Temperature control of low density polyethylene (LDPE) tubular reactor using Model Predictive Control (MPC). Issue 4 (January 2020)

• Record Type: Journal Article
• Authors: Muhammad, D; Ahmad, Z; Aziz, N
• Is Part Of: IOP conference series. Volume 736:Issue 4(2020)
• Publication Date: 2020-01
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/736/4/042014
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: Application of advanced process control has become demanding in order to save energy and reduce operating cost, while attaining excellent controllability of the process. This situation can be achieved via better controller action performance compared to conventional control scheme such as PID controller. Model Predictive Control (MPC) has a wide application in the industry due to its ability to handle multivariable control and optimized process performance. In this study, a linear based MPC was implemented to control Low Density Polyethylene (LDPE) tubular reactor temperature. The control of LDPE tubular reactor is challenging due to the complexity of the polymerization process and the nature of the reactor itself. The steady state polymerization reactor model was simulated using Aspen Plus software. The validated steady state reactor model was then exported to Aspen Dynamic software for dynamic simulation. In order to implement online control of the process, the dynamic model was linked with Matlab Simulink environment. The linear model of the process was estimated using State-space model identification technique. Sequential Quadratic programming (SQP) method was adopted by the MPC to calculate the controller action. The performance of MPC was compared to a PID controller. Based on the results, MPC had overcome PID performance in set point tracking and disturbance rejection test despite its low accuracy process model. The linear model low accuracy drawback was compensated by proper tuning of the MPC. In overall, MPC had demonstrated its capability to control the process with optimized control action, which can lead to production cost saving in the long run.