[{"command":"settings","settings":{"pluralDelimiter":"\u0003","suppressDeprecationErrors":true,"user":{"uid":0,"permissionsHash":"d9587e6f410d2e7f476e3da6cb10a457c78ab82347f962bf83d9020620f901dd"}},"merge":true},{"command":"add_css","data":[{"rel":"stylesheet","media":"all","href":"\/modules\/contrib\/addtocal\/addtocal.css?t2408i"},{"rel":"stylesheet","media":"all","href":"\/themes\/custom\/cest2025\/css\/components\/node.css?t2408i"}]},{"command":"add_js","selector":"body","data":[{"src":"\/core\/assets\/vendor\/jquery\/jquery.min.js?v=3.7.1"},{"src":"\/core\/assets\/vendor\/once\/once.min.js?v=1.0.1"},{"src":"\/core\/misc\/drupalSettingsLoader.js?v=10.5.1"},{"src":"\/core\/misc\/drupal.js?v=10.5.1"},{"src":"\/core\/misc\/drupal.init.js?v=10.5.1"},{"src":"\/modules\/contrib\/addtocal\/addtocal.js?v=10.5.1"},{"src":"\/modules\/contrib\/addtocal\/addtocal-download.js?v=10.5.1"}]},{"command":"openDialog","selector":"#drupal-modal","settings":null,"data":"\n\u003Carticle class=\u0022node node--type-presentation node--promoted node--view-mode-modal\u0022\u003E\n      \u003Cdiv\u003ESession 12 - Wastewater treatment (3)\u003C\/div\u003E\n  \n      \u003Cb\u003E\u003Cspan\u003EMachine Learning (ML) Applications in Water Treatment: Possibilities and Advantages\u003C\/span\u003E\n\u003C\/b\u003E\n  \n      \u003Cdiv\u003E\u003Cb\u003ECEST ID: cest2025_00235\u003C\/b\u003E\u003C\/div\u003E\n  \n        \u003Cdiv class=\u0022mb-3\u0022\u003E\n      \u003Cb\u003ERoom Aegle B | Thu 4 Sep 2025 | 16:45 - 16:55 pm\u003C\/b\u003E\n    \u003C\/div\u003E\n  \n          \n    \n  \n      \u003Cdiv class=\u0022mt-10\u0022\u003E\n            \u003Cdiv class=\u0022clearfix text-formatted field field--name-presentation-body field--type-text-long field--label-hidden field__item\u0022\u003EArtificial intelligence (AI), especially machine learning (ML) algorithms, has gained traction in water treatment processes (WTPs) for tasks such as process optimization, operational decision-making, and cost efficiency. Since 1997, at least 91 peer-reviewed studies have documented the use of AI in various WTP operations, including coagulation\/flocculation (41 studies), membrane filtration (21), formation of disinfection byproducts (DBPs) (13), adsorption (16), and other aspects of plant management. This paper critically reviews these studies to evaluate how AI technologies have been applied in WTPs, highlighting both advancements and current limitations. AI has contributed significantly to improving the accuracy of predictions related to coagulant dosage, membrane performance (flux, fouling, and rejection), DBP formation, and contaminant removal. Notably, deep learning (DL) approaches have demonstrated strong feature extraction and data mining capabilities. These have enabled the development of image-based DL models capable of correlating floc morphology with coagulant dosages. Moreover, hybrid models\u2014integrating AI with traditional regression or physical\/kinetic approaches\u2014have shown enhanced predictive capabilities. The review also identifies key future research directions aimed at further refining AI-based control systems for water treatment processes.\u003C\/div\u003E\n      \u003C\/div\u003E\n  \n  \u003Cdiv class=\u0022mt-5 mb-5\u0022\u003E\n          \u003Cspan\u003E\n          \u003Cb\u003EPresenter:\u003C\/b\u003E\n                      \u003Cp\u003E\n            Prof Shakhawat Chowdhury\n            \u003C\/p\u003E\n                  \u003C\/span\u003E\n      \u003C\/div\u003E\n\n  \u003Cdiv class=\u0022mb-5\u0022\u003E\n          \u003Cdiv class=\u0022field__label\u0022\u003E\n        Author\n      \u003C\/div\u003E\n              \u003Cp\u003E\n          Shakhawat Chowdhury\n        \u003C\/p\u003E\n            \u003C\/div\u003E\n\n\u003C\/article\u003E\n","dialogOptions":{"width":"700","position":{"my":"right top","at":"right top"},"closeOnEscape":true,"dialogClass":"presentation-dialog","modal":true,"title":"","classes":{"ui-dialog":"presentation-dialog"}}}]