1 Model reduction in chemical process optimization

Abstract

: Chemical processes are often described by heterogeneous models that range from algebraic equations for lumped parameter systems to black-box models for PDE systems. The integration, solution, and optimization of this ensemble of process models is often difficult and computationally expensive. As a result, reduction in the form of reduced-order models and data-driven surrogate models is widely applied in chemical processes. This chapter reviews the development and application of reduced models (RMs) in this area, as well as their integration to process optimization. Special at-tention is given to the construction of reduced models that provide suitable represen-tations of their detailed counterparts, and a novel trust region filter algorithm with reduced models is described that ensures convergence to the optimum with truth models. Two case studies on CO 2 capture are described and optimized with this trust region filter method. These results demonstrate the effectiveness and wide applicability of the trust region approach with reduced models.