Data driven fuzzy c-means clustering based on particle swarm optimization for pH process

The control of pH process has a vast range of applications in wastewater treatment, biochemical and electrochemical processes, the paper and pulp industry and many other areas. Tight control of pH is also critical in the production of pharmaceuticals. However, the dynamics of pH process is highly no...

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Bibliographic Details
Published in2011 International Conference on Emerging Trends in Electrical and Computer Technology pp. 220 - 225
Main Authors Sivaraman, E, Arulselvi, S, Babu, K
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2011
Subjects
Algorithm design and analysis
Clustering algorithms
Equations
fuzzy
Load modeling
Mathematical model
nonlinear
Particle swarm optimization
pH control
pole placement
Process control
T-S model
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Summary:The control of pH process has a vast range of applications in wastewater treatment, biochemical and electrochemical processes, the paper and pulp industry and many other areas. Tight control of pH is also critical in the production of pharmaceuticals. However, the dynamics of pH process is highly nonlinear, time varying with change in gain of several orders. It is very difficult to investigate the dynamic behavior of such systems using conventional modeling techniques thereby designing controller parameters. In this paper, pole placement based PI controller is designed for a experimental pH process, Takagi-Sugeno (T-S) model is developed for a pH process using fuzzy c-means (FCM) algorithm and particle swarm optimization (PSO) based FCM algorithm. The performance of the proposed model FCM with PSO is compared with the results obtained by fuzzy c-means algorithm. The comparison shows the superiority of the proposed model. The proposed model can be used to develop model based control techniques.
ISBN:1424479231
9781424479238
DOI:10.1109/ICETECT.2011.5760119