Kruber, KaiKaiKruberMiroschnitschenko, AnnaAnnaMiroschnitschenkoSkiborowski, MirkoMirkoSkiborowski2022-08-252022-08-252022-01Computer Aided Chemical Engineering 51 : 1261-1266 (2022-01)http://hdl.handle.net/11420/13485The optimal design of chemical processes is of essential importance for an increased sustainability. However, the resulting non-convex mixed-integer nonlinear programming (MINLP) problems cannot directly be solved to global optimality. Therefore, different alternatives have been proposed, which either build on the application of a simulation-based optimization by means of a metaheuristic or the global optimization of a surrogate model, both requiring extensive simulations. The current work proposes a novel alternative approach for a surrogate-assisted hybrid optimization, which exploits a local deterministic optimization of a full MINLP problem to generate a compact artificial neural network (ANN) model that allows for the direct optimization on a reduced search space. In order to provide a sufficient accuracy of the ANN while targeting the global optimum of the design problem, a tailored mixed adaptive sampling is introduced. Application of the algorithm is illustrated for the optimal design of a distillation-based separation of benzene, toluene, and xylene with different means for energy integration.en1570-7946202212611266artificial neural networksdistillationenergy integrationoptimizationsamplingBook Part10.1016/B978-0-323-95879-0.50211-3Other