{"id":6097,"date":"2025-07-28T13:40:02","date_gmt":"2025-07-28T04:40:02","guid":{"rendered":"https:\/\/aizoth.com\/?post_type=use_case&#038;p=6097"},"modified":"2025-12-05T15:45:48","modified_gmt":"2025-12-05T06:45:48","slug":"contents-e014","status":"publish","type":"use_case","link":"https:\/\/aizoth.com\/en\/use_case\/contents-e014\/","title":{"rendered":"Optimizing the Aluminum Recycling Upgrade Process Using Multi-Sigma\u00ae"},"content":{"rendered":"\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong>This case study showcases how Multi-Sigma<sup>\u00ae<\/sup>\u2019s AI-driven analysis platform optimizes the aluminum recycling upgrade process by enhancing mechanical properties while minimizing greenhouse gas (GHG) emissions and costs. This project was conducted as a part of a NEDO-funded project, this study leverages AI to drive significant advancements in sustainability and performance.<\/strong><\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:30px;text-decoration:underline\"><strong>1. AI analysis<\/strong> <\/h2>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">A dataset containing 18 process samples was analyzed. The AI model was trained using key input parameters from the aluminum recycling process, including impurity concentration, solution treatment duration, high-pressure sliding process, and aging treatment conditions. The output parameters included the mechanical properties of the recycled aluminum, as well as associated GHG emissions and cost estimates.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"380\" src=\"https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E014_1-2-1024x380.png\" alt=\"Optimizing the Aluminum Recycling Upgrade Process Using Multi-Sigma\u00ae\nAI analysis\" class=\"wp-image-6960\" srcset=\"https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E014_1-2-1024x380.png 1024w, https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E014_1-2-300x111.png 300w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:30px;text-decoration:underline\"><strong>2. Contribution Analysis<\/strong><\/h2>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">The contribution analysis identified the most influential parameters affecting the aluminum recycling process:<\/p>\n\n\n\n<ol class=\"wp-block-list has-medium-font-size\">\n<li>Length of the high-pressure sliding process (~ 41% influence).<\/li>\n\n\n\n<li>impurity concentration (~ 19% influence).<\/li>\n\n\n\n<li>solution treatment time (~ 12% influence).<\/li>\n<\/ol>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<h2 class=\"wp-block-heading\" style=\"font-size:30px;text-decoration:underline\"><strong>3. Multi-objective Optimization of the Recycle Process<\/strong> <\/h2>\n\n\n\n<p class=\"has-medium-font-size wp-block-paragraph\">Multi-Sigma<strong><sup>\u00ae<\/sup><\/strong>\u2019s optimization module was utilized to determine the ideal recycling process parameters. The objective was to maximize key mechanical properties\u2014such as&nbsp;tensile strength, 0.2% proof stress, and elongation\u2014while minimizing&nbsp;GHG emissions and cost.<\/p>\n\n\n\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"284\" src=\"https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E014_2-1024x284.png\" alt=\"Multi-objective Optimization of the Recycle Process\" class=\"wp-image-6165\" srcset=\"https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E014_2-1024x284.png 1024w, https:\/\/aizoth.com\/wp-content\/uploads\/2025\/07\/E014_2-300x83.png 300w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">This usecase is based on results obtained from a project, JPNP21003, commissioned by the New Energy and Industrial Technology Development Organization (NEDO).<\/p>\n\n\n\n<p class=\"has-small-font-size wp-block-paragraph\">Source: NEDO project (Development of advanced circulation technology for aluminum materials)<br><a href=\"https:\/\/www.nedo.go.jp\/english\/activities\/activities_ZZJP_100195.html\">https:\/\/www.nedo.go.jp\/english\/activities\/activities_ZZJP_100195.html<\/a><br><a href=\"https:\/\/aizoth.com\/research-project\/nedo\/\">https:\/\/aizoth.com\/research-project\/nedo\/<\/a><\/p>\n\n\n\n<div style=\"height:100px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-fe48e5de wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button has-custom-width wp-block-button__width-50 is-style-outline is-style-outline--1\"><a class=\"wp-block-button__link has-black-color has-text-color has-background has-link-color has-medium-font-size has-custom-font-size wp-element-button\" 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