WCSE 2017
ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.173

Neural Network Estimator for Gas-Phase Ethylene Polymerization Process

Thanawat Kaewsanmuang, Paisan Kittisupakorn, David Banjerdpongchai

Abstract— Ethylene concentration is a significant parameter in the polymerization process because it affects the molecular weight distribution of the polymer produced. However, there is a challenge to obtain a good estimation of ethylene concentration due to its highly nonlinear behavior and interaction among state variables. In this work, the neural network (NN) has been applied to estimate the concentration of ethylene monomer with generic model control (GMC) controlling the reactor temperature. The neural network has been trained with backpropagation and Levenberg-Marquart techniques. Simulation results have shown that the neural network estimator can provide good estimates of the monomer concentration under nominal condition and disturbance cases.

Index Terms— Polymerization process, Neural network, Estimator, Generic model control.

Thanawat Kaewsanmuang, Paisan Kittisupakorn
Department of Chemical Engineering, Chulalongkorn University, THAILAND
David Banjerdpongchai
Department of Electrical Engineering, Chulalongkorn University, THAILAND


Cite: Thanawat Kaewsanmuang, Paisan Kittisupakorn, David Banjerdpongchai, "Neural Network Estimator for Gas-Phase Ethylene Polymerization Process," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 999-1003, Beijing, 25-27 June, 2017.