Enhancing Effluent Quality Predictions in Wastewater Treatment with LSTM Neural Network
This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate t...
Gespeichert in:
| Veröffentlicht in: | International Conference on System Theory, Control and Computing S. 83 - 88 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
10.10.2024
|
| Schlagworte: | |
| ISSN: | 2473-5698 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate the dynamics of wastewater treatment more accurately and adjust operational strategies dynamically. By integrating LSTM networks, the research enables continuous prediction of Effluent Quality Index (EQI) variables under stochastic and deterministic scenarios, thereby improving the accuracy and efficiency of predicting pollutant levels. The research uses an LSTM model to learn from a comprehensive dataset derived from historical simulations of BSM2, where key parameters such as the oxygen transfer coefficient (K L a) are systematically varied to measure their impact on effluent quality. The LSTM's capability to handle complex, nonlinear data and its adaptability to time series forecasting significantly enhances model performance, offering a robust tool for real-time decision-making and process optimization in wastewater treatment facilities. This approach not only improves the accuracy and efficiency of predicting pollutant levels but also supports environmental compliance and operational sustainability, making it a valuable tool for environmental engineers and professionals in the field of wastewater treatment. |
|---|---|
| AbstractList | This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance the predictability and efficiency of wastewater treatment processes. The study aims to develop advanced predictive models that can simulate the dynamics of wastewater treatment more accurately and adjust operational strategies dynamically. By integrating LSTM networks, the research enables continuous prediction of Effluent Quality Index (EQI) variables under stochastic and deterministic scenarios, thereby improving the accuracy and efficiency of predicting pollutant levels. The research uses an LSTM model to learn from a comprehensive dataset derived from historical simulations of BSM2, where key parameters such as the oxygen transfer coefficient (K L a) are systematically varied to measure their impact on effluent quality. The LSTM's capability to handle complex, nonlinear data and its adaptability to time series forecasting significantly enhances model performance, offering a robust tool for real-time decision-making and process optimization in wastewater treatment facilities. This approach not only improves the accuracy and efficiency of predicting pollutant levels but also supports environmental compliance and operational sustainability, making it a valuable tool for environmental engineers and professionals in the field of wastewater treatment. |
| Author | Diaconu, Larisa Voipan, Andreea-Elena Barbu, Marian Vasiliev, Iulian Voipan, Daniel |
| Author_xml | – sequence: 1 givenname: Daniel surname: Voipan fullname: Voipan, Daniel email: Daniel.Voipan@ugal.ro organization: "Dunărea de Jos" University of Galați,Department of Computer Science and Information Technology,Galați,Romania – sequence: 2 givenname: Iulian surname: Vasiliev fullname: Vasiliev, Iulian email: Iulian.Vasiliev@ugal.ro organization: "Dunărea de Jos" University of Galați,Department of Automatic Control and Electrical Engineering,Galați,Romania – sequence: 3 givenname: Larisa surname: Diaconu fullname: Diaconu, Larisa email: Larisa.Diaconu@ugal.ro organization: "Dunărea de Jos" University of Galați,Department of Automatic Control and Electrical Engineering,Galați,Romania – sequence: 4 givenname: Andreea-Elena surname: Voipan fullname: Voipan, Andreea-Elena email: Andreea.Voipan@ugal.ro organization: "Dunărea de Jos" University of Galați,Department of Automatic Control and Electrical Engineering,Galați,Romania – sequence: 5 givenname: Marian surname: Barbu fullname: Barbu, Marian email: Marian.Barbu@ugal.ro organization: "Dunărea de Jos" University of Galați,Department of Automatic Control and Electrical Engineering,Galați,Romania |
