Low density polyethylene (LDPE) is a valuable commodity polymer with high demands because of its versatile applications. Due to the competitive LDPE market, manufacturers need to improve their production by implementing advanced process control (APC) schemes such as nonlinear model predictive control (NMPC) to control grade transition and increase polymer conversion. Recently, NMPC based on first principle model (FPM) has been implemented to control the LDPE tubular reactor process. However, such a model requires significant effort to be developed and is less feasible for industrial implementation. Moreover, there are time delay issues with the practical LDPE quality measurement, i.e., melt flow index (MFI) and polymer conversion, that affect the NMPC control performance. Thus, this study aims to develop and evaluate the performance of the Neural Wiener MPC (NWMPC) in controlling LDPE grade transition and conversion. In addition, a soft sensor model with a bias updated scheme was developed to estimate the delay measurements and simultaneously update the model output signal with the current measurements. In order to obtain the input-output data to develop the NW model, a dynamic simulation model of the LDPE tubular reactor was developed using Aspen Plus and Aspen Dynamic software. The NW model produced a correlation of determination (R2) of 0.989 for the LDPE conversion and R2 of 0.986 for the MFI profile from the model validation results. During the development of the soft sensor model, the input selection was conducted based on the Pearson correlation coefficient xxiv (PCC) and expert knowledge. The validation results of the soft sensor model showed R2 of 0.999 and R2 of 0.998 for polymer conversion and MFI, respectively. In this work, the NWMPC control scheme was developed inside Matlab Simulink and integrated with Aspen Dynamic for online LDPE tubular reactor control. In order to evaluate the NWMPC performance, the controller was tested in grade transition, conversion change, disturbance rejection, and robustness tests using State space MPC (SSMPC) as a comparison. The tests’ process profiling and integral squared error (ISE) analysis showed that the NWMPC successfully outperformed the SSMPC. Furthermore, the combination of the NWMPC with soft sensor (NWMPC-SS) demonstrated excellent performance in handling LDPE grade transitions and conversion changes despite a time delay in the control loop. Based on these performances, the ability of the NWMPC-SS to control the LDPE tubular reactor is established, which highlights its potential comparable with FPM-based NMPC.
