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.
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Acknowledgements
This research was supported by the National Key R&D Program of China (No. 2021YFC1809001).
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Conflict of Interests The author Yongzhen Peng is Editorial Board Member of Frontiers of Environmental Science & Engineering. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Highlights
• A novel brain-inspired network accurately predicts sewage effluent quality.
• Sewage-surface images are utilized in data analysis by the model.
• The developed method outperforms traditional ones by reducing error by 23%.
• The model offers the potential for cost-effective monitoring.
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Li, J., Lin, S., Zhang, L. et al. Brain-inspired multimodal approach for effluent quality prediction using wastewater surface images and water quality data. Front. Environ. Sci. Eng. 18, 31 (2024). https://doi.org/10.1007/s11783-024-1791-x
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DOI: https://doi.org/10.1007/s11783-024-1791-x