Using AI and BES/MFC to decrease the prediction time of BOD(5) measurement

Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD(5) is the amount of oxygen consumed in 5 days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this research is to apply micr...

Descripción completa

Detalles Bibliográficos
Autores principales: Medvedev, Ivan, Kornaukhova, Mariya, Galazis, Christoforos, Lóránt, Bálint, Tardy, Gábor Márk, Losev, Alexander, Goryanin, Igor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403401/
https://www.ncbi.nlm.nih.gov/pubmed/37542117
http://dx.doi.org/10.1007/s10661-023-11576-0
_version_ 1785085058967666688
author Medvedev, Ivan
Kornaukhova, Mariya
Galazis, Christoforos
Lóránt, Bálint
Tardy, Gábor Márk
Losev, Alexander
Goryanin, Igor
author_facet Medvedev, Ivan
Kornaukhova, Mariya
Galazis, Christoforos
Lóránt, Bálint
Tardy, Gábor Márk
Losev, Alexander
Goryanin, Igor
author_sort Medvedev, Ivan
collection PubMed
description Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD(5) is the amount of oxygen consumed in 5 days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this research is to apply microbial fuel cells (MFCs) to reduce the time requirement of BOD(5) measurements. An artificial neural network (ANN) has been created, and the predictions we obtained for BOD(5) measurements were carried out within 6–24 h with an average error of 7%. The outcomes demonstrated the viability of our AI MFC/BES BOD(5) sensor in real-life scenarios.
format Online
Article
Text
id pubmed-10403401
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Springer International Publishing
record_format MEDLINE/PubMed
spelling pubmed-104034012023-08-06 Using AI and BES/MFC to decrease the prediction time of BOD(5) measurement Medvedev, Ivan Kornaukhova, Mariya Galazis, Christoforos Lóránt, Bálint Tardy, Gábor Márk Losev, Alexander Goryanin, Igor Environ Monit Assess Research Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD(5) is the amount of oxygen consumed in 5 days by microorganisms that oxidize biodegradable organic materials in an aerobic biochemical manner. The primary objective of this research is to apply microbial fuel cells (MFCs) to reduce the time requirement of BOD(5) measurements. An artificial neural network (ANN) has been created, and the predictions we obtained for BOD(5) measurements were carried out within 6–24 h with an average error of 7%. The outcomes demonstrated the viability of our AI MFC/BES BOD(5) sensor in real-life scenarios. Springer International Publishing 2023-08-05 2023 /pmc/articles/PMC10403401/ /pubmed/37542117 http://dx.doi.org/10.1007/s10661-023-11576-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Research
Medvedev, Ivan
Kornaukhova, Mariya
Galazis, Christoforos
Lóránt, Bálint
Tardy, Gábor Márk
Losev, Alexander
Goryanin, Igor
Using AI and BES/MFC to decrease the prediction time of BOD(5) measurement
title Using AI and BES/MFC to decrease the prediction time of BOD(5) measurement
title_full Using AI and BES/MFC to decrease the prediction time of BOD(5) measurement
title_fullStr Using AI and BES/MFC to decrease the prediction time of BOD(5) measurement
title_full_unstemmed Using AI and BES/MFC to decrease the prediction time of BOD(5) measurement
title_short Using AI and BES/MFC to decrease the prediction time of BOD(5) measurement
title_sort using ai and bes/mfc to decrease the prediction time of bod(5) measurement
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10403401/
https://www.ncbi.nlm.nih.gov/pubmed/37542117
http://dx.doi.org/10.1007/s10661-023-11576-0
work_keys_str_mv AT medvedevivan usingaiandbesmfctodecreasethepredictiontimeofbod5measurement
AT kornaukhovamariya usingaiandbesmfctodecreasethepredictiontimeofbod5measurement
AT galazischristoforos usingaiandbesmfctodecreasethepredictiontimeofbod5measurement
AT lorantbalint usingaiandbesmfctodecreasethepredictiontimeofbod5measurement
AT tardygabormark usingaiandbesmfctodecreasethepredictiontimeofbod5measurement
AT losevalexander usingaiandbesmfctodecreasethepredictiontimeofbod5measurement
AT goryaninigor usingaiandbesmfctodecreasethepredictiontimeofbod5measurement

Ejemplares similares