{"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.
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.