A survey on artificial intelligence techniques for various wastewater treatment processes
Pollutant removal percentage is a key parameter for every WWTPs, and it is crucial to predict pollutant removal efficiency. The efficiency of pollutant removal processes can be increased with the help of modeling and its optimization. Statistical models are not practical enough for wastewater treatm...
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| Format: | Article |
| Language: | English English |
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Penerbit UTEM
2023
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| Online Access: | http://umpir.ump.edu.my/id/eprint/37142/1/A%20survey%20on%20artificial%20intelligence%20techniques%20for%20various%20wastewater%20treatment%20processes.pdf http://umpir.ump.edu.my/id/eprint/37142/7/A%20survey%20on%20artificial%20intelligence%20techniques%20for%20various%20wastewater.pdf |
| _version_ | 1796995623617036288 |
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| author | Mohan, Varun Geetha Mubarak-Ali, Al-Fahim Vijayan, Bincy Lathakumari Mohamed Ariff, Ameedeen |
| author_facet | Mohan, Varun Geetha Mubarak-Ali, Al-Fahim Vijayan, Bincy Lathakumari Mohamed Ariff, Ameedeen |
| author_sort | Mohan, Varun Geetha |
| collection | UMP |
| description | Pollutant removal percentage is a key parameter for every WWTPs, and it is crucial to predict pollutant removal efficiency. The efficiency of pollutant removal processes can be increased with the help of modeling and its optimization. Statistical models are not practical enough for wastewater treatments due to complicated relationship among input and output parameters. AI models are generally more flexible while modeling complex datasets with missing data and nonlinearities. Many AI techniques are available, and the aim is to sort out the best AI technique to design predictive models for WWTPs. Deep Learning and Ensemble are the main techniques reviewed in this work. The Ensemble Learning models showing the most successful performance among other techniques by generally showed their accuracy and efficiency. |
| first_indexed | 2024-03-06T13:05:05Z |
| format | Article |
| id | UMPir37142 |
| institution | Universiti Malaysia Pahang |
| language | English English |
| last_indexed | 2024-03-06T13:05:05Z |
| publishDate | 2023 |
| publisher | Penerbit UTEM |
| record_format | dspace |
| spelling | UMPir371422023-08-07T01:02:45Z http://umpir.ump.edu.my/id/eprint/37142/ A survey on artificial intelligence techniques for various wastewater treatment processes Mohan, Varun Geetha Mubarak-Ali, Al-Fahim Vijayan, Bincy Lathakumari Mohamed Ariff, Ameedeen QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Pollutant removal percentage is a key parameter for every WWTPs, and it is crucial to predict pollutant removal efficiency. The efficiency of pollutant removal processes can be increased with the help of modeling and its optimization. Statistical models are not practical enough for wastewater treatments due to complicated relationship among input and output parameters. AI models are generally more flexible while modeling complex datasets with missing data and nonlinearities. Many AI techniques are available, and the aim is to sort out the best AI technique to design predictive models for WWTPs. Deep Learning and Ensemble are the main techniques reviewed in this work. The Ensemble Learning models showing the most successful performance among other techniques by generally showed their accuracy and efficiency. Penerbit UTEM 2023-06 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/37142/1/A%20survey%20on%20artificial%20intelligence%20techniques%20for%20various%20wastewater%20treatment%20processes.pdf pdf en cc_by_nc_nd_4 http://umpir.ump.edu.my/id/eprint/37142/7/A%20survey%20on%20artificial%20intelligence%20techniques%20for%20various%20wastewater.pdf Mohan, Varun Geetha and Mubarak-Ali, Al-Fahim and Vijayan, Bincy Lathakumari and Mohamed Ariff, Ameedeen (2023) A survey on artificial intelligence techniques for various wastewater treatment processes. Journal of Engineering and Technology (JET), 14 (1). p. 175. ISSN 2180-3811. (Published) https://jet.utem.edu.my/jet/article/view/6411/4242 https://jet.utem.edu.my/jet/article/view/6411/4242 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) Mohan, Varun Geetha Mubarak-Ali, Al-Fahim Vijayan, Bincy Lathakumari Mohamed Ariff, Ameedeen A survey on artificial intelligence techniques for various wastewater treatment processes |
| title | A survey on artificial intelligence techniques for various wastewater treatment processes |
| title_full | A survey on artificial intelligence techniques for various wastewater treatment processes |
| title_fullStr | A survey on artificial intelligence techniques for various wastewater treatment processes |
| title_full_unstemmed | A survey on artificial intelligence techniques for various wastewater treatment processes |
| title_short | A survey on artificial intelligence techniques for various wastewater treatment processes |
| title_sort | survey on artificial intelligence techniques for various wastewater treatment processes |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TA Engineering (General). Civil engineering (General) |
| url | http://umpir.ump.edu.my/id/eprint/37142/1/A%20survey%20on%20artificial%20intelligence%20techniques%20for%20various%20wastewater%20treatment%20processes.pdf http://umpir.ump.edu.my/id/eprint/37142/7/A%20survey%20on%20artificial%20intelligence%20techniques%20for%20various%20wastewater.pdf |
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