在污水污泥堆肥中解读氨排放和堆肥参数之间的动态相互作用,使用多阶段机器学习
Jie Liu1, Weiguang Li1, Guangchun Shan2
1National Engineering Research Center for Safe Disposal and Resources Recovery of Sludge, Harbin Institute of Technology, Harbin, 150090, China; State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China.
Journal of environmental management
|March 2, 2026
概括
机器学习模型准确地预测了污水污泥堆肥过程中的氨排放. 特定阶段的分析揭示了针对性缓解策略的时间,通风和pH等关键因素.
科学领域:
- 环境科学 环境科学
- 废物管理 废物管理
- 计算科学 计算科学
背景情况:
- 污水污泥堆肥过程中的氨 (NH3) 排放是复杂的,传统方法难以控制.
- 堆肥参数之间的非线性相互作用阻碍了有效的,特定阶段的NH3排放缓解.
研究的目的:
- 应用多阶段机器学习来分析堆肥参数和累积氨 (cNH3) 排放之间的关系.
- 开发特定阶段的模型,以便在整个堆肥过程中预测和理解NH3排放.
主要方法:
- 利用多阶段机器学习模型来预测累积的NH3-N排放 (cNH3).
- 采用沙普利增量解释 (SHAP) 分析来确定影响关键参数.
- 生成双变量部分依赖图,以可视化参数相互作用和最佳范围.
主要成果:
- 特定阶段的模型在独立的测试组中实现了高预测准确性 (R2 = 0.850.91).
- 在中性/热性阶段,堆肥时间,通风率和pH值至关重要.
- 在冷却/成熟阶段,通风率,pH值和有机物质占主导地位.
- 确定了有机物和酸盐水平之间的协同效应,与减少的cNH3.3相关.
结论:
- 机器学习有效地模拟了堆肥参数和NH3排放之间的动态关系.
- 这些发现为制定有针对性的,特定阶段的战略提供了基础,以尽量减少污水污泥堆肥中的氨排放.
- 了解不断变化的参数影响是优化堆肥效率和环境影响的关键.
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