این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Mining and Environment، جلد ۱۶، شماره ۴، صفحات ۱۱۹۵-۱۲۱۹
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Optimization of Fragmentation and Operational Costs of Drilling and Blasting using Hybrid Machine Learning Models in an Open-Pit Mine in Peru
چکیده انگلیسی مقاله Mining plays a crucial role in the economy of many countries, contributing significantly to GDP, employment, and industrial development. However, optimizing drilling and blasting operations remains a key challenge in open-pit mining due to its direct impact on operational costs and rock fragmentation efficiency. This work aims to optimize fragmentation (X50) and drilling and blasting costs using hybrid machine learning models, an innovative approach that improves predictive accuracy and economic feasibility. Six models were developed: Artificial Neural Networks (ANNs), Decision Trees (DT), Extreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Regression (SVR), optimized using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The dataset, comprising 100 blasts, was split into 70% for training and 30% for testing. The SVR+PSO model achieved the highest accuracy for fragmentation prediction, with an RMSE of 0.27, MAE of 0.21, and R2 of 0.92. The RF+GA model was most effective for cost prediction, with an RMSE of 414.58, MAE of 354.14, and R2 of 0.99. Optimization scenarios were implemented by reducing burden (4.3 m to 3.8 m) and spacing (5.0 m to 4.5 m), achieving a 5.7% reduction in X50 (17.6 cm to 16.6 cm) and a 9.5% cost decrease (63,000 USD to 57,000 USD per blast). Predictions for 30 future blasts using the RF + GA model estimated a total cost of 1.7 MUSD, averaging 55,180 USD per blast. These findings confirm the effectiveness of machine learning in cost optimization and improving blasting efficiency, presenting a robust data-driven approach to optimizing mining operations.
کلیدواژه‌های انگلیسی مقاله Fragmentation,hybrid models,Machine learning,Particle Swarm Optimization,cost of extraction
نویسندگان مقاله Marco Antonio Cotrina Teatino |
Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

Jairo Jhonatan Marquina Araujo |
Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

Jose Nestor Mamani Quispe |
Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru

Solio Marino Arango-Retamozo |
Department of Mining Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

Johnny Henrry Ccatamayo-Barrios |
Department of Mining Engineering, Universidad Nacional San Cristobal de Huamanga, Ayacucho, Peru

Joe Alexis Gonzalez-Vasquez |
Department of Industrial Engineering, Faculty of Engineering, National University of Trujillo, Trujillo, Peru

Teofilo Donaires-Flores |
Faculty of Chemical Engineering, National University of the Altiplano of Puno, Puno, Peru

Maxgabriel Alexis Calla-Huayapa |
Faculty of Industrial Process Engineering, National University of Juliaca, Juliaca, Peru

نشانی اینترنتی https://jme.shahroodut.ac.ir/article_3429_d2ac6d7e1bd6579fc855315db8c01f51.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات