Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning
Currently, energy and environmental efficiency are critical aspects in wastewater treatment plants (WWTPs). In fact, WWTPs are significant energy consumers, especially in the active sludge process (ASP) for the N-ammonia removal. In this paper, we face the challenge of simultaneously improving the e...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/9/755 |
| _version_ | 1811263788688605184 |
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| author | Félix Hernández-del-Olmo Elena Gaudioso Raquel Dormido Natividad Duro |
| author_facet | Félix Hernández-del-Olmo Elena Gaudioso Raquel Dormido Natividad Duro |
| author_sort | Félix Hernández-del-Olmo |
| collection | DOAJ |
| description | Currently, energy and environmental efficiency are critical aspects in wastewater treatment plants (WWTPs). In fact, WWTPs are significant energy consumers, especially in the active sludge process (ASP) for the N-ammonia removal. In this paper, we face the challenge of simultaneously improving the economic and environmental performance by using a reinforcement learning approach. This approach improves the costs of the N-ammonia removal process in the extended WWTP Benchmark Simulation Model 1 (BSM1). It also performs better than a manual plant operator when disturbances affect the plant. Satisfactory experimental results show significant savings in a year of a working BSM1 plant. |
| first_indexed | 2024-04-12T19:51:33Z |
| format | Article |
| id | doaj.art-ecee093457264e7abec1245ad19ea3f7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-12T19:51:33Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ecee093457264e7abec1245ad19ea3f72022-12-22T03:18:49ZengMDPI AGEnergies1996-10732016-09-019975510.3390/en9090755en9090755Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement LearningFélix Hernández-del-Olmo0Elena Gaudioso1Raquel Dormido2Natividad Duro3Department of Artificial Intelligence, National Distance Education University (UNED), 28040 Madrid, SpainDepartment of Artificial Intelligence, National Distance Education University (UNED), 28040 Madrid, SpainDepartment of Computer Sciences and Automatic Control, National Distance Education University (UNED), 28040 Madrid, SpainDepartment of Computer Sciences and Automatic Control, National Distance Education University (UNED), 28040 Madrid, SpainCurrently, energy and environmental efficiency are critical aspects in wastewater treatment plants (WWTPs). In fact, WWTPs are significant energy consumers, especially in the active sludge process (ASP) for the N-ammonia removal. In this paper, we face the challenge of simultaneously improving the economic and environmental performance by using a reinforcement learning approach. This approach improves the costs of the N-ammonia removal process in the extended WWTP Benchmark Simulation Model 1 (BSM1). It also performs better than a manual plant operator when disturbances affect the plant. Satisfactory experimental results show significant savings in a year of a working BSM1 plant.http://www.mdpi.com/1996-1073/9/9/755benchmarkenergy savingenvironmental impactintelligent controlreinforcement learningwastewater system |
| spellingShingle | Félix Hernández-del-Olmo Elena Gaudioso Raquel Dormido Natividad Duro Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning Energies benchmark energy saving environmental impact intelligent control reinforcement learning wastewater system |
| title | Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning |
| title_full | Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning |
| title_fullStr | Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning |
| title_full_unstemmed | Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning |
| title_short | Energy and Environmental Efficiency for the N-Ammonia Removal Process in Wastewater Treatment Plants by Means of Reinforcement Learning |
| title_sort | energy and environmental efficiency for the n ammonia removal process in wastewater treatment plants by means of reinforcement learning |
| topic | benchmark energy saving environmental impact intelligent control reinforcement learning wastewater system |
| url | http://www.mdpi.com/1996-1073/9/9/755 |
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