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

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.

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.

Graphical abstract

Keywords

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

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Acknowledgements

This research was supported by the National Key R&D Program of China (No. 2021YFC1809001).

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.

Data Accessibility Statement

Due to the sensitive nature of the data and software copyright restrictions, the data used in this study cannot be made publicly available.

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2024 The Author(s) 2024. This article is published with open access at link.springer.com and journal.hep.com.cn