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231225s2020 xx |||||o 00| ||eng c |
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|a 10.2166/wst.2020.298
|2 doi
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|a pubmed24n1045.xml
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|e rakwb
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|a eng
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|a Bernardelli, A
|e verfasserin
|4 aut
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|a Real-time model predictive control of a wastewater treatment plant based on machine learning
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|c 2020
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|a Text
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|2 rdacontent
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|a ƒaComputermedien
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|a Date Completed 14.08.2020
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|a Date Revised 07.12.2022
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|a published: Print
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|a Citation Status MEDLINE
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|a 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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|a Journal Article
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|a Waste Water
|2 NLM
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|a Marsili-Libelli, S
|e verfasserin
|4 aut
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|a Manzini, A
|e verfasserin
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|a Stancari, S
|e verfasserin
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|a Tardini, G
|e verfasserin
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|a Montanari, D
|e verfasserin
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|a Anceschi, G
|e verfasserin
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|a Gelli, P
|e verfasserin
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|a Venier, S
|e verfasserin
|4 aut
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|i Enthalten in
|t Water science and technology : a journal of the International Association on Water Pollution Research
|d 1986
|g 81(2020), 11 vom: 30. Juni, Seite 2391-2400
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|g volume:81
|g year:2020
|g number:11
|g day:30
|g month:06
|g pages:2391-2400
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|u http://dx.doi.org/10.2166/wst.2020.298
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