{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:34:10Z","timestamp":1760060050010,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The accurate dosing of polyaluminum chloride (PAC) is essential for achieving effective coagulation in drinking water treatment, yet conventional methods such as jar tests are limited in their responsiveness and operational efficiency. This study proposes a hybrid modeling framework that integrates artificial neural networks (ANN) with genetic algorithms (GA) to optimize PAC dosage under variable raw water conditions. Operational data from 400 jar test experiments, collected between 2022 and 2024 at the Yanahurco water treatment plant (Ecuador), were used to train an ANN model capable of predicting six post-treatment water quality indicators, including turbidity, color, and pH. The ANN achieved excellent predictive accuracy (R2 &gt; 0.95 for turbidity and color), supporting its use as a surrogate model within a GA-based optimization scheme. The genetic algorithm evaluated dosage strategies by minimizing treatment costs while enforcing compliance with national water quality standards. The results revealed a bimodal dosing pattern, favoring low PAC dosages (~4 ppm) during routine conditions and higher dosages (~12 ppm) when influent quality declined. Optimization yielded a 49% reduction in median chemical costs and improved color compliance from 52% to 63%, while maintaining pH compliance above 97%. Turbidity remained a challenge under some conditions, indicating the potential benefit of complementary coagulants. The proposed ANN\u2013GA approach offers a scalable and adaptive solution for enhancing chemical dosing efficiency in water treatment operations.<\/jats:p>","DOI":"10.3390\/computation13080179","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T06:37:47Z","timestamp":1754030267000},"page":"179","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence Optimization of Polyaluminum Chloride (PAC) Dosage in Drinking Water Treatment: A Hybrid Genetic Algorithm\u2013Neural Network Approach"],"prefix":"10.3390","volume":"13","author":[{"given":"Dar\u00edo Fernando","family":"Guam\u00e1n-Lozada","sequence":"first","affiliation":[{"name":"Grupo de Investigaci\u00f3n Estudios Interdisciplinarios, Facultad de Ingenier\u00eda, Universidad Nacional de Chimborazo, Av. Antonio Jos\u00e9 de Sucre km 1\u00bd v\u00eda Guano, Riobamba 060103, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4202-3633","authenticated-orcid":false,"given":"Lenin Santiago","family":"Orozco Cantos","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n Estudios Interdisciplinarios, Facultad de Ingenier\u00eda, Universidad Nacional de Chimborazo, Av. Antonio Jos\u00e9 de Sucre km 1\u00bd v\u00eda Guano, Riobamba 060103, Ecuador"}]},{"given":"Guido Patricio","family":"Santill\u00e1n Lima","sequence":"additional","affiliation":[{"name":"Grupo de Investigaci\u00f3n Estudios Interdisciplinarios, Facultad de Ingenier\u00eda, Universidad Nacional de Chimborazo, Av. Antonio Jos\u00e9 de Sucre km 1\u00bd v\u00eda Guano, Riobamba 060103, Ecuador"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2663-5808","authenticated-orcid":false,"given":"Fabian","family":"Arias Arias","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias, Escuela Superior Polit\u00e9cnica de Chimborazo (ESPOCH), Riobamba 060155, Ecuador"},{"name":"Department of Chemistry and Chemical Technologies, University of Calabria, Via P. Bucci, Cubo 9 15D, 87036 Arcavacata di Rende, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"ref_1","unstructured":"Assembly, U.G. (2025, June 21). Resolution Adopted by the General Assembly on 25 September 2015. Transforming Our World: The 2030 Agenda for Sustainable Development. Available online: http:\/\/www.un.org\/en\/development\/desa\/population\/migration\/generalassembly\/docs\/globalcompact\/A_RES_70_1_E.pdf."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tubon-Usca, G., Centeno, C., Pomasqui, S., Beneduci, A., and Arias, F.A. (2025). Enhanced Adsorption of Methylene Blue in Wastewater Using Natural Zeolite Impregnated with Graphene Oxide. Appl. Sci., 15.","DOI":"10.3390\/app15052824"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"114888","DOI":"10.1016\/j.watres.2019.114888","article-title":"Integrating water quality and operation into prediction of water production in drinking water treatment plants by genetic algorithm enhanced artificial neural network","volume":"164","author":"Zhang","year":"2019","journal-title":"Water Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3429","DOI":"10.1007\/s10098-024-02815-0","article-title":"Green flocculation for sustainable remediation of municipal landfill leachate using Parkia biglobosa extract: Optimization, mechanistic insights and implication for design","volume":"26","author":"Igwegbe","year":"2024","journal-title":"Clean Technol. Environ. Policy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"93","DOI":"10.37116\/revistaenergia.v20.n1.2023.562","article-title":"Prediction of the Optimal Dosage of Poly Aluminum Chloride for Coagulation in Drinking Water Treatment using Artificial Neural Networks","volume":"20","author":"Izquierdo","year":"2023","journal-title":"Rev. T\u00e9cnica Energ\u00eda"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1016\/S0160-4120(98)00073-7","article-title":"synthesis and speciation of polyaluminum chloride for water treatment","volume":"24","author":"Shen","year":"1998","journal-title":"Environ. Int."