Morimasa Ogawa

Industrial application of a nonlinear model predictive control to polymerization reactors

A nonlinear model predictive control has been developed and applied to an industrial polypropylene semi-batch reactor and a high density polyethylene continuous stirred tank reactor. The control system consists of two-tier algorithms: at the first tier, an LQI is formulated based on a successively linearized nonlinear first principles process model. At the second tier, actual control actions are determined in consideration of process constraints by solving a QP problem, which is formulated by linearizing the nonlinear model around the LQI trajectory. A simple state estimator, which is capable of providing offset-free estimates of the outputs at steady states, has been designed for each application. In the semi-batch reactor process, the controller was able to maximize the monomer feed while satisfying the heat removal constraint. In the high density polyethylene process, the performance of the grade transition control was greatly improved. r 2001 Elsevier Science Ltd. All rights reserved.

Optimal grade transition control for polymerization reactors

Abstract A nonlinear control system integrating an off-line optimizer and a nonlinear MPC controller is developed to perform optimal grade transition operations at the industrial polyolefin reactors. In this, paper, the details of the optimizer are given: The sequential nonlinear programming is performed in the optimizer by employing control vector parameterization method together with sensitivity analysis. The switching times (i.e. times of chaning in the input actions) can be also optimized by new formulation and a modification in trial functions. The simulation result illustrates the capability of the control system with the proposed optimizer.

Quality inferential control of an industrial high density polyethylene process

Abstract The melt index (MI) is an important quality variable of control in polyolefin processes. However, it is difficult to measure this variable at frequent and regular sample intervals. A practical on-line inferential scheme is proposed in this paper for predicting the MI using secondary on-line measurements. This on-line MI inferential estimation scheme is combined with a quality control system that utilizes a two-degree of freedom cascaded model predictive controller. The proposed system has been successfully evaluated, under regulatory as well as tracking conditions, on an industrial polyolefin operation.

A Nonlinear Predictive Control for Optimal Grade Transition of Polymerization Reactors

Abstract An optimal grade transition control system integrating off-line optimizer and nonlinear model predictive controller is developed for polymerization reactors. The optimal trajectory of inputs and outputs is off-line computed by using first principles model. The nonlinear model predictive controller using the same first principles model together with a linearized state-space model ensures that the real process response does not stray away from the optimal trajectory. The proposed algorithm can provide attractive computational efficiency, nominal stability and good steady-state performance. An application of grade transition control on a slurry polyethylene reactor is presented to illustrate the capability of the algorithm.

Application of Statistical Process Monitoring with External Analysis to an Industrial Monomer Plant

Abstract The main objective of this industry-university collaboration is to develop all on-line process monitoring system that can detect a particular malfunction in an industrial monomer plant, The most serious malfunction is a blockage caused by an accumulation of polymers inside a cooling unit. Since the blockage requires shutdown maintenance, it is crucial to detect its symptom as early as possible and properly adjust the operating condition to avoid further polymer accumulation. The developed on-line monitoring system can detect the symptom of the blockage by using multivariate statistical process control, distinguish it from normal changes in operating conditions by using external analysis. and persuade operators to take appropriate action

Temperature Control of a Highly Active Catalyst Polyolefin Polymerization Reactor at an Unstable Operating Point

Abstract The invention of a new catalyst, Metallocenes, which is highly active, selective and able to control polymer structure more precisely than the ordinary catalyst, leads the polyolefin polymer production to a new catalyst revolution. However, because the catalyst is sensitive to temperature, it pushes the plant to operate at an open-loop unstable point. This paper deals with a design problem of temperature controller for an industrial polypropylene polymerization reactor, which is open-loop unstable when the new catalyst is used. The dynamics of the open-loop unstable process is identified by a first order system with integral and dead time element. Then, the controller is realized in an I-PD structure. The application result illustrates that the developed controller can successfully stabilize the plant.

Retuning PID Temperature Controller for an Unstable Gas-Phase Polyolefin Reactor

Abstract Temperature control for a gas-phase polyolefin reactor is prone to be unstable when operating conditions and/or catalyst are changed. A practical retuning method of PID temperature controller, which can be performed at the onset of closed-loop instability, is developed. The frequency domain analysis using a simple first principles model shows that plant nonlinearity introduces extra phase delay to PID controller and causes closed loop instability. Thus, temperature controller is retuned so as to increase phase margin. Effectiveness of the proposed method is demonstrated through simulations.

Industrial Application of a Nonlinear Model Predictive Control to a Semi-Batch Polymerization Reactor

Abstract A nonlinear model predictive control has been applied to an industrial semi-batch polypropylene reactor where the control objective is to maximize production rate (i.e., monomer feed) while satisfying a capacity constraint on the heat removal. The NMPC is formulated by LQI with the successive linearization of a nonlinear model. The real operation illustrates the capability of the developed system. Some issues specific to the semi-batch process control are also addressed.