A nonlinear industrial model predictive controller using integrated PLS and neural net state-space model

Model predictive control (MPC) technology has been well developed and succe ssfully applied in the refinery and petrochemical process industries over t he last 20 years. Recent development has been focused on nonlinear MPC and robust MPC technologies because new challenges have been encountered in the polymer and chemical industries where many processes show strong nonlinear ity and uncertainty. This paper presents a nonlinear industrial model predi ctive controller, recently developed by Aspen Technology Inc. This MPC cont roller uses a nonlinear, state-space, integrated partial least-squares (PLS ) and neural net model (Zhao, Guiver and Sentoni, American control conferen ce, Philadelphia, PA, USA, 1998), and a multi-step, constrained. Newton-typ e optimization algorithm (Oliveira and Biegler, Automatica, 31 (2) (1995) 2 81-286). It results in a robust and cost-effective industrial nonlinear MPC controller. A pH reactor example and a successful industrial application i n NOx emission control of a power plant are presented to demonstrate the ca pability of this controller. (C) 2001 Elsevier Science Ltd. All rights rese rved.