A hierarchical approach to simultaneous design of products and processes

Abstract

Product and process design involve algorithmic and heuristic processing of symbolic and numeric data. Therefore, for such a design task, a hybrid approach that interweaves numerical and heuristic paradigms is warranted. The increasing rigor in modeling along with the necessary knowledge feedback results in a generalized system architecture that forms the basis of this paper. The approach is implemented using KAPPA on a Sun SPARC 5 station. The superstructure developed using the heuristic method is optimized with respect to the choice of technology, operating conditions, the technology sequencing, and the stream flows using Mixed Integer (Non) Linear Programming (MI(N)LP). Product design involves relating product mix and processing conditions to various product characteristics. The multi-objective approach called for this type of problem is addressed through relative weighing of the objectives in the objective function. The lumped parameters used are derived from detailed distributed models using a two-tier approach. The first example used is porous matrix-polymer composite design through impregnation and surface treatment. A second example on catalyst design is also used. The rigorous models utilize a genetic algorithm for search in the discrete variable space. Learning from the rigorous models is used to update the process flowsheets as well as the knowledge bases.