Work

Dynamic plant-wide optimization of wastewater treatment plant performance using neuro-fuzzy models and predictive control

Year: 2026

Type: article

Abstract: Wastewater treatment plants (WWTPs) are complex facilities that must be operated efficiently from both environmental and economic perspectives. They need to balance a range of often conflicting performance objectives, necessitating a plant-wide approach and multi-objective optimization. This paper presents an approach for dynamic plant-wide optimization of the effluent quality index ( E Q I ) and operational cost index ( O C I ) by employing reduced neuro-fuzzy models to represent performance indices, combined with simplified predictive control. The approach is validated through a one-year simulation using the plant-wide benchmark simulation model no. 2 (BSM2PSFe) and by dynamically optimizing the dissolved oxygen (DO) concentration set-point in an aerobic reactor. The Pareto front of optimal solutions is analyzed, and the individual indices are integrated into a single objective function by adjusting the weights of the criteria. The results show that the proposed optimization approach provides a better trade-off between the E Q I and O C I indices than constant DO set-point values. Compared to the DO set-point of 1.5 g/m 3 , the presented optimization case improves E Q I for 2.64% and O C I for 0.87%. The advantage of the approach, apart from plant-wide consideration, is its reliance on measurements available in practice, making it suitable for implementation in real WWTPs.

Source: Desalination and Water Treatment

DOI: 10.1016/j.dwt.2026.102008

Authors: Gorazd Karer, Darko Vrečko, Nadja Hvala, Igor Škrjanc

Institutions: University of Ljubljana, Jožef Stefan Institute

Language: English

Topic: Wastewater Treatment and Nitrogen Removal

Subfield: Pollution

Field: Environmental Science

Domain: Physical Sciences

SDGs: Clean water and sanitation, Responsible consumption and production

Open Access status: gold

Estimated APC paid: $1,000