Artificial neural networks for performance prediction of full-scale wastewater treatment plants: a systematic review.
| Autor: | Dantas MS; Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, MG CEP 31270-901, Brazil E-mail: marina-dantas@hotmail.com., Christofaro C; Department of Forestry Engineering, Federal University of Jequitinhonha and Mucuri Valleys, Road MG 367, 5000, Diamantina, MG CEP 39100-000, Brazil., Oliveira SC; Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Av. Presidente Antônio Carlos, 6627, Belo Horizonte, MG CEP 31270-901, Brazil. |
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| Jazyk: | angličtina |
| Zdroj: | Water science and technology : a journal of the International Association on Water Pollution Research [Water Sci Technol] 2023 Sep; Vol. 88 (6), pp. 1447-1470. |
| DOI: | 10.2166/wst.2023.276 |
| Abstrakt: | Wastewater treatment plants (WWTPs) are complex systems that must maintain high levels of performance to achieve adequate effluent quality to protect the environment and public health. Artificial intelligence and machine learning methods have gained attention in recent years for modeling complex problems, such as wastewater treatment. Although artificial neural networks (ANNs) have been identified as the most common of these methods, no study has investigated the development and configuration of these models. We conducted a systematic literature review on the use of ANNs to predict the effluent quality and removal efficiencies of full-scale WWTPs. Three databases were searched, and 44 records of the 667 identified were selected based on the eligibility criteria. The data extracted from the papers showed that the majority of studies used the feedforward neural network model with a backpropagation training algorithm to predict the effluent quality of plants, particularly in terms of organic matter indicators. The findings of this research may help in the search for an optimum design modeling process for future studies of similar prediction problems. |
| Databáze: | MEDLINE |
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