Economic optimization for a dual-feedstock lignocellulosic-based sustainable biofuel supply chain considering greenhouse gas emission and soil carbon stock

Environmental factors, including greenhouse gas (GHG) emissions and soil organic carbon (SOC), should be considered when building a sustainable biofuel supply chain. This work developed a three-step optimization approach integrating a geographical information system-based mixed-integer linear programming model to economically optimize the biofuel supply chain on the premise of meeting certain GHG emission criteria. The biomass supply grid cell was considered first, based on a maximum level of GHG emissions, prior to economic optimization. The optimization simultaneously considered dual-feedstock sourcing, selection between distributed and centralized configurations, and the impact of maintaining SOC balance in agricultural soil on biomass availability. The applicability of the modeling approach was demonstrated through a case study that optimized a dual-feedstock renewable jet fuel supply chain via a gasification-Fischer–Tropsch (gasification-FT) conversion pathway in 2050 under three biomass availability scenarios. The case study results show that the differences in procurement costs and GHG emissions between energy crops and agricultural residues have a large impact on the layout of the supply chain. The supply-chain configuration tends to be more centralized with large-scale biorefineries when a supply region has an intensive and centralized distribution of biomass resources. The cost-supply curves demonstrated the technical potential of biofuels that could be obtained at a certain level of cost. Additionally, sensitivity analysis shows that the GHG emission credit from producing extra electricity during the gasification-FT process will be significantly reduced with a rising share of renewable electricity generation in the future

How a Pareto frontier complements scenario projections in land use change impact assessment

To evaluate the sustainability of potential agricultural land developments, scenario projections with land use change models are often combined with environmental impact assessments. Although this allows inter-scenario comparison of impacts, it does not permit interpretation of scenarios in the light of theoretically optimal impacts. A Pareto frontier provides this information. We demonstrate this for ethanol production in Goiás, Brazil, in 2030. For a Business-as-Usual scenario projection, the spatial configuration, production costs, and GHG emissions of the production chain are compared with those obtained from spatial optimization and summarized by the Pareto frontier. Projected production costs are 729 $/m3 ethanol, with GHG emissions of 40 kg CO2-eq/m3 ethanol. The Pareto frontier indicates an improvement potential of ∼50$/m3 ethanol when keeping emissions fixed, or ∼250 kg CO2-eq/m3 ethanol when keeping costs fixed. Robust locations having low costs and emissions show where and how improvements are reached, offering instruments for policy (re)design

Economic optimization for a dual-feedstock lignocellulosic-based sustainable biofuel supply chain considering greenhouse gas emission and soil carbon stock

Environmental factors, including greenhouse gas (GHG) emissions and soil organic carbon (SOC), should be considered when building a sustainable biofuel supply chain. This work developed a three-step optimization approach integrating a geographical information system-based mixed-integer linear programming model to economically optimize the biofuel supply chain on the premise of meeting certain GHG emission criteria. The biomass supply grid cell was considered first, based on a maximum level of GHG emissions, prior to economic optimization. The optimization simultaneously considered dual-feedstock sourcing, selection between distributed and centralized configurations, and the impact of maintaining SOC balance in agricultural soil on biomass availability. The applicability of the modeling approach was demonstrated through a case study that optimized a dual-feedstock renewable jet fuel supply chain via a gasification-Fischer–Tropsch (gasification-FT) conversion pathway in 2050 under three biomass availability scenarios. The case study results show that the differences in procurement costs and GHG emissions between energy crops and agricultural residues have a large impact on the layout of the supply chain. The supply-chain configuration tends to be more centralized with large-scale biorefineries when a supply region has an intensive and centralized distribution of biomass resources. The cost-supply curves demonstrated the technical potential of biofuels that could be obtained at a certain level of cost. Additionally, sensitivity analysis shows that the GHG emission credit from producing extra electricity during the gasification-FT process will be significantly reduced with a rising share of renewable electricity generation in the future

Introductory Overview: Optimization using Evolutionary Algorithms and other Metaheuristics

Environmental models are used extensively to evaluate the effectiveness of a range of design, planning, operational, management and policy options. However, the number of options that can be evaluated manually is generally limited, making it difficult to identify the most suitable options to consider in decision-making processes. By linking environmental models with evolutionary and other metaheuristic optimization algorithms, the decision options that make best use of scarce resources, achieve the best environmental outcomes for a given budget or provide the best trade-offs between competing objectives can be identified. This Introductory Overview presents reasons for embedding formal optimization approaches in environmental decision-making processes, details how environmental problems are formulated as optimization problems and outlines how single- and multi-objective optimization approaches find good solutions to environmental problems. Practical guidance and potential challenges are also provided.</p