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1007\/s10163-019-00933-2","article-title":"PSO\u2013ANN-based prediction of cobalt leaching rate from waste lithium-ion batteries","volume":"22","author":"Ebrahimzade","year":"2020","journal-title":"J. Mater. Cycles Waste Manag."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gyparakis, S., Trichakis, I., and Diamadopoulos, E. (2024). Using Artificial Neural Networks to Predict Operational Parameters of a Drinking Water Treatment Plant (DWTP). Water, 16.","DOI":"10.3390\/w16192863"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104700","DOI":"10.1016\/j.jwpe.2023.104700","article-title":"Synthesis of polyaluminum chloride: Optimization of process parameters, characterization and performance investigation for water treatment","volume":"57","author":"Kassa","year":"2024","journal-title":"J. Water Process. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yateh, M., Lartey-Young, G., Li, F., Li, M., and Tang, Y. (2023). Application of Response Surface Methodology to Optimize Coagulation Treatment Process of Urban Drinking Water Using Polyaluminium Chloride. Water, 15.","DOI":"10.3390\/w15050853"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Gyparakis, S., Trichakis, I., Daras, T., and Diamadopoulos, E. (2025). Artificial Neural Networks (ANNs) and Multiple Linear Regression (MLR) Analysis Modelling for Predicting Chemical Dosages of a Water Treatment Plant (WTP) of Drinking Water. Water, 17.","DOI":"10.3390\/w17020227"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/j.engappai.2008.03.015","article-title":"Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system","volume":"21","author":"Wu","year":"2008","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_13","first-page":"208","article-title":"Predictive Modeling and Optimization of Plywood Drying: An Artificial Neural Network Approach","volume":"17","author":"Heredia","year":"2024","journal-title":"Recent Innov. Chem. Eng. (Former. Recent Pat. Chem. Eng.)"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"869","DOI":"10.2166\/wst.2018.263","article-title":"Chemical coagulation of greywater: Modelling using artificial neural networks","volume":"2017","author":"Vinitha","year":"2018","journal-title":"Water Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1007\/s40201-021-00710-0","article-title":"Prediction of the optimal dosage of coagulants in water treatment plants through developing models based on artificial neural network fuzzy inference system (ANFIS)","volume":"19","author":"Narges","year":"2021","journal-title":"J. Environ. Health Sci. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"4974","DOI":"10.1016\/j.eswa.2009.12.016","article-title":"Effects of data normalization and inherent-factor on decision of optimal coagulant dosage in water treatment by artificial neural network","volume":"37","author":"Wu","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tariq, R., Abatal, M., Vargas, J., and V\u00e1zquez-S\u00e1nchez, A.Y. (2024). Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-70989-0"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"69","DOI":"10.5004\/dwt.2017.21197","article-title":"Application of an artificial neural network\u2013genetic algorithm methodology for modelling and optimization of the improved biosorption of a chemically modified peat moss: Kinetic studies","volume":"84","author":"Sutherland","year":"2017","journal-title":"Desalination Water Treat."},{"key":"ref_19","first-page":"S389","article-title":"Artificial neural networks and genetic algorithms: An efficient modelling and optimization methodology for active chlorine production using the electrolysis process","volume":"99","author":"Shirkoohi","year":"2021","journal-title":"Can. J. Chem. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"11189","DOI":"10.1007\/s10668-022-02523-z","article-title":"Modeling and optimization of coagulant dosage in water treatment plants using hybridized random forest model with genetic algorithm optimization","volume":"25","author":"Achite","year":"2023","journal-title":"Environ. Dev. Sustain."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1007\/s12517-020-05940-4","article-title":"Performance prediction modeling of andesite processing wastewater physicochemical treatment via artificial neural network","volume":"13","author":"Yel","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"105390","DOI":"10.1016\/j.jaridenv.2025.105390","article-title":"Evaluation of machine-learning algorithms in estimation of relative water content of sorghum under different irrigated environments","volume":"229","author":"Dharshini","year":"2025","journal-title":"J. Arid Environ."