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机器学习框架用于预测废水处理厂的能源消耗,该框架结合了重新采样和队列沙普利值分析
Kangrong Tang1, Anlei Wei2, Zixuan Wang1
1Xi'an Key Laboratory of Environmental Simulation and Ecological Health in the Yellow River Basin, College of Urban and Environmental Sciences, Northwest University, Xi'an 710127, China.
Bioresource technology
|June 11, 2025
概括
本研究介绍了一种机器学习框架,用于在废水处理厂 (WWTP) 中准确的能源预测. 该方法提高了模型的解释性和性能,提供了优化的能源管理策略.
科学领域:
- 环境工程 环境工程
- 机器学习应用 机器学习应用
- 废水处理技术 废水处理技术
背景情况:
- 能源消耗是废水处理厂 (WWTP) 的一个主要运营成本.
- 精确预测能源使用对于运营效率和降低成本至关重要.
- 现有的模型往往因数据不平衡和缺乏解释性而扎,这阻碍了实际应用.
研究的目的:
- 开发一个强大的和可解释的机器学习框架,用于预测WWTP中的能源消耗.
- 为了应对数据不平衡和能源预测中模型解释性差的挑战.
- 确定影响WWTP内能源消耗模式的关键因素.
主要方法:
- 整合了三种重新采样方法与队列Shapley增量解释 (SHAP).
- 应用随机低采样,采样权重为3 (SUS-3) 与随机森林模型相结合.
- 使用队列SHAP进行详细分析对能源消耗的变量影响.
主要成果:
- 开发的框架实现了0.928的确定系数和4.255的RMSE总能耗预测.
- 在单位能耗分类中,SUS-3提高了准确度和精度超过30%,并将模型不确定性降低了35%.
- 确定了氨-和生物化学氧需求对通风能量的稳定影响,其他参数的季节性变化.
结论:
- 拟议的机器学习框架为WWTP中的能源预测提供了可转移和可解释的解决方案.
- 这些发现强调了适应性策略的重要性,以基于已识别的模式来优化能源使用.
- 这种方法有助于更好地了解和管理废水处理操作中的能源消耗.
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