{"id":2903,"date":"2026-06-23T10:51:06","date_gmt":"2026-06-23T10:51:06","guid":{"rendered":"https:\/\/artelnics.com\/case_studies\/celsa-rolling-furnace-digital-twin\/"},"modified":"2026-07-22T09:37:15","modified_gmt":"2026-07-22T09:37:15","slug":"celsa-rolling-furnace-digital-twin","status":"publish","type":"case_studies","link":"https:\/\/artelnics.com\/case_studies\/celsa-rolling-furnace-digital-twin\/","title":{"rendered":"Building a digital twin of a rolling furnace"},"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 digital twin models furnace behavior to understand billet temperature, gas consumption and process optimization opportunities.<\/p>\n<div class=\"meta\">\n    <span class=\"tag\">Steel manufacturing<\/span><br \/>\n    <span class=\"tag\">Digital twin<\/span><br \/>\n    <span class=\"tag\">Process optimization<\/span>\n  <\/div>\n<h2>Challenge<\/h2>\n<p>Continuous rolling furnaces involve high temperatures, several zones and moving billets. Operators need to understand how process settings affect billet temperature and energy consumption.<\/p>\n<h2>Data<\/h2>\n<p>The model used furnace-zone temperatures, gas-flow information, billet properties, speed and process geometry, combining physical knowledge with measured plant data.<\/p>\n<h2>Solution<\/h2>\n<p>Artelnics developed a digital twin based on differential-equation models and data validation, comparing modeling approaches and relating furnace settings to process behavior.<\/p>\n<div class=\"grid\">\n<div class=\"card\"><strong>Core model<\/strong><\/p>\n<p>Temperature evolution of billets across furnace zones.<\/p>\n<\/div>\n<div class=\"card\"><strong>Optimization lever<\/strong><\/p>\n<p>Gas flow, zone temperature and speed settings.<\/p>\n<\/div>\n<\/div>\n<h2>Results<\/h2>\n<p>The digital twin gives engineers a practical tool to study furnace operation, evaluate scenarios and identify opportunities to reduce energy use while preserving product quality.<\/p>\n<h2>Illustrations<\/h2>\n<figure><img decoding=\"async\" src=\"https:\/\/artelnics.com\/wp-content\/uploads\/2026\/06\/celsa_twin_temp.jpg\" alt=\"Temperature comparison in the rolling furnace\"><figcaption>Furnace temperature comparison<\/figcaption><\/figure>\n<figure><img decoding=\"async\" src=\"https:\/\/artelnics.com\/wp-content\/uploads\/2026\/06\/celsa_twin_gas.jpg\" alt=\"Gas consumption versus furnace temperature\"><figcaption>Gas flow and temperature<\/figcaption><\/figure>\n<\/article>\n<nav class=\"codex-case-study-cta\" aria-label=\"Case study actions\">\n<div><strong>Have a similar challenge?<\/strong><span>Artelnics can help turn operational data into a practical AI system.<\/span><\/div>\n<div class=\"codex-case-study-actions\"><a href=\"\/case_studies\/\">View all projects<\/a><a href=\"\/contact\/\">Talk to us<\/a><\/div>\n<\/nav>\n<style id=\"codex-case-study-polish-v1\">\/* codex-case-study-polish-v1 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