Modeling, Identification, and Validation of Models for Predictive Ammonia Control in a Wastewater Treatment Plant—A Case Study

• Published in: ISA transactions 2006-04, Vol.45 (2), p.159-174
• Main Authors: Stare, Aljaž, Hvala, Nadja, Vrečko, Darko
• Format: Article
• Language: English
• Subjects: Ammonia - isolation & purification; Ammonia - pharmacokinetics; Applied sciences; Bacteria - metabolism; Bioreactors - microbiology; Computer science; control theory; systems; Computer Simulation; Control theory. Systems; Exact sciences and technology; Industry - methods; Kalman filter; Modeling; Modelling and identification; Models for predictive ammonia control; Models, Biological; N4 sid subspace identification; Nitrogen removal; Pollution; Reduced order model; Wastewater treatment; Wastewaters; Water Pollutants - pharmacokinetics; Water Purification - methods; Water treatment and pollution
• ISSN: 0019-0578; 1879-2022
• DOI: 10.1016/S0019-0578(07)60187-6

The aim of this work is to develop the ammonia models that could be used for model predictive control (MPC) of nitrification process in a wastewater treatment plant. First, a reduced nonlinear model is presented, which is based on expression for nitrification reaction rate in activated sludge model No. 1 and modified for attached biomass processes, while second, a linear black-box model is shown. The data used for model identification were collected during several weeks of experiments on a real plant so that good identification data were obtained. The designed models were validated based on open loop simulations and predictions. Validation results show that the reduced nonlinear model performs better compared to the linear model, however, both models show relatively large errors compared to the real plant data. Hence, a closed loop simulation study was performed to see the differences between the performance of model predictive controller using previously estimated linear and nonlinear models and a standard proportional integral (PI) controller. From the simulation study results it was seen that in spite of relatively large model errors the MPC algorithms give better results in terms of ammonia removal compared to the PI controller, while MPC with the nonlinear model shows additional improvements over the MPC with the linear model.