant role in the green energy market because of high production cost. In order to propel this nascent industry one step further towards commercial viability, the present study expands the narrow outlook of previous studies which is restricted to a single location into a perspective with a nationwide network by proposing a sustainable supply chain model capable of revealing opportunities and limitations of algae industry’s future. The proposed model is formulated based on batch and continuous production systems which completely cover the possible future configuration of microalgae-based biofuel supply chain and also offers managerial implications contributing to cost mitigation. Moreover, the uncertainty whose importance is accentuated in the case of algae production due to lack of real-world implementation is captured by the utilization of robust optimization under two norms of the uncertain data vector. A real case study is carried out to explore the applicability of the proposed model. Furthermore, robust sensitivity analysis technique is performed, allowing decision makers to analyze the effect of uncertain parameters on production cost based on their confidence levels.

Optimal design of supply chains for second generation biofuels incorporating European biofuel regulations

Expected changes in European biofuel regulations lead to uncertainty in supply chain planning for second generation biofuel industries. Previous studies used optimization to design biofuels' supply chains and a few of them analyzed the impact of selected biofuel policy instruments on sustainability targets. This study presents a mixed integer programming model for the optimal design of second generation biofuel supply chains considering all policy instruments of European biofuel regulations. The model maximizes the net present value of second generation biofuel supply chains considering time-dependent quotas for biofuel market shares and greenhouse gas emission savings. The model includes multiple counting of second generation biofuel towards meeting biofuel market share targets and captures all greenhouse gas emissions of the second generation biofuel supply chain including land use change emissions. We demonstrated the use of the model through the case study of second generation biodiesel in a German region, and evaluated the sensitivity of the optimal solution to model parameters and policy instruments' levels. A set of remarks were made regarding the impact of policy instruments on the performance of second generation biodiesel supply chain under study. Future work entails the application of the analysis to the aggregate level in the European Union.

Design of biofuel supply chains with variable regional depot and biorefinery locations

We propose a multi-period mixed-integer linear programming (MILP) model for the design and operational planning of cellulosic biofuel supply chains. Specifically, the proposed MILP model accounts for biomass selection and allocation, technology selection and capacity planning at regional depots and biorefineries. Importantly, it considers the location of regional depots and biorefineries as continuous optimization decisions. We introduce approximation and reformulation methods for the calculation of the shipments and transportation distance in order to obtain a linear model. We illustrate the applicability of the proposed methods using two medium-scale examples with realistic data.

Life cycle energy and CO2 emission optimization for biofuel supply chain planning under uncertainties

The purpose of this paper is to develop a model for the decision-makers/stakeholders to design biofuel supply chain under uncertainties. Life cycle energy and CO2 emission of biofuel supply chain are employed as the objective functions, multiple feedstocks, multiple transportation modes, multiple sites for building biofuel plants, multiple technologies for biofuel production, and multiple markets for biofuel distribution are considered, and the amount of feedstocks in agricultural system, transportation capacities, yields of crops, and market demands are considered as uncertainty variables in this study. A bi-objective interval mix integer programming model has been developed for biofuel supply chain design under uncertainties, and the bio-objective interval programming method has been developed to solve this model. An illustrative case of a multiple-feedstock-bioethanol system has been studied by the proposed method, and the results show that the proposed model can help decision-makers/stakeholders plan and design the biofuel supply chain by proposing feasible solutions to them.

Integrating multimodal transport into forest-delivered biofuel supply chain design

An integrated multi-stage, mixed integer programming (MIP) model using multimodal transport was designed for a forest biomass biofuel supply chain to manage logistics. The two transport modes are rail and truck. The objective was to minimize the total cost for infrastructure, feedstock procurement harvest, transport, storage and process. The model coordinated strategic and tactical decisions. Strategic decisions include the number, capacity, and location of storage yards and biorefineries. Tactical decisions included the amount of biomass shipped, processed and inventoried during a time period. The model was validated using the state of Michigan, in the Midwest United States, as the base case. It was uncovered that trucks are preferred over rail for short-haul deliveries while rails are more effective for long-haul transport. Taking advantage of these benefits, the multimodal transport model provided more cost effective solutions.

Biofuel supply chain design from Coffee Cut Stem under environmental analysis

The fossil fuel consumption is still a global concern that has received great deal of attention from Chemical Engineering and Operation Research fields. In this sense, this paper presents a SCND (Supply Chain Network Design) for second-generation biofuel production. For this aim, we proposed a mathematical model that includes, simultaneously, economic and environmental issues. The model feasibility has been tested in Colombia. In this case study, the bioethanol production from Coffee Cut Stem (Coffee-CS) is examined considering the three main echelons of the supply chain. Experimental results showed that Coffee-CS is a profitable and sustainable feedstock for biofuel production.

Enhanced models and improved solution for competitive biofuel supply chain design under land use constraints

This paper studies the effect of government regulation on farmland use to balance food and energy production in a competitive biofuel supply chain design framework. We propose a Stackelberg–Nash game model with a direct land-use constraint that captures farmland, food, and fuel market equilibrium. To provide farmers with incentives to comply with the land-use regulation, we implement the land-use constraint through a cap-and-trade mechanism which we show attains equivalent land-use patterns. We further prove the existence of optimal solutions of the two equivalent discretely constrained mathematical program with equilibrium constraints (DC-MPEC) models. Two adaptive “relax-and-tighten” schemes, integrality and Lagrangian duality based relaxation, are proposed to handle the binary variables in a mixed integer formulation of the models. The proposed methodology is tested in a case study for the State of Illinois. The computational results demonstrate the superiority of our customized algorithms to publicly available solvers for solving problems with realistic sizes. Finally, the efficiency of the cap-and-trade mechanism is demonstrated through comparisons of scenarios with and without regulation as well as a sensitivity analysis on the cap-and-trade level. The cap-and-trade policy is found to effectively reduce biomass farmland use and profits of the biofuel industry. It may have different impacts on social welfare depending on problem settings and market parameters. Cap-and-trade levels also affect system behavior and thus should be carefully selected.

Design of Sustainable Biofuel Processes and Supply Chains: Challenges and Opportunities

The current methodological approach for developing sustainable biofuel processes and supply chains is flawed. Life cycle principles are often retrospectively incorporated in the design phase resulting in incremental environmental improvement rather than selection of fuel pathways that minimize environmental impacts across the life cycle. Further, designing sustainable biofuel supply chains requires joint consideration of economic, environmental, and social factors that span multiple spatial and temporal scales. However, traditional life cycle assessment (LCA) ignores economic aspects and the role of ecological goods and services in supply chains, and hence is limited in its ability for guiding decision-making among alternatives—often resulting in sub-optimal solutions. Simultaneously incorporating economic and environment objectives in the design and optimization of emerging biofuel supply chains requires a radical new paradigm. This work discusses key research opportunities and challenges in the design of emerging biofuel supply chains and provides a high-level overview of the current “state of the art” in environmental sustainability assessment of biofuel production. Additionally, a bibliometric analysis of over 20,000 biofuel research articles from 2000-to-present is performed to identify active topical areas of research in the biofuel literature, quantify the relative strength of connections between various biofuels research domains, and determine any potential research gaps.