{"id":15708,"date":"2024-10-15T08:17:32","date_gmt":"2024-10-15T11:17:32","guid":{"rendered":"https:\/\/www.fie.undef.edu.ar\/ceptm\/?p=15708"},"modified":"2024-10-15T08:17:32","modified_gmt":"2024-10-15T11:17:32","slug":"analisis-de-la-fragmentacion-de-rocas-impulsado-por-ia-la-influencia-de-la-cantidad-de-carga-explosiva","status":"publish","type":"post","link":"https:\/\/www.fie.undef.edu.ar\/ceptm\/?p=15708","title":{"rendered":"An\u00e1lisis de la fragmentaci\u00f3n de rocas impulsado por IA, la influencia de la cantidad de carga explosiva"},"content":{"rendered":"<p>La perforaci\u00f3n y la voladura son fundamentales para la fragmentaci\u00f3n de materiales en la miner\u00eda, y contribuyen significativamente a sus costos. Lograr la rentabilidad requiere un dise\u00f1o de voladura preciso, incluida la cantidad \u00f3ptima de carga explosiva para controlar la fragmentaci\u00f3n, las vibraciones, las rocas proyectadas y la sobrepresi\u00f3n del aire. Este estudio presenta un nuevo modelo impulsado por IA,\u00a0<b>XGBoost-PSO-T<\/b>, que combina\u00a0<b>eXtreme Gradient Boosting (XGBoost)<\/b>\u00a0con\u00a0<b>Particle Swarm Optimization (PSO)<\/b>\u00a0utilizando la t\u00e9cnica Tri-Weight para mejorar la precisi\u00f3n predictiva. El modelo, evaluado por las m\u00e9tricas RMSE y R\u00b2, supera al XGBoost est\u00e1ndar, logrando un RMSE de 0,657 y un R\u00b2 de 0,922. Muestra potencial para optimizar los dise\u00f1os de voladuras, mejorar la eficiencia y reducir los costos en la miner\u00eda de superficie.<\/p>\n<hr \/>\n<p><strong>Abstract<\/strong><\/p>\n<p>Drilling and blasting are essential operations within the mining industry, playing a critical role in material fragmentation. Despite advancements in various blasting technologies, the process remains a dominant contributor to overall mining costs. Achieving cost efficiency requires the precise configuration of blast design parameters, including explosive charge quantity, to attain desired outcomes in fragmentation, ground vibrations, fly rock, and air over-pressure. This study introduces a novel artificial intelligence (AI)-driven model, XGBoost-PSO-T, which combines eXtreme Gradient Boosting (XGBoost) with Particle Swarm Optimization (PSO) through the integration of the Tri-Weight technique. The PSO-Tri-Weight method optimizes the hyperparameters of the XGBoost model, enhancing its predictive capabilities. The model&#8217;s performance was evaluated using root mean square error (RMSE) and coefficient of determination (R\u00b2), with the results demonstrating that the XGBoost-PSO-T system outperforms the standard XGBoost approach, achieving an RMSE of 0.657 and an R\u00b2 of 0.922. These findings suggest that the XGBoost-PSO-T model is a valuable tool for predicting fragmentation outcomes and optimizing blast designs in surface mining operations. The implementation of this system is recommended to improve blasting efficiency and reduce operational costs.<\/p>\n<p><a href=\"https:\/\/www.fie.undef.edu.ar\/ceptm\/wp-content\/uploads\/2024\/10\/ATG_03.03_01.pdf\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" class=\"aligncenter wp-image-5168\" src=\"https:\/\/www.fie.undef.edu.ar\/ceptm\/wp-content\/uploads\/2020\/02\/icono-PDF-1-243x300.png\" alt=\"Descargar archivo pdf\" width=\"75\" height=\"93\" srcset=\"https:\/\/www.fie.undef.edu.ar\/ceptm\/wp-content\/uploads\/2020\/02\/icono-PDF-1-243x300.png 243w, https:\/\/www.fie.undef.edu.ar\/ceptm\/wp-content\/uploads\/2020\/02\/icono-PDF-1.png 310w\" sizes=\"(max-width: 75px) 100vw, 75px\" \/><\/a><strong>Fuente:<\/strong> <a href=\"https:\/\/www.acadlore.com\/article\/ATG\/2024_3_3\/atg030301\" target=\"_blank\" rel=\"noopener\"><em>https:\/\/www.acadlore.com<\/em><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>La perforaci\u00f3n y la voladura son fundamentales para la fragmentaci\u00f3n de materiales en la miner\u00eda, y contribuyen significativamente a sus costos. Lograr la rentabilidad requiere&hellip; <\/p>\n","protected":false},"author":1,"featured_media":15710,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[23,24],"tags":[],"_links":{"self":[{"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/posts\/15708"}],"collection":[{"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=15708"}],"version-history":[{"count":1,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/posts\/15708\/revisions"}],"predecessor-version":[{"id":15711,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/posts\/15708\/revisions\/15711"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=\/wp\/v2\/media\/15710"}],"wp:attachment":[{"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=15708"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=15708"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.fie.undef.edu.ar\/ceptm\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=15708"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}