基于机器学习的对中国污水处理厂的电力消耗进行基准测试
Minjian Li1, Chongqiao Tang1, Junhan Gu1
1State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, Jiangsu, PR China.
Water research X
|February 24, 2025
概括
污水处理厂 (WWTP) 的电力消耗的基准测试对于可持续性至关重要. 一种新的机器学习方法有效地描述了影响质量,从而实现了强大的基准,并确定了影响能源使用的关键因素.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
背景情况:
- 废水处理厂 (WWTP) 的电力消耗是主要的运营成本.
- 准确的基准测试受阻于在特征变量影响水质的挑战.
- 可持续的废水管理需要高效的能源使用和强大的绩效指标.
研究的目的:
- 为WWTP电力消耗制定可靠的基准.
- 克服传统影响力质量评估的局限性.
- 确定影响WWTPs能源使用的关键因素.
主要方法:
- 应用光谱聚类,一种无监督的机器学习算法,分析来自中国2576个WWTP的数据.
- 将复杂的废水组成描述为一个单一的影响质量变量.
- 建立了基于规模,影响质量,排放标准和处理过程的基准.
主要成果:
- 实现了强大的基准,75%的配件显示R2 > 0.85.
- 确定影响质量作为区域WWTP每体积单位 (UEC) 电力消耗的主要驱动因素.
- 阐述了影响参数的区域差异及其对能源消耗的影响.
结论:
- 该研究提供了一个可扩展的模型,用于理解和比较WWTP的电力消耗.
- 频谱聚类为解决废水分析中的多变量挑战提供了一种新的方法.
- 这些发现有助于更好地理解WWTP中的能源消耗模式,以改善可持续管理.
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