Hybrid modelling and kinetic estimation for polystyrene batch reactor using Artificial Neutral Network (ANN) approach

Mohammad Anwar Hosen, Mohd Azlan Hussain, Farouq S. Mjalli

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Modelling polymerization processes involves considerable uncertainties due to the intricate polymerization reaction mechanism involved. The complex reaction kinetics results in highly nonlinear process dynamics. Available conventional models are limited in applicability and cannot describe accurately the actual physico-chemical characteristics of the reactor dynamics. The usual practice for operating polymerization reactors is to optimize the reactor temperature profile because the end use properties of the product polymer depend highly on temperature. However, to obtain accurate models in order to optimize the temperature profile, the kinetic parameters (i.e. frequency factors and activation energies) for a specific reactor must be determined accurately. Kinetic parameters vary considerably in batch reactors because of its high sensitivity to other reactor design and operational variables such as agitator geometry and speed, gel effects, heating systems, etc. In this work, the kinetic parameters were estimated for a styrene-free radical polymerization conducted in an experimental batch reactor system using a nonlinear least squares optimization algorithm. The estimated kinetic parameters were correlated with respect to reactor operating variables including initial reactor temperature (To), initial initiator concentration (Io) and heat duty (Q) using artificial neural network (ANN) techniques. The ANN kinetic model was then utilized in combination with the conventional mechanistic model. The experimental validation of the model revealed that the new model has high prediction capabilities compared withother reported models.

Original languageEnglish
Pages (from-to)274-287
Number of pages14
JournalAsia-Pacific Journal of Chemical Engineering
Volume6
Issue number2
DOIs
Publication statusPublished - Mar 2011

Fingerprint

Polystyrenes
Batch reactors
kinetics
Kinetics
Kinetic parameters
modeling
polymerization
Polymerization
temperature profile
Temperature
Styrene
Free radical polymerization
reactor
Reaction kinetics
reaction kinetics
free radical
Polymers
Gels
Activation energy
activation energy

Keywords

  • artificial neural network
  • kinetic parameters
  • modelling polymerization reactor
  • optimization
  • parameter estimation
  • polystyrene batch reactor

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Renewable Energy, Sustainability and the Environment
  • Waste Management and Disposal

Cite this

Hybrid modelling and kinetic estimation for polystyrene batch reactor using Artificial Neutral Network (ANN) approach. / Hosen, Mohammad Anwar; Hussain, Mohd Azlan; Mjalli, Farouq S.

In: Asia-Pacific Journal of Chemical Engineering, Vol. 6, No. 2, 03.2011, p. 274-287.

Research output: Contribution to journalArticle

@article{c6feae3d74eb44bfae8f1e50ba852e55,
title = "Hybrid modelling and kinetic estimation for polystyrene batch reactor using Artificial Neutral Network (ANN) approach",
abstract = "Modelling polymerization processes involves considerable uncertainties due to the intricate polymerization reaction mechanism involved. The complex reaction kinetics results in highly nonlinear process dynamics. Available conventional models are limited in applicability and cannot describe accurately the actual physico-chemical characteristics of the reactor dynamics. The usual practice for operating polymerization reactors is to optimize the reactor temperature profile because the end use properties of the product polymer depend highly on temperature. However, to obtain accurate models in order to optimize the temperature profile, the kinetic parameters (i.e. frequency factors and activation energies) for a specific reactor must be determined accurately. Kinetic parameters vary considerably in batch reactors because of its high sensitivity to other reactor design and operational variables such as agitator geometry and speed, gel effects, heating systems, etc. In this work, the kinetic parameters were estimated for a styrene-free radical polymerization conducted in an experimental batch reactor system using a nonlinear least squares optimization algorithm. The estimated kinetic parameters were correlated with respect to reactor operating variables including initial reactor temperature (To), initial initiator concentration (Io) and heat duty (Q) using artificial neural network (ANN) techniques. The ANN kinetic model was then utilized in combination with the conventional mechanistic model. The experimental validation of the model revealed that the new model has high prediction capabilities compared withother reported models.",
keywords = "artificial neural network, kinetic parameters, modelling polymerization reactor, optimization, parameter estimation, polystyrene batch reactor",
author = "Hosen, {Mohammad Anwar} and Hussain, {Mohd Azlan} and Mjalli, {Farouq S.}",
year = "2011",
month = "3",
doi = "10.1002/apj.435",
language = "English",
volume = "6",
pages = "274--287",
journal = "Asia-Pacific Journal of Chemical Engineering",
issn = "1932-2135",
publisher = "John Wiley and Sons Ltd",
number = "2",

