Metodi numerici avanzati per il controllo e l’ottimizzazione multi-obiettivo nell’industria (bio)chimica

Advanced model based techniques for computer aided decision making proved their value in the last decades, leading to increased safety and production, and lowered energy consumption. These different goals are often in conflict with each other, and most likely they are incommensurable. Hence, our research explores the advantages of exploiting a multi-objective approach to generate a set of optimal alternatives (Pareto set). However, the currently used multi-objective optimization methods often inefficiently generate Pareto optimal alternatives (e.g., the Weighted Sum), or are computationally expensive. Moreover, the complex and dynamic nature of (bio)chemical processes typically gives rise to large-scale nonlinear dynamic optimization problems, while safety requirements rise the question for robust optimization solutions, which statistically guarantee the satisfaction of constraints despite possible model uncertainty. Case studies are presented that illustrate the huge potential of such an approach for optimal control problems in the (bio)chemical industry.