用于微生物燃料电池的渗滤液床反应器中挥发性脂肪酸生成的优化和建模

IF 1.7 4区环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES

Water and Environment Journal Pub Date : 2023-03-12 DOI:10.1111/wej.12861

R. Gurjar, M. Behera

{"title":"用于微生物燃料电池的渗滤液床反应器中挥发性脂肪酸生成的优化和建模","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":"{\"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}","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

摘要

在浸出床反应器（LBR）中，厨房垃圾产酸产生的富含挥发性脂肪酸（VFA）的渗滤液被用于土壤微生物燃料电池（EMFC）发电。有机负荷率的影响（OLR，5-10 g VS/L·day）和pH（5–7）对LBR的影响，列举了OLR的优化参数（10 g VS/L·day）和pH值（5.74），得到7.7的总VFA（TVFA） ± 渗滤液中0.3 g/L，乙酸的贡献最大。将从LBR中获得的渗滤液加入具有不同OLR的EMFC（2–7 公斤 COD/m3·d）。最高功率密度为0.76 W/m3（OLR 7 公斤 COD／m3·d），VFA含量较高。基于Levenberg-Marquard函数的神经网络有效地预测了化学需氧量和TVFA的去除。本研究将LBR确立为一种获得EMFC有用基质的技术经济方法。此外，EMFC的响应建模证明了在生物处理中利用机器学习的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Optimization and modelling of volatile fatty acid generation in a leachate bed reactor for utilization in microbial fuel cells

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Water and Environment Journal 环境科学-湖沼学

CiteScore

4.80

自引率

0.00%

发文量

审稿时长

18-36 weeks

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