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
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U2 - 10.1002/apj.435
DO - 10.1002/apj.435
M3 - Article
AN - SCOPUS:79953700570
SN - 1932-2135
VL - 6
SP - 274
EP - 287
JO - Asia-Pacific Journal of Chemical Engineering
JF - Asia-Pacific Journal of Chemical Engineering
IS - 2
ER -