Adaptive control and identification of the dissolved oxygen process

Abstract

This paper suggests how nonlinear adaptive control might lead to improved control of the dissolved oxygen (DO) concentration in the aerator of a wastewater treatment plant. The DO dynamics can be represented by a bilinear model for which we are interested in both parameter identification and control. The estimation of key parameters of the process model is important because the values of these parameters cannot be obtained from direct measurement. Hence a least-squares procedure for obtaining unique parameter estimates is developed and then combined with a minimum variance control algorithm to obtain an adaptive controller which is used both to generate useful parameter estimates and to control the process. Extensions to the case where the parameters vary at the same rate as the DO are also discussed.