System identification of nonlinear dynamical models: Application to wastewater treatment plant

The paper presents a novel approach to identification of stochastic nonlinear dynamic systems using efficient approximation methods. The motivation behind this work is to develop a computationally efficient and robust algorithm for estimation of wastewater treatment plant model parameters. The mathematical model of the plant is required for the application of advanced predictive control algorithms and condition monitoring. The presented algorithm employs the Expectation-Maximization algorithm to compute the Maximum likelihood estimates of the unknown model parameters. The algorithm uses the Unscented Transformation (UT) to approximate the posterior distribution of the random variable that undergoes a nonlinear transformations. The advantage of this approach lies in efficient approximation methods that greatly reduce the computational load of the algorithm and is therefore suitable for on-line implementation.