In industrial practice, conceptual process design is typically conducted by repetitive simulation studies, which require detailed design specifications in an early design phase. Guided by heuristics, these iterative solution procedures result in high manual effort and, in addition, no guarantee concerning the quality of the solution can be given. Optimization-based design methods provide a tremendous potential to accelerate and improve conceptual process design. For this purpose, a synthesis framework for the optimization-based design of chemical processes is presented in this thesis. Powerful shortcut and rigorous evaluation methods for reaction and distillation are presented. These methods are computationally efficient in order to allow an optimization-based design of large-scale chemical processes. Various industrial case studies illustrate the application of the novel approaches and highlight their benefits.
