Interactive Multi-Objective Decision-Support for the Optimization of Nonlinear Dynamic (Bio)Chemical Processes with Uncertainty

The manufacturing industry is faced with the challenge to constantly improve its processes under more and more stringent conditions, e.g., due to more strict environmental policies, lower profit margins and increased societal awareness. These three aspects are considered as the pillars of sustainable development and typically give rise to multiple and conflicting objectives. Hence, any decision taken will require trade-offs to be evaluated and compromises to be made. To support decision making, in this work an interactive multi-objective software framework is presented to systematically optimize nonlinear dynamic systems based on mathematical models. In particular, a numerically efficient strategy to account for parametric uncertainty, based on the Sigma Point method, is introduced allowing directly minimizing the operational risk. Consequently, the proposed software can provide sound decision-support for dynamic process optimization under uncertainty. The framework is tested on a three-objective case study of a fed-batch reactor.