Modeling of low density polyethylene tubular reactor using nonlinear block-oriented model

Muhammad, Dinie; Ahmad, Zainal Arifin; Aziz, N.

doi:10.1016/j.matpr.2020.09.238

Cited by 5 publications

(5 citation statements)

References 20 publications

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“…In this work, the L-N approach is considered due to its straightforward technique and ability to produce an accurate static nonlinearity block. 46 Therefore, the Wiener model identification using the L-N approach is adopted in this study, 39 which is summarized in Figure 4.…”

Section: Identification Methodsmentioning

confidence: 99%

“…The NW model has been reported to demonstrate the best modeling capability among the nonlinear blockoriented models. 22 The NW model structure is developed based on Muhammad, Ahmad, and Aziz, 39 which is presented in Figure 3. The model structure consists of a dynamic linear block represented by the SS model, cascaded with a static nonlinear block represented by the NN model.…”

Section: Mfi Modelmentioning

confidence: 99%

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

Muhammad

Rohman

Ahmad

et al. 2021

Asia-Pacific J Chem Eng

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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.

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Section: Identification Methodsmentioning

confidence: 99%

Section: Mfi Modelmentioning

confidence: 99%

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

Muhammad

Rohman

Ahmad

et al. 2021

Asia-Pacific J Chem Eng

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“…The Weiner model identification using the linear–nonlinear (L–NL) technique is used in this research. 46 In accordance with the method, the first step is to collect a set of dynamic data. To obtain this data, perturbations of the Aspen Plus Dynamics model are carried out.…”

Section: Development Of Online Safety Control Using Mpcmentioning

confidence: 99%

Nonlinear Control of Fouling in Polyethylene Reactors

et al. 2022

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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.

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“…(2022) [ 17 ] applied a soft sensor, based on FNN, to estimate the fouling thickness layer, and Muhammad et al. (2021) [ 18 ] developed three nonlinear block‐oriented virtual analyzers for MI and conversion, called Neural Wiener (NW), Modified Neural Wiener (M‐NW), and Neural Hammerstein (NH).…”

Section: Introductionmentioning

confidence: 99%

“…Two recent works involving virtual analyzers are associated with the production of Low Density Polyethylene (LDPE) in a tubular reactor. Rohman et al (2022) [17] applied a soft sensor, based on FNN, to estimate the fouling thickness layer, and Muhammad et al (2021) [18] developed three nonlinear block-oriented virtual analyzers for MI and conversion, called Neural Wiener (NW), Modified Neural Wiener (M-NW), and Neural Hammerstein (NH).…”

Section: Introductionmentioning

confidence: 99%

Virtual Analyzers for MI and Density Based on Neural Networks Improved through an Integrated Strategy Involving a Constructive Algorithm and Definition of Initial Weights

Silva

Fontes

Embiruçu

2023

Macro Reaction Engineering

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This work presents the development and validation of two virtual analyzers (density and Melt Index (MI)) for quality monitoring and control of the final product in an industrial unit of Linear Polyethylene (LPE). Both models are based on Feedforward Neural Networks which are improved through a strategy involving the initial estimation of weights and a constructive algorithm to define the number of hidden units. The initialization strategy is based on linearization of the neural model with only one hidden unit (nonlinear model) and subsequent optimization of this model by maximizing its similarity to the standard linear regression model whose solution is obtained analytically. The Initial Neural Model (INM) is then used as a starting point for a gradual increase in the number of hidden units. In a validation test involving MI and density values collected over 2 years of operation, the neural model is able to predict these properties with mean percentage errors equal to 0.81% (MI) and 0.04% (density) and determination coefficients equal to 0.970 (MI) and 0.983 (density). The population coefficient estimated in all tests involving grade transitions (0.96) shows a strong linear correlation between the proposed model and laboratory measurements.

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Modeling of low density polyethylene tubular reactor using nonlinear block-oriented model

Cited by 5 publications

References 20 publications

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

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

Nonlinear Control of Fouling in Polyethylene Reactors

Virtual Analyzers for MI and Density Based on Neural Networks Improved through an Integrated Strategy Involving a Constructive Algorithm and Definition of Initial Weights

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