Analysis of cost-environmental trade-offs in biodiesel production incorporating waste feedstocks: A multi-objective programming approach. (10th April 2019) - Caldeira, Carla; Freire, Fausto; Olivetti, Elsa A.; Kirchain, Randolph; Dias, Luis C.
Abstract: Decision-makers in government and industry must develop policy and strategy for highly complex systems, trading off competing objectives such as environmental and economic impact. These trade-offs can be difficult to analyze, which may lead to misinformed choices. There is lack of decision support tools that both include multiple objectives and facilitate communication to decision-makers in a comprehensive and simple way. To address this gap, a mathematical model that facilitates the decision process by allowing an agent to decide based on an explicit overall economic and environmental performance but simultaneously visualize graphically the trade-offs among the different objectives was developed. This model was used to assess the trade-offs of using waste-based feedstocks in blends with conventional feedstocks for biodiesel production, and explore opportunities to improve biodiesel cost effectiveness whilst managing environmental impacts, particularly in the feedstock selection process. The compositional uncertainty of the feedstocks is considered in the model ensuring that the final quality of the biodiesel is not compromised by the high uncertainty associated with the composition of waste materials. Reductions on production costs (3%) and on environmental impacts (from 2% to 32%) were obtained using this model to select the blend composition. The model was shown to be useful to inform decision-making by allowing comprehensive, simplified visualization of the trade-offs among cost and environmental impacts. The model can be used to support biodiesel production planning with lower environmental impacts. Highlights: Blends for biodiesel production using waste-based feedstocks are optimized. Trade-offs between cost and environmental impacts are analyzed. Compositional uncertainty of the feedstocks is addressed. Blends optimization allows a reduction in cost and environmental impacts. The model allows a simplified visualization of the trade-offs among the objectives.