{"title":"Optimization and modelling of volatile fatty acid generation in a leachate bed reactor for utilization in microbial fuel cells","authors":"R. Gurjar, M. Behera","doi":"10.1111/wej.12861","DOIUrl":null,"url":null,"abstract":"Volatile fatty acid (VFA)‐rich leachate generated from acidogenesis of kitchen waste in a leach bed reactor (LBR) was utilized in an earthen microbial fuel cell (EMFC) to generate electricity. Effects of organic loading rate (OLR, 5–10 g VS/L·day) and pH (5–7) on LBR enumerated optimized parameters of OLR (10 g VS/L·day) and pH (5.74) to obtain total VFA (TVFA) of 7.7 ± 0.3 g/L in the leachate, with maximum contribution from acetic acid. Leachate obtained from the LBR was fed to the EMFC with varying OLR (2–7 kg COD/m3·day). The highest power density of 0.76 W/m3 (at OLR 7 kg COD/m3·day) was obtained with higher VFA content in the leachate. A neural network based on the Levenberg–Marquard function effectively predicted chemical oxygen demand and TVFA removal. This study establishes LBR as a techno‐economic method to obtain useful substrate for EMFC. Furthermore, the response modelling of EMFC demonstrates the potential of utilizing machine learning in biological treatment.","PeriodicalId":23753,"journal":{"name":"Water and Environment Journal","volume":"37 1","pages":"581 - 593"},"PeriodicalIF":1.7000,"publicationDate":"2023-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Water and Environment Journal","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1111/wej.12861","RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Volatile fatty acid (VFA)‐rich leachate generated from acidogenesis of kitchen waste in a leach bed reactor (LBR) was utilized in an earthen microbial fuel cell (EMFC) to generate electricity. Effects of organic loading rate (OLR, 5–10 g VS/L·day) and pH (5–7) on LBR enumerated optimized parameters of OLR (10 g VS/L·day) and pH (5.74) to obtain total VFA (TVFA) of 7.7 ± 0.3 g/L in the leachate, with maximum contribution from acetic acid. Leachate obtained from the LBR was fed to the EMFC with varying OLR (2–7 kg COD/m3·day). The highest power density of 0.76 W/m3 (at OLR 7 kg COD/m3·day) was obtained with higher VFA content in the leachate. A neural network based on the Levenberg–Marquard function effectively predicted chemical oxygen demand and TVFA removal. This study establishes LBR as a techno‐economic method to obtain useful substrate for EMFC. Furthermore, the response modelling of EMFC demonstrates the potential of utilizing machine learning in biological treatment.
期刊介绍：
Water and Environment Journal is an internationally recognised peer reviewed Journal for the dissemination of innovations and solutions focussed on enhancing water management best practice. Water and Environment Journal is available to over 12,000 institutions with a further 7,000 copies physically distributed to the Chartered Institution of Water and Environmental Management (CIWEM) membership, comprised of environment sector professionals based across the value chain (utilities, consultancy, technology suppliers, regulators, government and NGOs). As such, the journal provides a conduit between academics and practitioners. We therefore particularly encourage contributions focussed at the interface between academia and industry, which deliver industrially impactful applied research underpinned by scientific evidence. We are keen to attract papers on a broad range of subjects including:
-Water and wastewater treatment for agricultural, municipal and industrial applications
-Sludge treatment including processing, storage and management
-Water recycling
-Urban and stormwater management
-Integrated water management strategies
-Water infrastructure and distribution
-Climate change mitigation including management of impacts on agriculture, urban areas and infrastructure
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