Kruber, KaiKaiKruberKinau, Siv MagdalenaSiv MagdalenaKinauSkiborowski, MirkoMirkoSkiborowskiFernando Israel Gómez-CastroVicente Rico-Ramírez2025-04-282025-04-282025-04in: Optimization in Chemical Engineering. Edt. Gómez-Castro, Fernando Israel; Rico-Ramírez, Vicente: 305-341 (2025)978-3-11-138338-5978-3-11-138343-9978-3-11-138362-0https://hdl.handle.net/11420/55464Process synthesis and design problems in chemical engineering usually require a solution to complex nonlinear optimization problems with continuous and discrete decision variables. The resulting mixed-integer nonlinear programming problems are particularly hard to solve, and different strategies are frequently applied for their solution. Gradient-based optimization enables fast computations, exploiting local sensitivity information, but is usually limited to local optima for nonconvex problems, whereas derivative-free optimization methods can be linked to available simulation models, with little effort, but also without any guarantee of optimality. Metaheuristics, especially swarm intelligence and population-based algorithms, are frequently applied for simulation-based process optimization, overcoming the lack of gradient information, at the cost of a considerable number of simulations. Another strategy that is receiving increasing interest builds on surrogate models that are first generated based on an initial sampling of process simulations for systematically varied design variables. Tractable surrogate models do provide the necessary sensitivity information that enables efficient gradient-based optimization, while being only an approximation of the original problem. Each strategy has its advantages and limitations, and no single best option is generally favorable for all kinds of problems. Thoughtful combinations of different strategies have the potential to overcome or at least reduce the individual limitations, while simultaneously combining the strengths of the individual methods. The current chapter provides an introduction and overview of such hybrid optimization methodologies, together with some illustrations of their use for applications in chemical engineering. Several case studies, including utility and entrainer selection, illustrate the performance of hybrid optimization methods and indicate the ability to solve even more complex design problems.enTechnology::600: TechnologyBook Part10.1515/9783111383439Book Chapter