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ji, X., Li, Z., Wang, M., Yuan, Z., and Jin, L. (2024). Response Surface Methodology Approach to Optimize Parameters for Coagulation Process Using Polyaluminum Chloride (PAC). Water, 16.","DOI":"10.3390\/w16111470"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"126673","DOI":"10.1016\/j.cej.2020.126673","article-title":"Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review","volume":"405","author":"Li","year":"2021","journal-title":"Chem. Eng. J."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"7014","DOI":"10.1007\/s11356-021-16265-4","article-title":"Predicting flocculant dosage in the drinking water treatment process using Elman neural network","volume":"29","author":"Wang","year":"2022","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_26","unstructured":"Instituto Ecuatoriano de Normalizaci\u00f3n (INEN) (2025, June 23). Drinking Water. Requirements, NTE INEN 1108:2014, 5th rev., Quito, Ecuador. Available online: https:\/\/www.insistec.ec\/images\/insistec\/02-cliente\/07-descargas\/NTE%20INEN%201108%20-%20AGUA%20POTABLE.%20REQUISITOS.pdf."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S1364-8152(99)00007-9","article-title":"Neural networks for the prediction and forecasting of water resources variables: A review of modelling issues and applications","volume":"15","author":"Maier","year":"2000","journal-title":"Environ. Model. Softw."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"106962","DOI":"10.1016\/j.jwpe.2025.106962","article-title":"A precise dosing system based on a coagulation reaction model for water treatment","volume":"70","author":"Huang","year":"2025","journal-title":"J. Water Process Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chatterjee, S., Sarkar, S., Dey, N., Sen, S., Goto, T., and Debnath, N.C. (2017, January 24\u201326). Water quality prediction: Multi objective genetic algorithm coupled artificial neural network based approach. Proceedings of the 2017 IEEE 15th International Conference on Industrial Informatics, INDIN 2017, Emden, Germany.","DOI":"10.1109\/INDIN.2017.8104902"},{"key":"ref_30","first-page":"155","article-title":"Use of ANFIS\/Genetic Algorithm and Neural Network to Predict Inorganic Indicators of Water Quality","volume":"54","author":"Mohadesi","year":"2020","journal-title":"J. Chem. Pet. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1016\/S1665-6423(14)71629-3","article-title":"The Use of Combined Neural Networks and Genetic Algorithms for Prediction of River Water Quality","volume":"12","author":"Ding","year":"2014","journal-title":"J. Appl. Res. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4789","DOI":"10.1007\/s11069-023-06387-y","article-title":"Combining artificial neural networks and genetic algorithms to model nitrate contamination in groundwater","volume":"120","author":"Gholami","year":"2024","journal-title":"Nat. Hazards"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3395","DOI":"10.1007\/s10668-022-02835-0","article-title":"Modeling the optimal dosage of coagulants in water treatment plants using various machine learning models","volume":"26","author":"Achite","year":"2024","journal-title":"Environ. Dev. Sustain."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"105784","DOI":"10.1016\/j.jwpe.2024.105784","article-title":"Development of long short-term memory along with differential optimization and neural networks for coagulant dosage prediction in water treatment plant","volume":"65","author":"Sharafi","year":"2024","journal-title":"J. Water Process Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1007\/s13201-017-0541-5","article-title":"Prediction of settled water turbidity and optimal coagulant dosage in drinking water treatment plant using a hybrid model of k-means clustering and adaptive neuro-fuzzy inference system","volume":"7","author":"Kim","year":"2017","journal-title":"Appl. Water Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"01012","DOI":"10.1051\/e3sconf\/202127201012","article-title":"Study of PFS and PAC coagulation effect on Pi River Water","volume":"272","author":"Wang","year":"2021","journal-title":"E3S Web Conf."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"04006","DOI":"10.1051\/e3sconf\/20185304006","article-title":"Experimental Research on Removal of Turbidity and UV254 by Poly-aluminum Chloride (PAC)","volume":"53","author":"Jia","year":"2018","journal-title":"E3S Web Conf."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Campinas, M., Viegas, R.M.C., Coelho, R., Lucas, H., and Rosa, M.J. (2021). Adsorption\/Coagulation\/Ceramic Microfiltration for Treating Challenging Waters for Drinking Water Production. Membranes, 11.","DOI":"10.3390\/membranes11020091"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"117550","DOI":"10.1016\/j.watres.2021.117550","article-title":"Differences in removal rates of virgin\/decayed microplastics, viruses, activated carbon, and kaolin\/montmorillonite clay particles by coagulation, flocculation, sedimentation, and rapid sand filtration during water treatment","volume":"203","author":"Nakazawa","year":"2021","journal-title":"Water Res."