Using AI and BES/MFC to decrease the prediction time of BOD5 measurement
Open Access
- 5 August 2023
- journal article
- research article
- Published by Springer Nature in Environmental Monitoring and Assessment
- Vol. 195 (9) , 1-15
Abstract
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.Keywords
This publication has 18 references indexed in Scilit:
- A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classificationISPRS Journal of Photogrammetry and Remote Sensing, 2018
- Recourse recovery of bioenergy from cellulosic material in a microbial fuel cell fed with giant reed-loaded wastewaterBiofuels, 2017
- Microbial fuel cells for biosensor applicationsBiotechnology Letters, 2015
- Performance evaluation of microbial fuel cell by artificial intelligence methodsExpert Systems with Applications, 2014
- Optimizing multi-variables of microbial fuel cell for electricity generation with an integrated modeling and experimental approachApplied Energy, 2013
- Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soilsExpert Systems with Applications, 2011
- A two-population bio-electrochemical model of a microbial fuel cellBioresource Technology, 2010
- Model based evaluation of the effect of pH and electrode geometry on microbial fuel cell performanceBioelectrochemistry, 2010
- Microbial fuel cells: novel biotechnology for energy generationTrends in Biotechnology, 2005
- Multilayer perceptron, fuzzy sets, and classificationIEEE Transactions on Neural Networks, 1992