Multimodal Machine Learning Guides Low Carbon Aeration Strategies in Urban Wastewater Treatment

Abstract

The potential for reducing greenhouse gas (GHG) emissions and energy consumption in wastewater treatment can be realized through intelligent control, with machine learning (ML) and multimodality emerging as a promising solution. Here, we introduce an ML technique based on multimodal strategies, focusing specifically on intelligent aeration control in wastewater treatment plants (WWTPs). The generalization of the multimodal strategy is demonstrated on eight ML models. The results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control, exhibiting exceptional performance and interpretability. Integrating random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models, with a mean absolute percentage error of 4.4% and a coefficient of determination of 0.948. Practical testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8% compared to traditional fuzzy control methods. The potential application of these strategies in critical water science domains is discussed. To foster accessibility and promote widespread adoption, the multimodal ML models are freely available on GitHub, thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.

Keywords

• Wastewater treatment
• Multimodal machine learning
• Deep learning
• Aeration control
• Interpretable machine learning