Journal
WATER SCIENCE AND TECHNOLOGY
Volume 81, Issue 11, Pages 2391-2400Publisher
IWA PUBLISHING
DOI: 10.2166/wst.2020.298
Keywords
Artificial Intelligence; machine learning; model predictive control; neuro-fuzzy computing; nutrient removal; real-time control
Funding
- HERA SpA
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Two separate goals should be jointly pursued in wastewater treatment: nutrient removal and energy conservation. An efficient controller performance should cope with process uncertainties, seasonal variations and process nonlinearities. This paper describes the design and testing of a model predictive controller (MPC) based on neuro-fuzzy techniques that is capable of estimating the main process variables and providing the right amount of aeration to achieve an efficient and economical operation. This algorithm has been field tested on a large-scale municipal wastewater treatment plant of about 500,000 PE, with encouraging results in terms of better effluent quality and energy savings.
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