Low‐density polyethylene tubular reactor control using neural Wiener model predictive control

Abstract

Low-density polyethylene (LDPE) is a valued commodity plastic with versatile applications. However, controlling the LDPE polymerization reactor is a challenging task due to its nonlinear behavior and multivariable process and operates in a board operating region. This work aims to develop and explore the neural Wiener MPC (NWMPC) application in controlling the LDPE tubular reactor process. The motivation for NWMPC development originates from its advantage in terms of lower development effort, time, resource, and computational costs compared to nonlinear MPC (NMPC) using the first principle model (FPM). In order to develop the LDPE tubular reactor model, the Aspen Plus and Aspen Dynamic software are used to simulate the process in steady-state and dynamic form. The neural Wiener (NW) model identification is performed using state space and neural network model identification using Matlab software. The identification result shows the ability of the NW model to identify the nonlinear LDPE tubular reactor process successfully. Furthermore, the NWMPC has outperformed the state space MPC (SSMPC) in controlling grade transition, feed pressure loss, feed impurity disturbance, and heat of polymerization change during the online closed-loop performance tests. These results signify the ability of NWMPC to handle practical LDPE tubular reactor control scenarios.

Optimization Control of Thermal Efficiency for Cement Raw Meal Pre-Decomposition Based on Two-Layer Structure Model Predictive Control

“…As a representative of advanced process control, the MPC has been widely used in complex industrial processes because of its advantages, such as low computational effort and high robustness [27], [28], [29], [30]. Fig.…”