应对预测废水处理厂的废水特性方面的数据挑战
Ali Mohammad Roohi1, Sara Nazif1, Pouria Ramazi2
1School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Journal of environmental management
|February 16, 2024
概括
准确的废水处理预测需要了解数据需求. 关键变量包括流速和温度,至少有250个样本,缺失值不足10%,对于可靠的废水质量预测至关重要.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 废水处理技术 废水处理技术
背景情况:
- 废水处理厂 (WWTP) 的废水质量因不同的影响和运行条件而波动.
- 数据驱动的模型可以预测废水质量,从而能够及时进行操作调整.
- 开发这些模型面临着不完整数据,传感器成本和模型选择的挑战.
研究的目的:
- 为了应对数据挑战,开发精确的数据驱动的WWTP废水预测器.
- 确定关键变量,最低数据要求和可容忍的缺失数据百分比.
- 评估和选择最适合的机器学习模型用于废水预测.
主要方法:
- 测试了9个机器学习模型,使用来自3个国际WWTP的数据.
- 确定了关键的预测变量:流速,悬浮固体总量,电导率,和温度.
- 利用表现最好的模型 (贝叶斯网络) 来分析数据需求.
主要成果:
- 流速,悬浮固体总量,电导率,,废水温度和空气温度是关键变量.
- 需要至少250个数据样本才能在培训期间显著减少错误.
- 当缺失值超过10%时,预测误差会急剧增加.
结论:
- 提供了对WWTP废水预测数据收集策略的关键见解.
- 帮助工厂经理优化数据采集工作,平衡成本和准确性.
- 通过增强的预测建模,有助于改善废水质量管理.
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