Identification and predictive control of spray tower system using artificial neural network and differential evolution algorithm

Danzomo, Bashir A. and Salami, Momoh Jimoh Eyiomika and Khan, Md. Raisuddin (2015) Identification and predictive control of spray tower system using artificial neural network and differential evolution algorithm. In: 2015 10th Asian Control Conference (ASCC 2015), 31st May- 3rd June 2015, Kota Kinabalu, Sabah.

	PDF - Published Version Restricted to Repository staff only Download (483kB) | Request a copy
	PDF Restricted to Repository staff only Download (1MB) | Request a copy

Abstract

Increasing demands for high precision environmental protection measures regarding particulate matter (PM) emission from industrial productions and non-linear characteristics of spray tower system lead to the application of an intelligent control technique to adequately deal with these complexities. This includes the use of an artificial neural network (ANN) based predictive control strategy and differential evolution (DE) optimization algorithm to determines the optimal control signal, uk (liquid droplet size, dD) by minimizing the cost function such that the output is set below the allowable PM concentration. A recurrent neural network (RNN) based on non-linear autoregressive with exogenous inputs (NARX) model has been used to develop the dynamic model of the system. The data for the training was obtained from empirical model of a spray tower system which involved 500 data sets representing the process input and the output PM concentration. The control process was implemented using MATLAB code by considering two DE optimization strategies; DE/best/1/bin and DE/rand/1/bin. The effectiveness of the controllers was demonstrated for different iterations by tuning the control parameters such as the prediction horizon, weight factor and control horizon. From the control response, it can be seen that the controller for the DE/rand/1/bin does a very good job of controlling the PM below the WHO allowable emission rate of 20g/μm3.

Item Type:	Conference or Workshop Item (Invited Papers)
Additional Information:	2470/45226
Uncontrolled Keywords:	System identification, predictive control, spray tower system, artificial neural network, differential evolution algorithm
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QD Chemistry
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Prof Momoh-Jimoh Salami
Date Deposited:	27 Oct 2015 12:26
Last Modified:	23 May 2016 11:10
URI:	http://irep.iium.edu.my/id/eprint/45226

Actions (login required)

View Item