Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data

IF 6.1 2区 环境科学与生态学 Q2 ENGINEERING, ENVIRONMENTAL Frontiers of Environmental Science & Engineering Pub Date : 2023-11-10 DOI:10.1007/s11783-024-1791-x
Junchen Li, Sijie Lin, Liang Zhang, Yuheng Liu, Yongzhen Peng, Qing Hu
{"title":"Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data","authors":"Junchen Li, Sijie Lin, Liang Zhang, Yuheng Liu, Yongzhen Peng, Qing Hu","doi":"10.1007/s11783-024-1791-x","DOIUrl":null,"url":null,"abstract":"<p>Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations. In this study, we aimed to develop an integrated method for predicting effluent COD and NH<sub>3</sub> levels. We employed a 200 L pilot-scale sequencing batch reactor (SBR) to gather multimodal data from urban sewage over 40 d. Then we collected data on critical parameters like COD, DO, pH, NH<sub>3</sub>, EC, ORP, SS, and water temperature, alongside wastewater surface images, resulting in a data set of approximately 40246 points. Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network (BITF-CL) using this data. This innovative model synergized sewage imagery with water quality data, enhancing prediction accuracy. As a result, the BITF-CL model reduced prediction error by over 23% compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data. Consequently, this research presents a cost-effective and precise prediction system for sewage treatment, demonstrating the potential of brain-inspired models.\n</p>","PeriodicalId":12720,"journal":{"name":"Frontiers of Environmental Science & Engineering","volume":"230 1","pages":""},"PeriodicalIF":6.1000,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers of Environmental Science & Engineering","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s11783-024-1791-x","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

Efficiently predicting effluent quality through data-driven analysis presents a significant advancement for consistent wastewater treatment operations. In this study, we aimed to develop an integrated method for predicting effluent COD and NH3 levels. We employed a 200 L pilot-scale sequencing batch reactor (SBR) to gather multimodal data from urban sewage over 40 d. Then we collected data on critical parameters like COD, DO, pH, NH3, EC, ORP, SS, and water temperature, alongside wastewater surface images, resulting in a data set of approximately 40246 points. Then we proposed a brain-inspired image and temporal fusion model integrated with a CNN-LSTM network (BITF-CL) using this data. This innovative model synergized sewage imagery with water quality data, enhancing prediction accuracy. As a result, the BITF-CL model reduced prediction error by over 23% compared to traditional methods and still performed comparably to conventional techniques even without using DO and SS sensor data. Consequently, this research presents a cost-effective and precise prediction system for sewage treatment, demonstrating the potential of brain-inspired models.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用废水表面图像和水质数据进行污水水质预测的脑启发多模式方法
通过数据驱动的分析来有效预测出水水质,这对污水处理的稳定运行具有重大意义。在本研究中,我们旨在开发一种预测出水 COD 和 NH3 水平的综合方法。然后,我们收集了 COD、DO、pH、NH3、EC、ORP、SS 和水温等关键参数的数据,以及污水表面图像,形成了约 40246 个点的数据集。然后,我们利用这些数据提出了一个与 CNN-LSTM 网络(BITF-CL)集成的脑启发图像和时间融合模型。这一创新模型将污水图像与水质数据进行了协同,从而提高了预测精度。因此,与传统方法相比,BITF-CL 模型减少了 23% 以上的预测误差,即使不使用溶解氧和 SS 传感器数据,其性能仍可与传统技术媲美。因此,这项研究为污水处理提供了一个经济高效的精确预测系统,展示了大脑启发模型的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Frontiers of Environmental Science & Engineering
Frontiers of Environmental Science & Engineering ENGINEERING, ENVIRONMENTAL-ENVIRONMENTAL SCIENCES
CiteScore
10.90
自引率
12.50%
发文量
988
审稿时长
6.1 months
期刊介绍: Frontiers of Environmental Science & Engineering (FESE) is an international journal for researchers interested in a wide range of environmental disciplines. The journal''s aim is to advance and disseminate knowledge in all main branches of environmental science & engineering. The journal emphasizes papers in developing fields, as well as papers showing the interaction between environmental disciplines and other disciplines. FESE is a bi-monthly journal. Its peer-reviewed contents consist of a broad blend of reviews, research papers, policy analyses, short communications, and opinions. Nonscheduled “special issue” and "hot topic", including a review article followed by a couple of related research articles, are organized to publish novel contributions and breaking results on all aspects of environmental field.
期刊最新文献
Spatio-temporal characteristics of genotoxicity in the Yangtze River under the background of COVID-19 pandemic Pollution characteristics and ecological risk assessment of glucocorticoids in the Jiangsu section of the Yangtze River Basin Aquatic photo-transformation and enhanced photoinduced toxicity of ionizable tetracycline antibiotics Application of nanozymes in problematic biofilm control: progress, challenges and prospects Three-dimensional electro-Fenton system with iron-carbon packing as a particle electrode for nitrobenzene wastewater treatment
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1