Sustainable decision making for chemical process systems via dimensionality reduction of many objective problems

Abstract

Recent global events and the rise of sustainable investing have made clear that the chemical and energy industry must consider sustainability goals beyond profit maximization to remain competitive. Multiobjective optimization provides an ideal framework for analyzing sustainability tradeoffs, but when four or more objectives are considered, the ability to rigorously solve problems and interpret results is lost. This necessitates an approach to systematically reduce the dimensionality of many objective problems to three or fewer objectives. In this work, an algorithm to group objectives based on their correlating nature a priori to solving the full space problem is proposed. It utilizes community detection on a novel weighted objective correlation graph to identify two or three groups of correlated objectives. Results from three representative case studies demonstrate that objective groupings obtained from this algorithm minimize the amount of tradeoff information lost and outperform intuitive groupings by economics or the environment.