Consequential Life Cycle Optimization: General Conceptual Framework and Application to Algal Renewable Diesel Production

Life cycle optimization (LCO) enables static life cycle analysis (LCA) and techno-economic analysis to be performed dynamically for automatic generation and optimization of process alternatives. Existing LCO models are developed following an attributional LCA approach, which overlooks the environmental consequences in response to the changes in the market. In this study, we develop a consequential LCO framework that simultaneously optimizes consequential environmental impacts and economic performance. We propose a general system boundary that encloses processes linked by markets. On the basis of the general system boundary, we develop a multiobjective optimization model, which integrates process models and market models with the tenets of consequential LCA and techno-economic analysis methodologies. To efficiently solve the resulting nonconvex mixed-integer nonlinear programming problem, a global optimization algorithm is proposed to integrate the inexact parametric algorithm and the branch-and-refine algorithm. The application of the proposed framework is illustrated through a case study of producing renewable diesel from microalgae. We conduct detailed market analysis to identify the consequences associated with the renewable diesel production process. The environmental impacts of the optimal process designs based on the proposed consequential LCO framework are significantly lower than those based on the existing attributional LCO framework.