通过结合深度学习和深度强化学习优化废水处理:最近的进展和未来的前景
Yusnhu Bai1, Zibing Li1, Jinqi Jiang2
1State Key Laboratory of Water Pollution Control and Green Resource Recycling, Shanghai Institute of Pollution Control and Ecological Security, College of Environmental Science and Engineering, Tongji University, Siping Road, Shanghai 200092, P. R. China.
Environmental research
|January 17, 2026
概括
人工智能 (AI),包括深度学习 (DL) 和深度强化学习 (DRL),可以优化废水处理厂 (WWTP). 这些先进的方法提高了性能,减少了排放,解决了可持续城市发展的运营挑战.
科学领域:
- 环境工程 环境工程
- 计算机科学 计算机科学
- 城市基础设施管理 城市基础设施管理
背景情况:
- 污水处理厂 (WWTP) 对城市可持续性至关重要,但面临着诸如影响力变化,监测有限,运营调整缓慢等挑战,阻碍了对排放标准的遵守.
- 提高WWTP的性能和减少碳排放是可持续城市发展的关键目标,需要为复杂的运营问题提供创新解决方案.
研究的目的:
- 审查人工智能 (AI) 的应用适用性和优势,特别是深度学习 (DL) 和深度强化学习 (DRL),以优化废水处理过程.
- 突出DL和DRL在应对故障检测,水质预测,自适应控制和WWTP多目标优化等挑战方面的潜力.
主要方法:
- 该审查澄清了与废水处理相关的DL和DRL的计算原则.
- 它分析了DL在特征提取和非线性过程配件 (例如,故障检测,预测) 和DLR在决策导向任务 (例如,自适应控制,优化) 中的优势.
主要成果:
- 在需要准确适配非线性和波动变量的任务中,DL擅长,例如故障检测和实时监控.
- 通过试错学习方法,DRL在适应性控制和WWTP操作的多目标优化方面展示了更大的潜力.
结论:
- 人工智能,特别是DL和DLR,为WWTP的创新流程优化提供了巨大的潜力,提高了性能和可持续性.
- 标准化的数据平台,开源模型和透明的方法对于智能系统在动态的WWTP环境中的实际,现实应用至关重要.
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