基于机器学习的废弃活性污泥生成的预测,以优化WWTP的运行效率
Seongjun Yang1, Junyoung Kim2, Jiyoung Eom1
1Department of Energy and Environmental Engineering, The Catholic University of Korea, 43 Jibong-ro, Bucheon-si, Gyeonggi-do, Republic of Korea.
Environmental research
|October 8, 2025
概括
机器学习模型使用操作数据准确预测废弃活性污泥 (WAS) 生产. 综合管道优化了污泥减少,同时满足水质标准,提高了废水处理厂的效率.
科学领域:
- 环境工程 环境工程
- 水处理技术水处理技术
- 环境管理中的数据科学
背景情况:
- 精确预测废弃活性污泥 (WAS) 对于高效的废水处理厂 (WWTP) 运营和降低成本至关重要.
- 现有的方法往往缺乏精度,无法考虑工艺参数和污泥产生之间的复杂相互作用.
研究的目的:
- 开发和评估用于预测WAS生成的机器学习模型.
- 创建一个集成的管道,以优化污泥减少策略在废水限制下.
主要方法:
- 使用了随机森林,XGBoost和LightGBM模型,具有超参数优化和滑动窗移动平均值.
- 开发了一种集成的预测优化管道合 WAS 预测与 NSGA-II,以确定最佳固体保留时间 (SRT).
- 采用SHAP分析来确定关键的预测变量.
主要成果:
- XGBoost-Exp1模型实现了最高的预测准确度 (R2 = 0.911).
- 污水和影响的COD (化学氧气需求) 被确定为影响WAS生成的最重要因素.
- NSGA-II算法为SRT设定点生成了帕雷托边界,平衡了WAS减少和废水质量.
结论:
- 拟议的机器学习策略为WAS生成提供了高的预测准确性.
- 综合管道使WWTP运营的数据驱动优化能够提高效率和合规性.
- 该模型适用于实时预测系统,并为预测性维护策略提供信息.
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