• 1 School of Chemical Engineering, Engineering Campus, Universiti Sains Malaysia, Nibong Tebal, 14700Penang, Malaysia
  • 2 School of Chemical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam, 40450Selangor, Malaysia
  • 3 School of Chemical and Energy Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, 81310Johor, Malaysia
  • 4 Mechanical Engineering, Faculty of Engineering and Technology, Future University in Egypt, New Cairo11845, Egypt
ACS Omega, 2022 Nov 08;7(44):39648-39661.
PMID: 36385840 DOI: 10.1021/acsomega.2c03078


Fouling formation in reactor vessels poses a serious threat to the safe operation of the industrial low-density polyethylene (LDPE) polymerization. Fouling also degrades the polymer quality and causes productivity loss to some extent. In this work, neural Wiener model predictive control (NWMPC) is introduced to address the fouling concern. In addition, a soft sensor model is used to activate the fouling-defouling (F-D) mechanism when the fouling surpasses the thickness limit to prevent vessel overheating. NWMPC is proven to be fast, stable, and robust under various control scenarios. The use of a soft sensor model in conjunction with NWMPC enables the online monitoring and controlling of the F-D processes. When comparison is made with a state space (SSMPC) utilizing only the linear block, NWMPC is found to be able to control the LDPE grade with quicker grade transition and lower resource consumption.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.