}

TY - JOUR

T1 - Hybrid modelling and kinetic estimation for polystyrene batch reactor using Artificial Neutral Network (ANN) approach

AU - Hosen, Mohammad Anwar

AU - Hussain, Mohd Azlan

AU - Mjalli, Farouq S.

PY - 2011/3

Y1 - 2011/3

N2 - Modelling polymerization processes involves considerable uncertainties due to the intricate polymerization reaction mechanism involved. The complex reaction kinetics results in highly nonlinear process dynamics. Available conventional models are limited in applicability and cannot describe accurately the actual physico-chemical characteristics of the reactor dynamics. The usual practice for operating polymerization reactors is to optimize the reactor temperature profile because the end use properties of the product polymer depend highly on temperature. However, to obtain accurate models in order to optimize the temperature profile, the kinetic parameters (i.e. frequency factors and activation energies) for a specific reactor must be determined accurately. Kinetic parameters vary considerably in batch reactors because of its high sensitivity to other reactor design and operational variables such as agitator geometry and speed, gel effects, heating systems, etc. In this work, the kinetic parameters were estimated for a styrene-free radical polymerization conducted in an experimental batch reactor system using a nonlinear least squares optimization algorithm. The estimated kinetic parameters were correlated with respect to reactor operating variables including initial reactor temperature (To), initial initiator concentration (Io) and heat duty (Q) using artificial neural network (ANN) techniques. The ANN kinetic model was then utilized in combination with the conventional mechanistic model. The experimental validation of the model revealed that the new model has high prediction capabilities compared withother reported models.

AB - Modelling polymerization processes involves considerable uncertainties due to the intricate polymerization reaction mechanism involved. The complex reaction kinetics results in highly nonlinear process dynamics. Available conventional models are limited in applicability and cannot describe accurately the actual physico-chemical characteristics of the reactor dynamics. The usual practice for operating polymerization reactors is to optimize the reactor temperature profile because the end use properties of the product polymer depend highly on temperature. However, to obtain accurate models in order to optimize the temperature profile, the kinetic parameters (i.e. frequency factors and activation energies) for a specific reactor must be determined accurately. Kinetic parameters vary considerably in batch reactors because of its high sensitivity to other reactor design and operational variables such as agitator geometry and speed, gel effects, heating systems, etc. In this work, the kinetic parameters were estimated for a styrene-free radical polymerization conducted in an experimental batch reactor system using a nonlinear least squares optimization algorithm. The estimated kinetic parameters were correlated with respect to reactor operating variables including initial reactor temperature (To), initial initiator concentration (Io) and heat duty (Q) using artificial neural network (ANN) techniques. The ANN kinetic model was then utilized in combination with the conventional mechanistic model. The experimental validation of the model revealed that the new model has high prediction capabilities compared withother reported models.

KW - artificial neural network

KW - kinetic parameters

KW - modelling polymerization reactor

KW - optimization

KW - parameter estimation

KW - polystyrene batch reactor

UR - http://www.scopus.com/inward/record.url?scp=79953700570&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79953700570&partnerID=8YFLogxK

U2 - 10.1002/apj.435

DO - 10.1002/apj.435

M3 - Article

VL - 6

SP - 274

EP - 287

JO - Asia-Pacific Journal of Chemical Engineering

JF - Asia-Pacific Journal of Chemical Engineering

SN - 1932-2135

IS - 2

ER -