Design of fuzzy-based adaptive controllers for filtration process

Abstract

Filtration process uses membrane to separate solid components in a liquid suspension based on their size differences. However, the process is characterised with dynamic variability and uncertainty which makes its control difficult whenever a linear-based control scheme such as conventional proportional integral derivative (PID) is employed. Therefore, this work aimed to design adaptive controllers using fuzzy inference system to accommodate both the dynamic variability and uncertainty in filtration process. Experimental study was carried out on batch filtration unit where the slurry, a mixture of calcium carbonate (CaCO3) and water, was separated. The input and output data generated from the experiment consisted of one manipulated variable (feed pressure, Pr), one disturbance variable (concentration of the solute in the feed, Cs0) and one controlled variable (suspension concentration, Cs). The data were used to develop a model based on autoregressive integrated moving average exogenous input structure in the frame of fuzzy inference system. The fuzzy inference system was based on Takagi-Sugeno prototype. Adaptive controllers with fuzzy inference system were designed using pole placement and optimal control techniques. The controllers' performances were compared with conventional PI controller using rise time, settling time and overshoot.