| BookMark | eNo1kMtOwkAYRkejiYi8gYvxAYpz61yWpkEkwVuoYUn-0r8yWgYznYbw9mLUszmbk2_xXZKzsAtIyA1nY86Zu50Vi7IotHBcjAUTasyZUUpre0JGzjgrcya1Ek6ekoFQRma5dvaCjLrugzEmuTliB2Q5CRsIax_e6aRp2h5Doq89tD4d6EvE2q-T34WO-kCX0CXcQ8JIy4iQtj_t3qcNnS_KR_qEfYT2qLTfxc8rct5A2-Hoz0Pydj8pi4ds_jydFXfzzHOjUybqugZrUecaNThw3NkckNUN5AY0b4xCBxUKrKC2SlQOreUSseKVsdLIIbn-3fWIuPqKfgvxsPr_Qn4Ds2BXgg |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICSTCC62912.2024.10744668 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library (IEL) (UW System Shared) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE/IET Electronic Library url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISBN | 9798350364293 |
| EISSN | 2473-5698 |
| EndPage | 88 |
| ExternalDocumentID | 10744668 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IF 6IK 6IL 6IN AAJGR ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL |
| ID | FETCH-LOGICAL-i176t-2ddda88e656e6a9a91985ae0dfa57a61f74e9abe2ebad842b9e8813eeb1b78373 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 4 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001440908400014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 03:05:32 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i176t-2ddda88e656e6a9a91985ae0dfa57a61f74e9abe2ebad842b9e8813eeb1b78373 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_10744668 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-Oct.-10 |
| PublicationDateYYYYMMDD | 2024-10-10 |
| PublicationDate_xml | – month: 10 year: 2024 text: 2024-Oct.-10 day: 10 |
| PublicationDecade | 2020 |
| PublicationTitle | International Conference on System Theory, Control and Computing |
| PublicationTitleAbbrev | ICSTCC |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0003177778 |
| Score | 1.9153588 |
| Snippet | This research paper delves into the application of Long Short-Term Memory (LSTM) neural networks within the Benchmark Simulation Model No. 2 (BSM2) to enhance... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 83 |
| SubjectTerms | Accuracy Adaptation models Artificial Neural Networks BSM2 Effluent Quality Effluents Long short term memory LSTM Predictive models Real-time systems Stochastic processes Time series analysis Wastewater Wastewater treatment Wastewater Treatment Plants |
| Title | Enhancing Effluent Quality Predictions in Wastewater Treatment with LSTM Neural Network |
| URI | https://ieeexplore.ieee.org/document/10744668 |
| WOSCitedRecordID | wos001440908400014&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEF5sEfFUHxXfrOA1bTfZ7uMcWhRqKTTa3spsM9GCpNKmgv_e3W1S8eDBHJIQMpuwm2Qms_N9HyH3goGKIIMAQYmAQ2YCI5ld8bkNmEPNjNdYehnI4VBNp3pUgtU9FgYRffEZttyun8tPl_ONS5W1XfEgF0LVSE1KsQVr7RIq1hHaRR2Qu5JHs_0Yj5M4dhd0iKuQtyr7X0oq3pH0G_-8hSPS_IHk0dHO2RyTPcxPSKPSZKDlK3pKJr38zVFo5K-0l3n9kYJueTK-rL2blfEPGl3kdAJrlzlz5klVbk5dXpYOxskTdbQd8G43vk68SZ77vSR-CErxhGDBpCiCME1TUAptvIYCNGimVRewk2bQlSBYJjlqMBiigVTx0GhUikVov91G2r_W6IzU82WO54Tas-aZDWOUTiNuotBwFzYhGM07tm12QZquo2YfW36MWdVHl38cvyKHbjicB2Cda1IvVhu8Ifvzz2KxXt36Uf0GF5Ck_w |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PT8IwFH5RNOoJf2D8bU28DtatbO2ZQCAOQsIUbqTd3pTEDAPDxP_etjCMBw_usDXL2jXttvf2-r7vA3gMqOS-zKSDkgcOk5lyVEj1jiXaYfYEVVZj6SUKBwM-mYjhBqxusTCIaJPPsG6Kdi0_nScrEyprmORBFgR8F_aajHnuGq61DaloU6g3fgAPGybNRq81ilstc0uDufJYvWzhl5aKNSWd6j87cQy1H1AeGW7NzQnsYH4K1VKVgWxe0jMYt_M3Q6KRv5J2ZhVICrJmyvjS9c26jH3UyCwnY7k0sTNTPS4TzomJzJJoFPeJIe6Q7_pgM8Vr8Nxpx62us5FPcGY0DArHS9NUco7aY8NACimo4E2JbprJZigDmoUMhVTooZIpZ54SyDn1UX-9Vaj_W_1zqOTzHC-A6KuSTDsyXKQ-U76nmHGcUCrBXN02vYSaGajpx5ohY1qO0dUf5-_hsBv3o2nUGzxdw5GZGmMPqHsDlWKxwlvYTz6L2XJxZ2f4G9o2qEY |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=International+Conference+on+System+Theory%2C+Control+and+Computing&rft.atitle=Enhancing+Effluent+Quality+Predictions+in+Wastewater+Treatment+with+LSTM+Neural+Network&rft.au=Voipan%2C+Daniel&rft.au=Vasiliev%2C+Iulian&rft.au=Diaconu%2C+Larisa&rft.au=Voipan%2C+Andreea-Elena&rft.date=2024-10-10&rft.pub=IEEE&rft.eissn=2473-5698&rft.spage=83&rft.epage=88&rft_id=info:doi/10.1109%2FICSTCC62912.2024.10744668&rft.externalDocID=10744668 |