Multivariable identification of an activated sludge process with subspace based algorithms

Abstract

The objectives of this project are to identify a linear time-invariant dynamical model of an activated sludge process. Such a system is characterized by stiff dynamics, nonlinearities, time-variant parameters, recycles, multivariable with many cross-couplings and wide variations in the inflow and the composition of the incoming wastewater. In this project study, an identification approach based on subspace methods is applied in order to estimate a nominal MIMO state space model around a given operating point, by probing the system in open-loop with multi-level random signals (MRBS). Three subspace algorithms are used, and their performances are compared based on adequate quality criteria, taking into account identification/validation data. As a result, the selected model is a very low-order one, and it describes the complex dynamics of the process well. Important issues concerning the generation of the data set and the estimation of the model order is discussed.