An integrated approach to fast model-based process design: Integrating superstructure optimization under uncertainties and optimal design of experiments

Abstract

During the early-stage design of chemical production processes many decisions have to be made on the basis of incomplete knowledge about the underlying chemical and physical phenomena. Therefore, optimization-based approaches are often applied only in a later stage when more knowledge has been generated. In this work, an integrated approach to fast, model and optimization based process design for the selection of reactors and separation networks is discussed. The approach is based on superstructure optimization under uncertainties about the parameters of the models using a two-stage scenario-based approach. Usually, due to the uncertainties, structural decisions cannot be made in the initial stage as different structures are superior for different scenarios of the uncertain parameters. In order to arrive at such decisions, the models need to be refined based on experimental studies. We combine design of experiments with optimization under uncertainty to optimize the experiments such that the information obtained about those parameters that are critical with respect to taking structural design decisions is maximized. We apply the methodology to the development of a process for the hydroaminomethylation of 1-decene in a thermomorphic solvent system. It is shown that the integrated approach can help to significantly reduce the number of required experiments. Using the integrated approach, only 7 experiments need to be performed while more than 16 experiments following a full-factorial experimental design do not provide the same reduction of the design space.