机器学习用于建模废水处理厂的N2O排放:调整模型性能,复杂性和可解释性
Mostafa Khalil1, Ahmed AlSayed2, Yang Liu3
1Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada.
Water research
|October 1, 2023
概括
本研究引入了一种机器学习方法,用于精确在线建模废水处理厂 (WWTP) 排放的氧化 (N2O). 该方法平衡了准确性,速度和可解释性,用于实际应用和缓解指导.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 废水处理 废水处理
背景情况:
- 氧化 (N2O) 排放占污水处理厂 (WWTP) 碳足迹的很大一部分 (高达80%).
- 由于复杂的路径,机械模型难以准确地捕捉N2O排放动态.
- 数据驱动的方法为N2O预测提供了潜力,但对于WWTP缺乏全面的方法.
研究的目的:
- 开发和验证一套全面的机器学习方法,用于在线对WWTP中的N2O排放进行流程建模.
- 优先考虑模型的准确性,以及复杂性,计算速度和可解释性,以获得实际操作员的见解.
- 通过有效的特征选择,降低数据采集成本和计算负担.
主要方法:
- 利用了从一个全规模的WWTP中获得的长期N2O排放数据集.
- 实现并比较各种机器学习算法,包括k-Nearest Neighbors (kNN),决策树,合体学习 (AdaBoost) 和深度神经网络 (DNN).
- 应用了参数多变量异常值去除方法和特征选择来优化模型.
主要成果:
- 使用最好的模型实现了高预测准确性:AdaBoost (R2 = 0.94),DNN (R2 = 0.90) 和kNN (R2 = 0.88).
- 功能选择将功能数量减少了40%,降低了成本和计算负载,而不会影响准确性.
- 通过将特征重要性与流程知识进行比较来评估模型的可解释性.
结论:
- 开发的全面机器学习方法有效地模拟了WWTP中的N2O排放.
- 该方法为运营商提供了可解释的洞察力,用于明智的N2O减缓策略.
- 这种数据驱动的方法提高了在减少WWTP环境影响方面全面应用的潜力.
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