A framework for the modeling and optimization of process superstructures under uncertainty

Despite many efforts to formalize the early phase of conceptual process design, no tool-supported systematic method for the conceptual design of chemical processes has been developed to date. This contribution presents a methodology and a software tool for the early conceptual design phase, which is characterized by limited and uncertain information on the available process units. The proposed methodology supports the graphical modeling of hierarchical superstructures which are subsequently optimized by a hybrid evolutionary algorithm considering uncertainty in model parameters.