State and Coefficients Estimation for the Activated Sludge Process Using a Modified Kalman Filter Algorithm

Abstract

An advanced identification algorithm is proposed for the on-line estimation of the non-measurable state variables and coefficients of the IAWPRC biodegradation dynamic model for the Activated Sludge process. The new algorithm employs real-time measurable data. This paper represents a first step in a mathematical methodology which intends to improve the present techniques used for model calibration and validation. The algorithm is based on a recursive non-linear extended Kaiman filter and it has been verifiedrby using simulated values of the measurable data. These simulations describe the behaviour of a specific physical configuration of the AS process under steady and transient operational conditions. When the coefficients are assumed to be known, the algorithm estimates the state rapidly and accurately even when the measurements are affected by a very high degree of noise and the initial estimates are very far from the real values. The speed of convergence of the algorithm decreases when neither the states nor the coefficients of the model are known.