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

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

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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.

Wastewater treatment systemWater quality predictionData driven analysisBrain-inspired modelMultimodal dataAttention mechanism

Junchen Li、Sijie Lin、Liang Zhang、Yuheng Liu、Yongzhen Peng、Qing Hu

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School of Environment,Harbin Institute of Technology,Harbin 150090,China

School of Environmental Science and Engineering,Southern University of Science and Technology,Shenzhen 518055,China

Engineering Innovation Center of SUSTech(Beijing),Southern University of Science and Technology,Beijing 100083,China

Faculty of Environment and Life,Beijing University of Technology,Beijing 100124,China

Engineering Research Center of Intelligence Perception and Autonomous Control,Ministry of Education,Beijing 100124,China

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国家重点研发计划

2021YFC1809001

2024

10.1007/s11783-024-1791-x

环境科学与工程前沿
高等教育出版社

环境科学与工程前沿

影响因子：0.545
ISSN：2095-2201
年,卷(期)：2024.18(3)
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