Multistage Optimization for Chemical Process Sustainability Enhancement under Uncertainty

Chemical process sustainability is mainly concerned with energy and material efficiency, productivity and product quality, waste reduction, process safety, heath impact, etc. Due to these multiple factors as well as information and data uncertainty, development of optimal strategies and effective action plans for sustainability enhancement becomes a very challenging task. In this paper, we introduce a mathematical framework for optimal process sustainability performance enhancement. In this framework, we describe four types of optimization problems, which are defined based on decision makers different objectives for sustainability enhancement. In formulation, various economic, environmental, and social concerns and technical feasibilities are taken into account, where data uncertainty is dealt with by interval parameters. Solution search is performed by a genetic algorithm method and a Monte Carlo simulation technique. The methodological applicability is illustrated by a comprehensive case study on biodiesel manufacturing. The introduced methodology should be useful for decision makers to compare optimization methods and select the most suitable for their applications.