通过机器学习预测和优化废水处理过程中的现场过剩污泥
Jie Zhang1, Shiqi Liu2, Wanlai Xue3
1Beijing Water Science and Technology Institute, Beijing 100048, China; School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China.
Bioresource technology
|August 22, 2025
概括
机器学习模型准确地预测了现场过量污泥的减少. 溶解密度增长,特别是超声波,是优化污泥减少和废水质量的最有效技术.
科学领域:
- 环境工程
- 废水处理
- 机器学习应用
背景情况:
- 过度污泥生产是污水处理的一个重大挑战.
- 现有的现场污泥减少技术包括解代谢,溶解密码生长和微生物掠食.
- 大数据集与工业需要有效的污泥减少策略之间存在差距.
研究的目的:
- 使用机器学习分析现场过剩的污泥减少.
- 确定影响污泥减少效率的关键因素.
- 确定最佳的污泥减少技术和工业应用的条件.
主要方法:
- 使用机器学习,特别是随机森林,用于预测建模 (R2 = 0.8).
- 使用SHapley添加式扩展 (SHAP) 来识别重要的特征.
- 应用部分依赖图 (PDP) 来评估技术性能和优化条件.
主要成果:
- 随机森林模型实现了高预测准确度 (R2 = 0.8).
- 确定了关键因素:处理剂量,试剂类型,污泥保留时间和处理能量.
- 溶解密码生长被确定为最有效的技术,超声波和超声波脱是最佳的溶解方法.
结论:
- 机器学习为分析复杂的污泥减少过程提供了强大的工具.
- 在特定条件下 (15-100%的循环,0.36-1.8瓦/毫升的能量,0-15分钟的时间,4-5毫克/毫升的剂量) 的溶解密度增长可以显著减少污泥并改善废水质量.
- 这项研究为优化现场污泥减少在实际废水处理应用提供了科学基础.
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