数据驱动的可区分模型用于废水处理中的动态预测和控制
Yun-Peng Song1, Wen-Zhe Wang2, Yu-Qi Wang2
1State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, PR China; School of Eco-Environmental, Harbin Institute of Technology, Shenzhen 518055, PR China.
Water research
|May 7, 2025
概括
使用神经普通微分方程 (神经ODE) 的新连续时间神经框架增强了废水处理建模. 这种方法提高了城市废水处理厂 (WWTP) 的运营效率和可持续性.
科学领域:
- 环境工程 环境工程
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 城市污水处理厂 (WWTP) 在运营效率和可持续性方面面临着挑战.
- 城市化和环境标准的严格化加剧了这些问题.
- 传统的建模方法很难有效地捕捉复杂的废水动态.
研究的目的:
- 引入一个创新的连续时间神经框架,用于改善污水处理过程的建模.
- 解决WWTP中运营效率和可持续发展的双重挑战.
- 为了减少废水处理建模中的计算需求和内存使用.
主要方法:
- 基于神经常规微分方程 (神经ODE) 的连续时间神经框架的实施.
- 分析了一年时间内从三个全面的WWTP中获取的运营数据.
- 集成与强化学习用于控制战略优化.
主要成果:
- 在降低计算需求 (95%的降低) 的情况下,实现了优异的预测准确性 (R2 > 0.95).
- 显著减少了内存使用 (从111.88-12,484.59 MB降至17.74-50.92 MB).
- 证明了强大的性能,数据缺失高达30%,通风能耗减少21.9%.
结论:
- 开发的框架为智能废水管理提供了一个新的范式.
- 优化运营效率,促进WWTP中的环境可持续性.
- 提供可解释的特征归属,用于发现新的过程洞察力.
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