Mathematical Model and Advanced Control for Gas-phase Olefin Polymerization in Fluidized-bed Catalytic Reactors

Ahmmed S. Ibrehem; Mohamed Azlan Hussain; Nayef M. Ghasem

doi:10.1016/S1004-9541(08)60042-7

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Mathematical Model and Advanced Control for Gas-phase Olefin Polymerization in Fluidized-bed Catalytic Reactors

Updated：2026-01-06

- Mathematical Model and Advanced Control for Gas-phase Olefin Polymerization in Fluidized-bed Catalytic Reactors
- Chinese Journal of Chemical Engineering Vol. 16, Issue 1, Pages: 84-89(2008)
- Affiliations：
  
  1. Department of Chemical Engineering, University of Malaya,Kuala Lumpur,Malaysia,50603
  2. Department of Chemical & Petroleum Engineering, UAE University, Al-Ain 17555, UAE
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(08)60042-7
  CLC：
- Published：2008
- Accepted：
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Ahmmed S. Ibrehem, Mohamed Azlan Hussain, Nayef M. Ghasem. Mathematical Model and Advanced Control for Gas-phase Olefin Polymerization in Fluidized-bed Catalytic Reactors[J]. , 2008, 16(1): 84-89.
DOI：

Ahmmed S. Ibrehem, Mohamed Azlan Hussain, Nayef M. Ghasem. Mathematical Model and Advanced Control for Gas-phase Olefin Polymerization in Fluidized-bed Catalytic Reactors[J]. , 2008, 16(1): 84-89. DOI： 10.1016/S1004-9541(08)60042-7.

摘要

Abstract

In this study

the developments in modeling gas-phase catalyzed olefin polymerization fluidized-bed reactors (FBR) using Ziegler-Natta catalyst is presented. The modified mathematical model to account for mass and heat transfer between the solid particles and the surrounding gas in the emulsion phase is developed in this work to include site activation reaction. This model developed in the present study is subsequently compared with well-known models

namely

the bubble-growth

well-mixed and the constant bubble size models for porous and non porous catalyst. The results we obtained from the model was very close to the constant bubble size model

well-mixed model and bubble growth model at the beginning of the reaction but its overall behavior changed and is closer to the well-mixed model compared with the bubble growth model and constant bubble size model after half an hour of operation. Neural-network based predictive controller are implemented to control the system and com-pared with the conventional PID controller

giving acceptable results.

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