Model Predictive Control of a Nonlinear Large-Scale Process Network Used in the Production of Vinyl Acetate

Abstract

In this work, we focus on the development and application of two Lyapunov-based model predictive control (LMPC) schemes to a large-scale nonlinear chemical process network used in the production of vinyl acetate. The nonlinear dynamic model of the process consists of 179 state variables and 13 control (manipulated) inputs and features a cooled plug-flow reactor, an eight-stage gas-liquid absorber, and both gas and liquid recycle streams. The two control schemes considered are an LMPC scheme which is formulated with a convectional quadratic cost function and a Lyapunov-based economic model predictive control (LEMPC) scheme which is formulated with an economic (non-quadratic) cost measure. The economic cost measure for the entire process network accounts for the reaction selectivity and the product separation quality. In the LMPC and LEMPC control schemes, five inputs, directly affecting the economic cost, are regulated with LMPC/LEMPC and the remaining eight inputs are computed by proportional-integral controllers. Simulations are carried out to study the economic performance of the closed-loop system under LMPC and under LEMPC formulated with the proposed economic measure. A thorough comparison of the two control schemes is provided.