Development of gradient boosting-assisted machine learning data-driven model for free chlorine residual prediction
Tóm tắt
Chlorine-based disinfection is ubiquitous in conventional drinking water treatment (DWT) and serves to mitigate threats of acute microbial disease caused by pathogens that may be present in source water. An important index of disinfection efficiency is the free chlorine residual (FCR), a regulated disinfection parameter in the US that indirectly measures disinfectant power for prevention of microbial recontamination during DWT and distribution. This work demonstrates how machine learning (ML) can be implemented to improve FCR forecasting when supplied with water quality data from a real, full-scale chlorine disinfection system in Georgia, USA. More precisely, a gradient-boosting ML method (CatBoost) was developed from a full year of DWT plant-generated chlorine disinfection data, including water quality parameters (e.g., temperature, turbidity, pH) and operational process data (e.g., flowrates), to predict FCR. Four gradient-boosting models were implemented, with the highest performance achieving a coefficient of determination, R2, of 0.937. Values that provide explanations using Shapley’s additive method were used to interpret the model’s results, uncovering that standard DWT operating parameters, although non-intuitive and theoretically non-causal, vastly improved prediction performance. These results provide a base case for data-driven DWT disinfection supervision and suggest process monitoring methods to provide better information to plant operators for implementation of safe chlorine dosing to maintain optimum FCR.
Tài liệu tham khảo
Abdullah M P, Yee L F, Ata S, Abdullah A, Ishak B, Abidin K N Z (2009). The study of interrelationship between raw water quality parameters, chlorine demand and the formation of disinfection byproducts. Physics and Chemistry of the Earth Parts A/B/C, 34(13–16): 806–811
André Felipe L, Fábio Cosme Rodrigues Dos S, Cleber Gustavo D (2018). Artificial neural networks to control chlorine dosing in a water treatment plant. Acta Scientiarum. Technology, 40(1): 1–9
Boulos P F (2017). Optimal scheduling of pipe replacement. Journal-American Water Works Association, 109(1): 42–46
Buysschaert B, Vermijs L, Naka A, Boon N, De Gusseme B (2018). Online flow cytometric monitoring of microbial water quality in a full-scale water treatment plant. npj Clean Water, 1(1): 16
Clark R M, Sivaganesan M (2002). Predicting chlorine residuals in drinking water: second order model. Journal of Water Resources Planning and Management, 128(2): 152–161
Crider Y, Sultana S, Unicomb L, Davis J, Luby S P, Pickering A J (2018). Can you taste it? Taste detection and acceptability thresholds for chlorine residual in drinking water in Dhaka, Bangladesh. Science of the Total Environment, 613–614: 840–846
Delpla I, Jung A V, Baures E, Clement M, Thomas O (2009). Impacts of climate change on surface water quality in relation to drinking water production. Environment International, 35(8): 1225–1233
Di Nardo A, Di Natale M, Greco R, Santonastaso G F (2014). Ant algorithm for smart water network partitioning. Procedia Engineering, 70: 525–534
Fish K, Osborn A M, Boxall J B (2017). Biofilm structures (EPS and bacterial communities) in drinking water distribution systems are conditioned by hydraulics and influence discolouration. Science of the Total Environment, 593–594: 571–580
Frateur I, Deslouis C, Kiene L, Levi Y, Tribollet B (1999). Free chlorine consumption induced by cast iron corrosion in drinking water distribution systems. Water Research, 33(8): 1781–1790
Fujioka T, Hoang A T, Aizawa H, Ashiba H, Fujimaki M, Leddy M (2018). Real-time online monitoring for assessing removal of bacteria by reverse osmosis. Environmental Science & Technology Letters, 5(6): 389–393
Gagnon G A, Rand J L, O’leary K C, Rygel A C, Chauret C, Andrews R C (2005). Disinfectant efficacy of chlorite and chlorine dioxide in drinking water biofilms. Water Research, 39(9): 1809–1817
Gang D C, Clevenger T E, Banerji K S (2003). Modeling chlorine decay in surface water. Journal of Environmental Informatics, 1(1): 21–27
Gao H, Zhong S, Zhang W, Igou T, Berger E, Reid E, Zhao Y, Lambeth D, Gan L, Afolabi M A, Tong Z, Lan G, Chen Y (2022). Revolutionizing membrane design using machine learning-Bayesian optimization. Environmental Science & Technology, 56(4): 2572–2581
Gray M J, Wholey W Y, Jakob U (2013). Bacterial responses to reactive chlorine species. Annual Review of Microbiology, 67(1): 141–160
Holzinger A, Goebel R, Fong R, Moon T, Müller K R, Samek W (2022). xxAI-beyond explainable artificial intelligence. In: Proceedings of International Workshop on Extending Explainable AI Beyond Deep Models and Classifiers, Vienna, Austria, 18 July 2020. Cham: Springer, 3–10
