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 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.