可解释的因果机器学习优化工具,以提高内部碳源生物脱的效率
Shiqi Liu1, Zeqing Long2, Jinsong Liang1
1School of Energy & Environmental Engineering, Hebei University of Technology, Tianjin 300401, China.
Bioresource technology
|November 10, 2024
概括
可解释的因果机器学习准确预测了脱性能. 确定了HRT和C/N比率等关键因素,以优化废水处理中的去除.
科学领域:
- 环境科学 环境科学
- 水处理工程水处理工程
- 机器学习应用 机器学习应用
背景情况:
- 脱对于从废水中去除来说至关重要.
- 优化脱化需要理解操作参数和性能之间的复杂关系.
- 现有的模型在识别关键影响因素时可能缺乏可解释性.
研究的目的:
- 使用可解释因果机器学习 (ICML) 预测脱性能.
- 确定和澄清关键因素与脱效率之间的关系.
- 为了确定最佳的操作范围,以提高去除效果.
主要方法:
- 可解释因果机器学习 (ICML) 框架的应用.
- 评估多个机器学习模型,包括XG-Boost.
- 利用挖掘点和部分依赖分析进行因子调节.
主要成果:
- XG-Boost模型实现了最高的预测准确性 (R2 = 0.8743).
- 确定了液压保留时间 (HRT),COD/TN比 (C/N),COD度和预处理技术是关键因素.
- 确定了最佳范围:HRT (6-10.5小时),C / N (6-12),和COD (300-600毫克L-1),产生73-77%的TN去除.
结论:
- ICML为预测和理解脱过程提供了一个有效的框架.
- 优化的操作条件显著提高了总去除效率.
- 这些发现支持使用多余的污泥作为内部碳源来增强脱.
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