废水处理厂的废水质量软传感器与集体稀疏的基于学习的在线下一代水库计算
Gang Fang1,2, Daoping Huang1,2, Zhiying Wu3
1Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, School of Automation Science &Engineering, South China University of Technology, Guangzhou, 510640, China.
Water research X
|December 4, 2024
概括
本研究介绍了一种新的稀疏在线方法,用于下一代水库计算 (NG-RC),以提高废水处理厂 (WWTP) 的实时质量预测. 该方法提高了对噪声的准确性和稳定性,这对于稳定的工厂运行至关重要.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 废水质量的实时监测对于废水处理厂 (WWTP) 的安全和稳定的运行至关重要.
- 下一代储计算 (NG-RC) 显示出预测COD和BOD等质量变量的前景,作为数据驱动的软传感器.
- 传统的NG-RC模型在线训练与动态场景作斗争,导致性能下降.
研究的目的:
- 提出一种稀疏的在线NG-RC方法,用于WWTP中的实时质量预测.
- 为了应对动态环境中的模型退化,并减轻测量噪声.
- 开发一种有效的软传感器,用于持续监测质量指标.
主要方法:
- 以伍德伯里矩阵身份为灵感的增量策略被开发用于NG-RC输出权重的在线学习.
- 整体稀疏策略被整合,以减少预测模型中的过拟合.
- 使用集体稀疏在线NG-RC实现软传感器,用于实时预测废水质量.
主要成果:
- 拟议的稀疏在线NG-RC方法在WWTP实时质量预测方面表现出有效性.
- 与传统方法相比,该方法对动态变化和测量噪声的稳定性得到了改善.
- 使用来自两个实际WWTP的数据集的验证证实了该模型的实际适用性和性能.
结论:
- 开发的集体稀疏在线NG-RC提供了一个强大的和准确的解决方案,用于实时质量监测WWTPs.
- 这种数据驱动的软传感器方法通过及时预测质量,提高了运营稳定性和安全性.
- 这些发现在将先进的机器学习技术应用于环境工程挑战方面取得了重大进展.
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