Modeling of low density polyethylene tubular reactor using nonlinear block-oriented model. (2021)

• Record Type: Journal Article
• Authors: Muhammad, D.; Ahmad, Z.; Aziz, N.
• Is Part Of: Materials today. Volume 42:Part 1(2021)
• Page Start: 39
• Page End: 44
• Publication Date: 2021
• Subjects: Low density polyethylene (LDPE) -- Block-oriented model -- Tubular reactor -- Wiener model -- Hammerstein model; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2020.09.238
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: In this paper, nonlinear block-oriented models, namely Neural Wiener (NW) and Neural Hammerstein (NH) model, are developed to simulate Low density polyethylene (LDPE) tubular reactor process. The desired simulated outputs of the reactor model are LDPE polymer conversion and melt flow index (MFI). Generally, a nonlinear block-oriented model consists of dynamic linear block and static nonlinear element, which are identified using nonlinear optimization techniques. A modified Neural Wiener (M−NW) with additional input scheme is also considered during the model development stage. In order to generate data for the model identification process, a steady state model of the LDPE polymerization process is developed using Aspen Plus and then converted into dynamic state using Aspen Dynamic software. Based on the model validation results, M−NW has outperformed the other two models with the coefficient of determination (R 2 ) of 0.993 for polymer conversion and 0.986 for MFI results. Moreover, the simulation results for NW and NH models have shown a comparable performance in modeling the polymer conversion. However, the NW model has performed better in predicting MFI with R 2 0.984 compared to than the NH model with R 2 0.955. Thus, based on the simulation results, the application of nonlinear block-oriented models such as the M−NW and NW model in simulating the LDPE polymerization process is well justified. Such models can be further implemented in model-based control scheme or as soft sensor.