Soft Sensor with Adaptive Algorithm for Filter Gain Correction in the Online Monitoring System of a Polluted River

Abstract

This paper proposes the realization of a soft sensor using an adaptive algorithm with proportional correction of the gain coefficient for monitoring river water quality. This algorithm makes it possible to monitor online signals of an object described by nonlinear ordinary differential equations. Simulation studies of a biochemically polluted river, for which the water quality was represented by biochemical oxygen demand (BOD) indices and the dissolved oxygen (DO) deficit, were carried out. The algorithm concept uses only online measurements of the object, and adaptive changes in the gain coefficient are determined based on the adaptation error adopted for this purpose. Simulation results indicated the correct functioning of the soft sensor even for inaccurately identified parameters of the mathematical model and for unknown values and intensity of disturbances affecting the object. The quality of the signals monitored via a soft sensor implemented in this way was determined with the root-mean-squared error (RMSE) and mean percentage error (MPE) indicators and compared with the Kalman filter.