{"id":2901,"date":"2026-06-23T10:51:04","date_gmt":"2026-06-23T10:51:04","guid":{"rendered":"https:\/\/artelnics.com\/case_studies\/acciona-water-turbidity-prediction\/"},"modified":"2026-06-30T14:07:36","modified_gmt":"2026-06-30T14:07:36","slug":"acciona-water-turbidity-prediction","status":"publish","type":"case_studies","link":"https:\/\/artelnics.com\/case_studies\/acciona-water-turbidity-prediction\/","title":{"rendered":"Predicting turbidity in water treatment plants"},"content":{"rendered":"<style>@import url('https:\/\/fonts.googleapis.com\/css2?family=Roboto:wght@400;500;700;800;900&display=swap');\/* codex-artelnics-opennn-visual-sync-v5 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h2,.atl-home-cta-box h2,.atl-content-section h1{font-size:28px!important}.atl-case h2{font-size:28px!important}.atl-case p,.atl-case li{font-size:17px!important}.atl-home-lead,.atl-hero p,body section.atl-hero p:not(.atl-eyebrow){font-size:17px!important}}.cky-consent-container{left:auto!important;right:40px!important;bottom:40px!important;max-width:440px!important;width:min(440px,calc(100vw - 32px))!important;z-index:99999999!important}.cky-consent-container .cky-consent-bar{background:#FFFFFF!important;border:1px solid #D7E4EA!important;border-radius:8px!important;box-shadow:0 18px 42px rgba(30,83,116,.16)!important;color:#212121!important;padding:24px 26px!important}.cky-title,.cky-notice .cky-title{color:#212121!important;font-family:\"Roboto\",\"Segoe UI\",Arial,sans-serif!important;font-size:18px!important;font-weight:800!important;line-height:1.25!important}.cky-notice-des,.cky-notice-des *{color:#5F6368!important;font-family:\"Roboto\",\"Segoe UI\",Arial,sans-serif!important;font-size:14px!important;line-height:1.55!important}.cky-notice-group{display:block!important}.cky-notice-btn-wrapper{display:flex!important;flex-wrap:nowrap!important;gap:10px!important;justify-content:space-between!important;margin-top:18px!important}.cky-btn{flex:1 1 0!important;min-width:0!important;width:auto!important;min-height:44px!important;padding:0 20px!important;border-radius:0!important;font-family:\"Roboto\",\"Segoe UI\",Arial,sans-serif!important;font-size:14px!important;font-weight:700!important}.cky-btn-accept{background:#1E5374!important;border-color:#1E5374!important;color:#FFFFFF!important}.cky-btn-reject,.cky-btn-customize{background:#FFFFFF!important;border:1px solid #1E5374!important;color:#1E5374!important}.cky-btn:hover{opacity:1!important;box-shadow:none!important}@media(max-width:767px){.cky-consent-container{left:16px!important;right:16px!important;bottom:16px!important;width:auto!important;max-width:none!important}.cky-notice-btn-wrapper{flex-direction:column!important}.cky-btn{width:100%!important}}\/* codex-artelnics-opennn-visual-sync-v5-overrides-end *\/<\/style>\n<article class=\"atl-case\">\n<p class=\"lead\">A predictive analytics system anticipates high-turbidity events so water-treatment operators can act before quality problems escalate.<\/p>\n<div class=\"meta\">\n    <span class=\"tag\">Water and utilities<\/span><br \/>\n    <span class=\"tag\">Predictive analytics<\/span><br \/>\n    <span class=\"tag\">Early warning<\/span>\n  <\/div>\n<h2>Challenge<\/h2>\n<p>Water-treatment plants can experience sudden turbidity peaks caused by process conditions, environmental effects or upstream events. Operators need enough warning to diagnose the situation and adjust the process before the event affects production.<\/p>\n<h2>Data<\/h2>\n<p>The project combined historical turbidity measurements with process variables such as conductivity and operating signals. External variables, including satellite and environmental information, were considered when they helped explain turbidity events.<\/p>\n<h2>Solution<\/h2>\n<p>Artelnics built predictive models to identify patterns that precede high-turbidity episodes, studied the most relevant variables and produced visual diagnostics for model testing and event interpretation.<\/p>\n<div class=\"grid\">\n<div class=\"card\"><strong>Client value<\/strong><\/p>\n<p>Earlier detection of risky water-quality conditions.<\/p>\n<\/div>\n<div class=\"card\"><strong>Model output<\/strong><\/p>\n<p>Forecasts, correlations and event diagnostics for operators.<\/p>\n<\/div>\n<\/div>\n<h2>Results<\/h2>\n<p>The resulting workflow transforms historical plant data into an operational early-warning capability, making it easier to anticipate abnormal conditions and support decisions with objective evidence.<\/p>\n<h2>Illustrations<\/h2>\n<figure><img decoding=\"async\" src=\"https:\/\/artelnics.com\/wp-content\/uploads\/2026\/06\/acciona_turbidity_testing.jpg\" alt=\"Predicted and observed turbidity over time\"><figcaption>Turbidity prediction testing<\/figcaption><\/figure>\n<figure><img decoding=\"async\" src=\"https:\/\/artelnics.com\/wp-content\/uploads\/2026\/06\/acciona_turbidity_corr.jpg\" alt=\"Variable correlations for the turbidity model\"><figcaption>Turbidity variable correlations<\/figcaption><\/figure>\n<\/article>\n","protected":false},"excerpt":{"rendered":"<p>A predictive analytics system anticipates high-turbidity events so water-treatment operators can act before quality problems escalate.<\/p>\n","protected":false},"featured_media":2909,"parent":0,"menu_order":0,"template":"","categories":[],"class_list":["post-2901","case_studies","type-case_studies","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/artelnics.com\/api\/wp\/v2\/case_studies\/2901","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/artelnics.com\/api\/wp\/v2\/case_studies"}],"about":[{"href":"https:\/\/artelnics.com\/api\/wp\/v2\/types\/case_studies"}],"version-history":[{"count":5,"href":"https:\/\/artelnics.com\/api\/wp\/v2\/case_studies\/2901\/revisions"}],"predecessor-version":[{"id":3044,"href":"https:\/\/artelnics.com\/api\/wp\/v2\/case_studies\/2901\/revisions\/3044"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/artelnics.com\/api\/wp\/v2\/media\/2909"}],"wp:attachment":[{"href":"https:\/\/artelnics.com\/api\/wp\/v2\/media?parent=2901"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/artelnics.com\/api\/wp\/v2\/categories?post=2901"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}