N. Sri Chandrahas, Bhanwar Singh Choudhary, M. S. Venkataramayya, Fissha Yewuhalashet
{"title":"通过改进遗传 XG 提升算法技术,利用未来数据集同时预测平均碎片尺寸和峰值粒子速度的创新方法","authors":"N. Sri Chandrahas, Bhanwar Singh Choudhary, M. S. Venkataramayya, Fissha Yewuhalashet","doi":"10.1007/s42461-024-01045-8","DOIUrl":null,"url":null,"abstract":"<p>In the current study, two algorithms, custom XG Boost (CXGBA) and improved genetic XG Boost algorithm (IGXGBA), have been chosen to create an empirical formula for the simultaneous prediction of the mean fragmentation size (MFS) and the peak particle velocity (PPV) with sourced datasets of geo-blast parameters such as spacing burden ratio (S/B), stemming length (T), decking length (DL), firing pattern (FP), total quantity of explosive (TE), maximum charge per delay (MCD), measuring distance (MD), joint angle (JA), joint spanning height (JSP), joint set number (Jn), and rock compressive strength. Advanced technical combinations like K-10 cross-validation, and grid search executed along genetic algorithm processes with a high mutation rate to XGBoost algorithm. All algorithms were executed using Python programming in the Google Colab platform. The results unveiled that IGXGBA is superior and effective in-terms of metric <i>R</i><sup>2</sup>, RMSE, and MAPE in predicting MFS and PPV. A WEB APP called Bhanwar Blasting Formula (BBF) was created utilizing Google Cloud Platform (GCP) and FLASK APP to benefit practicing mining engineers to predict blasting results easily from the site itself and identify optimization.</p>","PeriodicalId":18588,"journal":{"name":"Mining, Metallurgy & Exploration","volume":"67 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Inventive Approach for Simultaneous Prediction of Mean Fragmentation Size and Peak Particle Velocity Using Futuristic Datasets Through Improved Techniques of Genetic XG Boost Algorithm\",\"authors\":\"N. Sri Chandrahas, Bhanwar Singh Choudhary, M. S. Venkataramayya, Fissha Yewuhalashet\",\"doi\":\"10.1007/s42461-024-01045-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In the current study, two algorithms, custom XG Boost (CXGBA) and improved genetic XG Boost algorithm (IGXGBA), have been chosen to create an empirical formula for the simultaneous prediction of the mean fragmentation size (MFS) and the peak particle velocity (PPV) with sourced datasets of geo-blast parameters such as spacing burden ratio (S/B), stemming length (T), decking length (DL), firing pattern (FP), total quantity of explosive (TE), maximum charge per delay (MCD), measuring distance (MD), joint angle (JA), joint spanning height (JSP), joint set number (Jn), and rock compressive strength. Advanced technical combinations like K-10 cross-validation, and grid search executed along genetic algorithm processes with a high mutation rate to XGBoost algorithm. All algorithms were executed using Python programming in the Google Colab platform. The results unveiled that IGXGBA is superior and effective in-terms of metric <i>R</i><sup>2</sup>, RMSE, and MAPE in predicting MFS and PPV. A WEB APP called Bhanwar Blasting Formula (BBF) was created utilizing Google Cloud Platform (GCP) and FLASK APP to benefit practicing mining engineers to predict blasting results easily from the site itself and identify optimization.</p>\",\"PeriodicalId\":18588,\"journal\":{\"name\":\"Mining, Metallurgy & Exploration\",\"volume\":\"67 1\",\"pages\":\"\"},\"PeriodicalIF\":1.5000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mining, Metallurgy & Exploration\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s42461-024-01045-8\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"METALLURGY & METALLURGICAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mining, Metallurgy & Exploration","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s42461-024-01045-8","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"METALLURGY & METALLURGICAL ENGINEERING","Score":null,"Total":0}
An Inventive Approach for Simultaneous Prediction of Mean Fragmentation Size and Peak Particle Velocity Using Futuristic Datasets Through Improved Techniques of Genetic XG Boost Algorithm
In the current study, two algorithms, custom XG Boost (CXGBA) and improved genetic XG Boost algorithm (IGXGBA), have been chosen to create an empirical formula for the simultaneous prediction of the mean fragmentation size (MFS) and the peak particle velocity (PPV) with sourced datasets of geo-blast parameters such as spacing burden ratio (S/B), stemming length (T), decking length (DL), firing pattern (FP), total quantity of explosive (TE), maximum charge per delay (MCD), measuring distance (MD), joint angle (JA), joint spanning height (JSP), joint set number (Jn), and rock compressive strength. Advanced technical combinations like K-10 cross-validation, and grid search executed along genetic algorithm processes with a high mutation rate to XGBoost algorithm. All algorithms were executed using Python programming in the Google Colab platform. The results unveiled that IGXGBA is superior and effective in-terms of metric R2, RMSE, and MAPE in predicting MFS and PPV. A WEB APP called Bhanwar Blasting Formula (BBF) was created utilizing Google Cloud Platform (GCP) and FLASK APP to benefit practicing mining engineers to predict blasting results easily from the site itself and identify optimization.
期刊介绍:
The aim of this international peer-reviewed journal of the Society for Mining, Metallurgy & Exploration (SME) is to provide a broad-based forum for the exchange of real-world and theoretical knowledge from academia, government and industry that is pertinent to mining, mineral/metallurgical processing, exploration and other fields served by the Society.
The journal publishes high-quality original research publications, in-depth special review articles, reviews of state-of-the-art and innovative technologies and industry methodologies, communications of work of topical and emerging interest, and other works that enhance understanding on both the fundamental and practical levels.