Pareto Optimization and Decision Making in Process Design

Model-based process design makes it possible to obtain insight into design alternatives. Since the number of alternatives can be huge and the evaluation criteria to sort out the most relevant alternatives are conflicting, identifying the most favorable ones can be a formidable task. In this work, it is demonstrated that adaptive Pareto optimization techniques can be used to address this task efficiently and reliably. Once a sufficient number of Pareto points that represents the Pareto boundary to a required accuracy is found, an interactive decision support scheme allows to explore correlations and tradeoffs between the different quality criteria. This work describes the introduction and realization of this scheme in industrially relevant contexts. Examples for significant process improvements that were achieved are given. Furthermore, since mathematical optimization requires many model evaluations, the challenge of shaping the model structure such that it meshes with the optimization requirements is addressed. Therefore, structural reformulations of the equilibrium stage model and asymptotic simplifications are proposed. Additionally, a hybrid modeling approach is presented in which physical models are augmented with statistical elements in order to obtain models of entire chemical processes suitable for optimization. Initimately related to data-driven model approaches is the task to estimate reliability of model predictions and, if necessary, to plan new experiments in order to obtain additional data. The optimal design of experiments is put in a multicriteria optimization context as well in this work.