Integration of Design and Control Using Efficient PSE Approximations

Abstract

A new method for the optimal design and control of chemical processes under process disturbances and parameter uncertainty is presented. The key idea is to back-off from the optimal (dynamically inoperable) steady-state design by solving a series of optimization sub-problems constructed using Power Series Expansion (PSE) functions. The PSE functions estimate the actual constraints and cost functions’ worst-case variability due to disturbances and parameter uncertainty. The proposed method was applied to address the optimal design of a non-isothermal CSTR. The results show that this method has the potential to address the optimal design of dynamic systems at low computational costs.