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

Li，Junchen1,2; Lin，Sijie2,3; Zhang，Liang4,5; Liu，Yuheng4,5; Peng，Yongzhen4,5; Hu，Qing2,3

2024-03-01

10.1007/s11783-024-1791-x

Frontiers of Environmental Science and Engineering   影响因子和分区

2095-2201

2095-221X

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 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, 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.[Figure not available: see fulltext.].

Attention mechanism Brain-inspired model Data driven analysis Multimodal data Wastewater treatment system Water quality prediction

SCI ; EI

英语

通讯

2-s2.0-85179180638

Scopus

1.School of Environment,Harbin Institute of Technology,Harbin,150090,China
2.School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Engineering Innovation Center of SUSTech (Beijing),Southern University of Science and Technology,Beijing,100083,China
4.Faculty of Environment and Life,Beijing University of Technology,Beijing,100124,China
5.Engineering Research Center of Intelligence Perception and Autonomous Control,Ministry of Education,Beijing,100124,China

Li，Junchen,Lin，Sijie,Zhang，Liang,et al. Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data[J]. Frontiers of Environmental Science and Engineering,2024,18(3).

Li，Junchen,Lin，Sijie,Zhang，Liang,Liu，Yuheng,Peng，Yongzhen,&Hu，Qing.(2024).Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data.Frontiers of Environmental Science and Engineering,18(3).

Li，Junchen,et al."Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data".Frontiers of Environmental Science and Engineering 18.3(2024).