Department of Civil and Environmental Engineering, Colorado School of Mines, Golden, CO, 80401, USA.
Kennedy/Jenks Consultants, Rancho Cordova, CA, 95670, USA.
Water Res. 2019 Jun 15;157:498-513. doi: 10.1016/j.watres.2019.03.030. Epub 2019 Mar 21.
Recent advancements in data-driven process control and performance analysis could provide the wastewater treatment industry with an opportunity to reduce costs and improve operations. However, big data in wastewater treatment plants (WWTP) is widely underutilized, due in part to a workforce that lacks background knowledge of data science required to fully analyze the unique characteristics of WWTP. Wastewater treatment processes exhibit nonlinear, nonstationary, autocorrelated, and co-correlated behavior that (i) is very difficult to model using first principals and (ii) must be considered when implementing data-driven methods. This review provides an overview of data-driven methods of achieving fault detection, variable prediction, and advanced control of WWTP. We present how big data has been used in the context of WWTP, and much of the discussion can also be applied to water treatment. Due to the assumptions inherent in different data-driven modeling approaches (e.g., control charts, statistical process control, model predictive control, neural networks, transfer functions, fuzzy logic), not all methods are appropriate for every goal or every dataset. Practical guidance is given for matching a desired goal with a particular methodology along with considerations regarding the assumed data structure. References for further reading are provided, and an overall analysis framework is presented.
近年来，数据驱动的过程控制和性能分析方面的进展为废水处理行业提供了降低成本和改善运营的机会。然而，由于缺乏充分分析废水处理厂（WWTP）独特特性所需的数据科学背景知识，废水处理厂中的大数据尚未得到充分利用。废水处理过程表现出非线性、非平稳、自相关和互相关的行为，（i）很难使用第一原理进行建模，（ii）在实施数据驱动方法时必须加以考虑。本文综述了实现 WWTP 故障检测、变量预测和先进控制的数据驱动方法。我们介绍了大数据在 WWTP 背景下的应用，其中许多讨论也适用于水处理。由于不同数据驱动建模方法（例如，控制图、统计过程控制、模型预测控制、神经网络、传递函数、模糊逻辑）中的固有假设，并非所有方法都适用于每个目标或每个数据集。我们提供了将期望目标与特定方法相匹配的实用指导，并考虑了所假设的数据结构。提供了进一步阅读的参考文献，并提出了一个整体分析框架。
