Neural network based pH control of a weak acid — Strong base system

Author

Tharakan, L.G. ; Benny, A. ; Jaffar, N.E. ; Jaleel, J. Abdul

fYear

2013

fDate

22-23 March 2013

Firstpage

674

Lastpage

679

Abstract

pH neutralization is a difficult process to be controlled due to the nonlinear and time-varying process characteristics. Model Predictive Control appeared in industry as an effective means to deal with multivariable constrained control problems. This paper does the study of the pH neutralization process of a weak acid - strong base system using a neural network model predictive control technique. The simulation results are analyzed for step and random acid disturbances, which shows that the controller controls the pH within the required limits with less mean square error.

Keywords

chemical industry; mean square error methods; neurocontrollers; nonlinear control systems; pH; predictive control; process control; time-varying systems; acid disturbance; mean square error; model predictive control; multivariable constrained control problem; neural network; nonlinear process; pH control; pH neutralization; time-varying process; weak acid-strong base system; Artificial neural networks; Biological neural networks; Mathematical model; Predictive control; Predictive models; Artificial Neural Network; Neural Network Model Predictive Control; Weak Acid — Strong Base System; pH Control;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), 2013 International Multi-Conference on

Conference_Location

Kottayam

Print_ISBN

978-1-4673-5089-1

Type

conf

DOI

10.1109/iMac4s.2013.6526494

Filename

6526494