城市污泥热辅助生物干燥过程的机器学习建模
Kaiqiang Zhang1, Ningfung Wang2
1College of Mechanical Engineering, Qinghai University, Xining, Qinghai 810016, China.
Waste management (New York, N.Y.)
|August 11, 2024
概括
机器学习模型准确地预测城市污泥生物干燥过程中的水分比率和堆肥温度. 高斯过程回归 (GPR) 证明了卓越的性能,使活性炭生产的优化干燥过程成为可能.
科学领域:
- 环境科学 环境科学
- 化学工程是化学工程的重要组成部分.
- 数据科学数据科学数据科学
背景情况:
- 城市污泥 (MS) 的利用对于资源回收至关重要,活性炭生产是其中一个关键应用.
- 干燥是将MS转化为活性炭的重要预处理步骤.
- 热辅助生物干燥为MS治疗提供了一个有前途的方法.
研究的目的:
- 开发精确的机器学习 (ML) 模型,用于预测MS生物干燥过程中的水分比率 (MR) 和堆肥温度 (CT).
- 使用贝叶斯优化优化ML模型的超参数.
- 评估特征对模型可解释性的重要性.
主要方法:
- 六个ML模型被用来预测MR和CT.
- 贝叶斯优化用于超参数调整.
- 为了特征分析,使用了夏普利添加式拓展 (SHAP) 和部分依赖图 (PDP).
主要成果:
- 高斯过程回归 (GPR) 成为MR (R2=0.9967) 和CT (R2=0.9958) 预测中表现最好的模型.
- 优化的GPR模型实现了低RMSE值的高预测准确性.
- 为GPR模型开发了一个用户友好的图形用户界面 (GUI).
结论:
- 该研究成功地建立了基于ML的准确预测模型,用于MS生物干燥参数.
- GPR提供了一个强大的工具,用于实时监控和优化干燥过程.
- 开发的GUI为研究人员和工程师提供了模型的实际应用,有助于高效的MS管理.
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