Abstract

Abstract The economic evaluation of alternative process configurations is an important step in process development. It should be based on optimization to correctly investigate the potential of different process routes and process variants. In many companies, such design studies are performed using block-oriented flowsheet simulators such as Aspen Plus to utilize the extensive model libraries and the ease of model building. We developed an optimization framework that integrates a process simulator (specifically Aspen Plus) with a memetic algorithm (MA). This MA combines an evolution strategy (ES) with derivative-free (DFO) local search methods. The ES addresses the global optimization of all design variables, whereas the DFO method locally optimizes the continuous sub-problems that arise by fixing the discrete variables. In this work, the performance of the memetic algorithm is evaluated for different local methods, involving different DFO methods or the internal equation-oriented optimization engine of Aspen Plus. We discuss the results and the efficiency of the different local methods.

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