Hsu L H H, Hoque E, Kruse P, Ravi Selvaganapathy P (2015). A carbon nanotube based resettable sensor for measuring free chlorine in drinking water. Applied Physics Letters, 106(6): 063102
Li L, Rong S, Wang R, Yu S (2021). Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: a review. Chemical Engineering Journal, 405: 126673
Liu X (2016). Methods and Applications of Longitudinal Data Analysis. Oxford: Academic Press, 441–473
Lowe M, Qin R, Mao X (2022). A review on machine learning, artificial intelligence, and smart technology in water treatment and monitoring. Water (Basel), 14(9): 1384–1411
Mac Kenzie W R, Hoxie N J, Proctor M E, Gradus M S, Blair K A, Peterson D E, Kazmierczak J J, Addiss D G, Fox K R, Rose J B, et al. (1994). A massive outbreak in Milwaukee of Cryptosporidium infection transmitted through the public water supply. New England Journal of Medicine, 331(3): 161–167
Merrick L, Taly A (2020). The Explanation Game: Explaining Machine Learning Models Using Shapley Values. Cham: Springer International Publishing, 17–38
Onyutha C, Kwio-Tamale J C (2022). Modelling chlorine residuals in drinking water: a review. International Journal of Environmental Science and Technology, 19(11): 11613–11630
Powell J C, Hallam N B, West J R, Forster C F, Simms J (2000). Factors which control bulk chlorine decay rates. Water Research, 34(1): 117–126
Reid E, Igou T, Zhao Y, Crittenden J, Huang C H, Westerhoff P, Rittmann B, Drewes J E, Chen Y (2023). The minus approach can redefine the standard of practice of drinking water treatment. Environmental Science & Technology, 57(18): 7150–7161
Richardson S D, Kimura S Y (2020). Water analysis: emerging contaminants and current issues. Analytical Chemistry, 92(1): 473–505
Rittmann B E, Snoeyink V L (1984). Achieving biologically stable drinking water. Journal–American Water Works Association, 76(10): 106–114
Romano M, Kapelan Z, Savić D A (2014). Automated detection of pipe bursts and other events in water distribution systems. Journal of Water Resources Planning and Management, 140(4): 457–467
Saboe D, Hristovski K D, Burge S R, Burge R G, Taylor E, Hoffman D A (2021). Measurement of free chlorine levels in water using potentiometric responses of biofilms and applications for monitoring and managing the quality of potable water. Science of the Total Environment, 766: 144424
Sedlak D L, Von Gunten U (2011). The chlorine dilemma. Science, 331(6013): 42–43
Smeets P W M H, Medema G J, Van Dijk J C (2009). The Dutch secret: How to provide safe drinking water without chlorine in the Netherlands? Drinking Water Engineering and Science, 2(1): 1–14
Suffet I H, Corado A, Chou D, Mcguire M J, Butterworth S (1996). AWWA taste and odor survey. Journal-American Water Works Association, 88(4): 168–180
Sundararajan M, Najmi A (2020). The many Shapley values for model explanation. In: Hal D III, Aarti S, editors. Proceedings of Machine Learning Research. Brookline, MA, USA: 119, 9269-9278
Tinelli S, Juran I (2019). Artificial intelligence-based monitoring system of water quality parameters for early detection of nonspecific bio-contamination in water distribution systems. Water Science and Technology: Water Supply, 19(6): 1785–1792
Valdivia-Garcia M, Weir P, Graham D W, Werner D (2019). Predicted impact of climate change on trihalomethanes formation in drinking water treatment. Scientific Reports, 9(1): 9967
Warton B, Heitz A, Joll C, Kagi R (2006). A new method for calculation of the chlorine demand of natural and treated waters. Water Research, 40(15): 2877–2884
Wilson R E, Stoianov I, O’hare D (2019). Continuous chlorine detection in drinking water and a review of new detection methods. Johnson Matthey Technology Review, 63(2): 103–118
World Health Organization (2017). Principles and Practices of Drinking-Water Chlorination: a Guide to Strengthening Chlorination Practices in Small to Medium Sized Water Supplies. New Delhi: World Health Organization Regional Office for South-East Asia
Zhang B, Kotsalis G, Khan J, Xiong Z, Igou T, Lan G, Chen Y (2020a). Backwash sequence optimization of a pilot-scale ultrafiltration membrane system using data-driven modeling for parameter forecasting. Journal of Membrane Science, 612: 118464
Zhang K, Zhong S, Zhang H (2020b). Predicting aqueous adsorption of organic compounds onto biochars, carbon nanotubes, granular activated carbons, and resins with machine learning. Environmental Science & Technology, 54(11): 7008–7018
Zhong S, Lambeth D R, Igou T K, Chen Y (2022). Enlarging applicability domain of quantitative structure-activity relationship models through uncertainty-based active learning. ACS ES&T Engineering, 2(7): 1211–1220
Zhong S, Zhang K, Bagheri M, Burken J G, Gu A, Li B, Ma X, Marrone B L, Ren Z J, Schrier J, et al. (2021). Machine learning: new ideas and tools in environmental science and engineering. Environmental Science & Technology, 55(19): 12741–12754