Application of machine learning at wastewater treatment facilities: a review of the science, challenges and barriers by level of implementation

ABSTRACTWastewater treatment facilities are complex environments with many unit treatment processes in series, in parallel, and connected by feedback loops. As such, addressing prediction, control, and optimisation problems within wastewater treatment facilities is challenging. Machine learning techniques provide powerful tools that can be applied to these challenges. Uncertainties of the treatment process can be quantified and navigated via probabilistic techniques inherent in machine learning. Despite the plethora of literature on the applications of ML techniques to many individual problems within wastewater treatment facilities, a paucity of information remains regarding how those applications can be organised. Hence, the objective of this paper is to provide a systematic review and novel break down of the organisation of ML applications into type and scope. Types of ML applications are classified as prediction, control, and optimisation, and each of these applications is further classified by scope of implementation, ranging from no ML (Level 0) to full facility (Level 4). Based on this analysis, the status of different types and scopes of ML applications is presented, and challenges and key knowledge gaps in ML applications for wastewater treatment facilities are identified. Results show that ML applications to date tend to be focused on prediction rather than control or optimisation, and that full facility applications are limited to prediction applications. However, this study also identified several control and optimisation applications that have demonstrated the ability of ML applications in these areas to balance optimisation of energy and chemical use with effluent quality.KEYWORDS: Machine learningwastewater treatmentpredictioncontroloptimisation AcknowledgmentThe authors acknowledge Peter Robertson for his assistance with graphics. This review was supported by the Stantec Institute for Water Technology and Policy.Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsSanaz ImenDr. Sanaz Imen is a Senior Research Advisor with Stantec. She has over 15 years of experience in the areas of stormwater management, water quality modelling, machine learning, and remote sensing. Dr. Imen has worked on many projects in academia and industry and has numerous publications. She currently serves as the elected member of the ASCE EWRI Total Maximum Daily Load Analysis and Modeling Task Committee, and the vice chair of the ASCE EWRI Remote Sensing Applications for TMDL Modeling Committee. Dr. Imen received her Ph.D. in Civil Engineering from the University of Central Florida, and her master's degree in Civil Engineering from the University of Tehran. She also holds a bachelor's degree in Civil Engineering from the University of Science and Culture, and she is a licensed professional engineer in the states of Washington and Florida.Henry C. CrollMr. Henry Croll is a researcher in the Stantec Institute for Water Technology and Policy, where he specialises in research on emerging contaminants within the One Water system and on the application of machine learning throughout the One Water system. He is passionate about leveraging fundamental research to better understand fundamental treatment processes, and in advancing applied research to carry improvements into real-world projects. Mr. Croll is also a Ph.D. student at Iowa State University, where his research focuses on investigating reinforcement learning agents for wastewater treatment process control optimisation. Mr. Croll received his bachelor's and master's degrees in Civil Engineering from the University of Minnesota and is a licensed professional engineer in the state of Iowa.Nicole L. McLellanMs. Nicolle McLellan is a process specialist with over 15 years of experience related to water quality and treatment in research, non-profit, government, post-secondary education, and consulting industries. Her areas of expertise comprise water treatment optimisation; regulatory compliance; process performance demonstrations; energy optimisation; control of algae blooms; scientific study designs; and wastewater disinfection. Ms. McLellan received her Hons. B.Sc. in Biology and M.A.Sc. in Civil Engineering from the University of Waterloo. She is pursuing her Ph.D. to improve the detection of waterborne pathogens and better inform risk assessments for water management.Mark BartlettDr. Mark Bartlett is an expert in innovating engineering analytics for extracting data insights and creating reliable machine learning models supported by engineering and science principles. His engineering work in data science is supported by his subject matter expertise and experience in climate change, hydraulic and hydrologic modelling, process engineering, stormwater management, agricultural and ecohydrological modelling, biological modelling, and the application of probability and statistics in science and engineering. As part of his research, he worked on the climate model of the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL). He is the author of numerous peer-reviewed publications and several publications presented at national and international conferences and invited talks. Dr. Bartlett received his PhD in Civil and Environmental Engineering from the Duke University. He also received his bachelor's degree in Engineering from Brown University and his master's degrees in Environmental Engineering from the University of Southern California. He is also a licensed professional engineer in the states of Massachusetts, New York, and California.Geno LehmanMr. Geno Lehman is a Vice President and part of the Applied Research Group within the Stantec Institute for Water Technology and Policy, focusing on identifying and evaluating emerging and innovative technologies and facilitating their integration into projects worldwide. Mr. Lehman is a registered professional civil engineer in California and has over 20 years of experience managing and conducting bench-scale, pilot-scale, and full-scale projects investigating water quality and regulatory compliance issues, and treatment technology development. Mr. Lehman is also an appointed Visiting Scholar at Johns Hopkins University School of Public Health as a part of the Stantec/JHU Alliance Partnership started in 2013.Joseph G. JacangeloDr. Joseph G. Jacangelo is a Vice President and Director of Research for Stantec. He has 35 years of experience in the field of environmental health engineering and has specialised in the areas of water quality and treatment, water and wastewater disinfection, membrane technology, water reuse and public health. He has served as Technical Director, Principal Investigator, Project Manager or Engineer for over 90 water and wastewater projects. Dr. Jacangelo has published numerous peer-reviewed technical papers and has held various positions within professional organisations. He is currently the President of the American Water Works Association (AWWA). Dr. Jacangelo is also a past Chair of the Board of Directors of the WateReuse Research Foundation and a past member of the editorial advisory board for the Journal of Water Reuse and Desalination. In addition to his role at Stantec, Dr. Jacangelo is an adjunct faculty member at the Johns Hopkins University Bloomberg School of Public Health in the Department of Environmental Health and Engineering. Dr. Jacangelo is a past recipient of the AWWA Golden Spigot Award and two AWWA Best Paper Awards. He also received that organisation's Volunteer of the Year of Award.