{"id":28340,"date":"2025-07-10T11:30:46","date_gmt":"2025-07-10T14:30:46","guid":{"rendered":"https:\/\/www.inthemine.com.br\/site\/?p=28340"},"modified":"2025-07-10T11:30:46","modified_gmt":"2025-07-10T14:30:46","slug":"solucao-deep-learning-para-classificacao-de-minerios","status":"publish","type":"post","link":"https:\/\/www.inthemine.com.br\/site\/solucao-deep-learning-para-classificacao-de-minerios\/","title":{"rendered":"SOLU\u00c7\u00c3O DEEP LEARNING PARA CLASSIFICA\u00c7\u00c3O DE MIN\u00c9RIOS"},"content":{"rendered":"
A TOMRA Mining lan\u00e7ou o CONTAIN\u2122, uma tecnologia propriet\u00e1ria de Deep Learning projetada para aprimorar a recupera\u00e7\u00e3o desse tipo de min\u00e9rio com inclus\u00e3o, que s\u00e3o dif\u00edceis de detectar usando m\u00e9todos de classifica\u00e7\u00e3o tradicionais. Projetado para uma integra\u00e7\u00e3o perfeita com o ecossistema da TOMRA Mining, o CONTAIN\u2122 representa a mais recente evolu\u00e7\u00e3o na plataforma de classifica\u00e7\u00e3o orientada por IA da empresa.<\/span><\/div>\n
Desenvolvido internamente pelos engenheiros de software e especialistas em minera\u00e7\u00e3o da TOMRA, o CONTAIN\u2122 usa redes neurais convolucionais para realizar an\u00e1lises em tempo real de imagens de raios X, classificando visualmente as rochas com base na probabilidade de apresentar inclus\u00f5es minerais. Isso inclui mineraliza\u00e7\u00f5es complexas, como em min\u00e9rios de tungst\u00eanio, n\u00edquel e estanho – materiais que tradicionalmente resultam em alta classifica\u00e7\u00e3o incorreta ou perda excessiva de produtos.<\/span><\/div>\n
<\/div>\n
\u201cNosso sistema foi treinado em dezenas de milhares de amostras de min\u00e9rio e projetado desde o in\u00edcio para classificar min\u00e9rios do tipo inclus\u00e3o\u201d, diz Stefan J\u00fcrgensen, l\u00edder da equipe de software da TOMRA Mining. \u201cCom o CONTAIN, os operadores podem ajustar dinamicamente a sensibilidade de teor de recupera\u00e7\u00e3o por meio de uma interface de tela sens\u00edvel ao toque, permitindo um controle preciso sobre o rendimento e as especifica\u00e7\u00f5es do produto.\u201d<\/span><\/div>\n
\n
\"Stefan<\/a>
Stefan J\u00fcrgensen: L\u00edder do time de Software da TOMRA Mining<\/figcaption><\/figure>\n

Hist\u00f3rico de inova\u00e7\u00e3o<\/b><\/p>\n<\/div>\n

A TOMRA tem sido pioneira na aplica\u00e7\u00e3o de intelig\u00eancia artificial no campo da classifica\u00e7\u00e3o baseada em sensores h\u00e1 d\u00e9cadas. A empresa implementou pela primeira vez o processamento de imagens com IA em 1993 e, posteriormente, expandiu seus recursos com aprendizado de m\u00e1quina na classifica\u00e7\u00e3o de transmiss\u00e3o de raios X (XRT) e infravermelho pr\u00f3ximo (NIR). Em 2018, a TOMRA estabeleceu uma equipe dedicada de \u2018deep learning\u2019, acelerando o desenvolvimento de plataformas de classifica\u00e7\u00e3o de IA de n\u00edvel industrial.<\/span><\/div>\n
Esse investimento inicial e substancial culminou com o lan\u00e7amento do OBTAIN\u2122, que introduziu a precis\u00e3o de uma \u00fanica part\u00edcula na classifica\u00e7\u00e3o de min\u00e9rio em altas capacidades. Agora, o CONTAIN\u2122 se baseia nesse alicerce com a classifica\u00e7\u00e3o direcionada de min\u00e9rios do tipo inclus\u00e3o – ampliando os limites da tomada de decis\u00f5es de classifica\u00e7\u00e3o automatizada.<\/span><\/div>\n
\n

Algoritmos avan\u00e7ados<\/b><\/p>\n<\/div>\n

Ao contr\u00e1rio dos sistemas tradicionais de classifica\u00e7\u00e3o \u00f3ptica, que t\u00eam limita\u00e7\u00f5es para detectar min\u00e9rios de baixo teor ou do tipo inclus\u00e3o, o CONTAIN\u2122 apresenta uma abordagem fundamentalmente diferente para a classifica\u00e7\u00e3o. Ao analisar a estrutura de cada rocha usando algoritmos avan\u00e7ados de Deep Learning, o sistema identifica padr\u00f5es mineral\u00f3gicos sutis que indicam a presen\u00e7a de metais valiosos, como tungst\u00eanio, n\u00edquel ou estanho nas camadas internas. <\/span><\/div>\n
<\/div>\n
A cada rocha \u00e9 atribu\u00edda uma pontua\u00e7\u00e3o com base em sua probabilidade de conter mineraliza\u00e7\u00e3o subterr\u00e2nea, permitindo decis\u00f5es de classifica\u00e7\u00e3o precisas e orientadas por dados. Esse recurso permite que as opera\u00e7\u00f5es de minera\u00e7\u00e3o adaptem suas estrat\u00e9gias em tempo real, quer que o objetivo seja maximizar o teor do concentrado, minimizar a perda de material valioso ou alinhar-se \u00e0s restri\u00e7\u00f5es de custo de processamento.<\/span><\/div>\n
<\/div>\n
CONTAIN\u2122 foi desenvolvido para desempenho em escala industrial. Como n\u00e3o depende de rendimento ou espa\u00e7amento espec\u00edfico na esteira, o sistema mant\u00e9m a precis\u00e3o exata mesmo em fluxos de entrada densos e r\u00e1pidos. Isso faz com que o CONTAIN\u2122 seja especialmente eficaz em plantas de processamento de alto volume, onde a consist\u00eancia, a velocidade e as taxas de recupera\u00e7\u00e3o s\u00e3o essenciais para a lucratividade.<\/span><\/div>\n

Veja\u00a0o video:\u00a0\u00a0CONTAIN\u2122: Deep learning classification for inclusion-type ore sorting<\/a><\/u><\/p>\n","protected":false},"excerpt":{"rendered":"

Uso de redes neurais convolucionais para realizar an\u00e1lises em tempo real das rochas com base na probabilidade de apresentar inclus\u00f5es minerais<\/p>\n","protected":false},"author":3,"featured_media":28341,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[48],"tags":[],"class_list":["post-28340","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-processamento-guia-itm"],"yoast_head":"\nSOLU\u00c7\u00c3O DEEP LEARNING PARA CLASSIFICA\u00c7\u00c3O DE MIN\u00c9RIOS - Revista In The Mine<\/title>\n<meta name=\"description\" content=\"Usa redes neurais convolucionais para realizar an\u00e1lises em tempo real das rochas com base na probabilidade de apresentar inclus\u00f5es minerais\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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