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.

中文翻译：

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多模态机器学习指导城市污水处理中的低碳曝气策略

通过智能控制可以实现减少废水处理中温室气体（GHG）排放和能源消耗的潜力，其中机器学习（ML）和多模态作为一种有前途的解决方案而出现。在这里，我们介绍一种基于多模式策略的机器学习技术，特别关注废水处理厂（WWTP）中的智能曝气控制。多模式策略的泛化在八个机器学习模型上得到了证明。结果表明，这种多模式策略显着增强了环境科学中机器学习的模型指标和通气控制的效率，表现出卓越的性能和可解释性。将随机森林与视觉模型相结合，在多模态模型中实现了预测通气量的最高准确度，平均绝对百分比误差为 4.4%，决定系数为 0.948。在全尺寸工厂的实际测试表明，与传统的模糊控制方法相比，多模态模型可以降低运营成本 19.8%。讨论了这些策略在关键水科学领域的潜在应用。为了提高可访问性并促进广泛采用，多模式机器学习模型在 GitHub 上免费提供，从而消除技术障碍并鼓励人工智能在城市废水处理中的应用。