Many Objective Optimization Tools for Sustainable Decision‐Making

Sustainable decision making for chemical process systems is inherently a problem with many, often competing objectives. Successful generation and interpretation of tradeoffs is necessary for identifying a sustainable compromise solution; however, achieving this for problems of four or more objectives is challenging. In this chapter, we review the most commonly used methods for solving multi-objective optimization problems. Then, we introduce a network-theoretic method for systematically reducing the dimensionality of a many-objective optimization problem to one of two or three objectives, while preserving maximum information about the most informative tradeoffs. We demonstrate how this approach can be used in practical sustainable decision making problems through two case studies. In the first, we assess the correlating or competing nature of cost versus emissions driven demand response in green ammonia production. In the second we analyze a hydrogen supply chain and show how planetary boundary objectives can be intelligently grouped together to give an interpretable tradeoff space.