Ivan Medvedev
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Ivan Medvedev has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Alexander Georgievich Losev
Volgograd State University
1 shared publication - Bálint Lóránt
Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, Budapest, Hungary
1 shared publication - Christopher Galazis
MMWR LTD, UK, UK
1 shared publication - Gábor Márk Tardy
Budapest University of Technology and Economics, Budapest, Hungary
1 shared publication - Igor Goryanin
University of Edinburgh, UK
1 shared publication - Mariya Kornaukhova
Volgograd State University, Volgograd, Russia
1 shared publication
Latest Publications
- Open Access
- 5 August 2023
- Published by Springer Nature in Environmental Monitoring and Assessment
- Vol. 195 (9) , 1-15
Using AI and BES/MFC to decrease the prediction time of BOD5 measurement
Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD5 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 BOD5 measurements. An artificial neural network (ANN) has been created, and the predictions we obtained for BOD5 measurements were carried out within 6–24 h with an average error of 7%. The outcomes demonstrated the viability of our AI MFC/BES BOD5 sensor in real-life scenarios.
- 5 August 2023
- Published by Springer Nature in Environmental Monitoring and Assessment
- Vol. 195 (9) , 1-15
Open AccessUsing AI and BES/MFC to decrease the prediction time of BOD5 measurement
Biochemical oxygen demand (BOD) is one of the most important water/wastewater quality parameters. BOD5 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 BOD5 measurements. An artificial neural network (ANN) has been created, and the predictions we obtained for BOD5 measurements were carried out within 6–24 h with an average error of 7%. The outcomes demonstrated the viability of our AI MFC/BES BOD5 sensor in real-life scenarios.