Main content area

Data-driven performance analyses of wastewater treatment plants: A review

Newhart, Kathryn B., Holloway, Ryan W., Hering, Amanda S., Cath, Tzahi Y.
Water research 2019 v.157 pp. 498-513
autocorrelation, cost effectiveness, data collection, fuzzy logic, labor force, neural networks, prediction, process control, wastewater treatment
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.