Self-tuning PID controller for activated sludge system

Abstract

In this study, a self-tuning Proportional-Integral-Derivative (PID) controller is applied to a multivariable sludge process model. The activated sludge process model, with a set of measured data from the existing operating plant, is obtained using prediction error method (PEM) with best fits of higher than 80% with two variables to be controlled i.e. concentration of Nitrate and Dissolve Oxygen (DO). The obtained model is then reduced with two model reduction techniques, i.e. Moore’s Balanced Model Reduction and Enn’s Frequency Weighted Model Reduction technique. At first, PI and PID controllers are implemented heuristically on these reduced models to control concentration of Nitrate and DO. Relative Gain Array (RGA) is applied which yields identity matrix for both reduced model. This implies that the multi-loop controllers in the models can be tuned similar to single-input single-output (SISO) controller due to least interactions occurred between concentration of Nitrate and DO. In order to optimize these controllers, particle swarm optimization (PSO) technique is utilized as optimization algorithm in order to tune the PID parameters. From the results obtained, it is concluded that the self-tuned PI controller yields a best result for the activated sludge process with a faster settling time and less percentage overshoot.