Nonlinear moving horizon state estimation of an activated sludge model

A nonlinear moving horizon estimation algorithm is developed for state identification of an activated sludge wastewater treatment plant model. The state estimation problem is formulated as an optimal control problem over a finite horizon using the state equations of the activated sludge model and a least squares objective. The numerical solution procedure based on direct multiple-shooting can incorporate additional restrictions on the estimated quantities. First simulation results indicate that the non-measurable state variables can be estimated reliably under realistic online measurement assumptions.