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"100222","DOI":"10.1016\/j.wroa.2024.100222","article-title":"Scattered and transmitted light as surrogates for activated carbon residual in advanced wastewater treatment processes: Investigating the influence of particle size","volume":"23","author":"Kirchen","year":"2024","journal-title":"Water Res. X"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sun, H., Zhou, Y., Du, M., and Du, Z. (2024). Enhanced Removal of Refractory Organic Compounds from Coking Wastewater Using Polyaluminum Chloride with Coagulant Aids. Water, 16.","DOI":"10.3390\/w16182662"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.seppur.2008.02.014","article-title":"Effect of polyaluminum chloride on enhanced softening for the typical organic-polluted high hardness North-China surface waters","volume":"62","author":"Yan","year":"2008","journal-title":"Sep. Purif. Technol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111624","DOI":"10.1016\/j.jece.2023.111624","article-title":"Treatment of low-turbidity water by coagulation combining Moringa oleifera Lam and polyaluminium chloride (PAC)","volume":"12","author":"Balbinoti","year":"2024","journal-title":"J. Environ. Chem. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.psep.2022.11.085","article-title":"Understanding synergistic mechanisms of silicate decorated polyaluminium chloride and organic polymer flocculation for enhancing polymer-flooding wastewater treatment","volume":"170","author":"Wu","year":"2023","journal-title":"Process Saf. Environ. Prot."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"108450","DOI":"10.1016\/j.jece.2022.108450","article-title":"Performance improvement and mechanism of composite PAC\/PDMDAAC coagulant via enhanced coagulation coupled with rapid sand filtration in the treatment of micro-polluted surface water","volume":"10","author":"Olukowi","year":"2022","journal-title":"J. Environ. Chem. Eng."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Al-Jadabi, N., Laaouan, M., El Hajjaji, S., Mabrouki, J., Benbouzid, M., and Dhiba, D. (2023). The Dual Performance of Moringa Oleifera Seeds as Eco-Friendly Natural Coagulant and as an Antimicrobial for Wastewater Treatment: A Review. Sustainability, 15.","DOI":"10.3390\/su15054280"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Alenezi, A., and Alabaiadly, Y. (2025). Artificial Intelligence Applications in Water Treatment and Desalination: A Comprehensive Review. Water, 17.","DOI":"10.1016\/j.nexus.2025.100373"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1515\/auto-2024-0023","article-title":"Challenges and requirements of AI-based waste water treatment systems","volume":"73","author":"Dalibard","year":"2025","journal-title":"At-Automatisierungstechnik"},{"key":"ref_49","first-page":"4284","article-title":"AI-Driven Optimization of Water Usage and Waste Management in Smart Cities for Environmental Sustainability","volume":"10","author":"Ojadi","year":"2025","journal-title":"Eng. Technol. J."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Lowe, M., Qin, R., and Mao, X. (2022). A Review on Machine Learning, Artificial Intelligence, and Smart Technology in Water Treatment and Monitoring. Water, 14.","DOI":"10.3390\/w14091384"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.jhazmat.2008.07.090","article-title":"Application of response surface methodology (RSM) to optimize coagulation\u2013flocculation treatment of leachate using poly-aluminum chloride (PAC) and alum","volume":"163","author":"Ghafari","year":"2009","journal-title":"J. Hazard. Mater."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1186\/s13568-018-0702-4","article-title":"Optimization and economic evaluation of modified coagulation\u2013flocculation process for enhanced treatment of ceramic-tile industry wastewater","volume":"8","author":"Mahmudabadi","year":"2018","journal-title":"AMB Express"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"9","DOI":"10.22430\/22565337.1085","article-title":"Evaluaci\u00f3n de FeCl3 y PAC para la potabilizaci\u00f3n de agua con alto contenido de color y baja turbiedad","volume":"22","year":"2019","journal-title":"TecnoL\u00f3gicas"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e1789","DOI":"10.22430\/22565337.1789","article-title":"Sedimentabilidad de part\u00edculas floculentas en aguas con alto contenido de color y baja turbiedad, coaguladas con FeCl3 + PAC versus PAC","volume":"24","author":"Montenegro","year":"2021","journal-title":"TecnoL\u00f3gicas"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/8\/179\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:20:31Z","timestamp":1760034031000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/13\/8\/179"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,1]]},"references-count":54,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["computation13080179"],"URL":"https:\/\/doi.org\/10.3390\/computation13080179","relation":{},"ISSN":["2079-3197"],"issn-type":[{"type":"electronic","value":"2079-3197"}],"subject":[],"published":{"date-parts":[[2025,8,1]]}}}