کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
276086 507979 2016 4 صفحه PDF ندارد دانلود رایگان
عنوان انگلیسی مقاله
Prediction of blast boulders in open pit mines via multiple regression and artificial neural networks
ترجمه فارسی عنوان
برآورد تخته سنگ های انفجاری در معادن روباز از طریق رگرسیون چند متغیره و شبکه عصبی مصنوعی
کلمات کلیدی
تخته سنگ انفجار؛ شبکه های عصبی مصنوعی؛ رگرسیون چندگانه؛ معدن سنگ آهن Golegohar
Blast boulder; Artificial neural networks; Multiple regression; Golegohar iron ore mine
موضوعات مرتبط
مهندسی و علوم پایه علوم زمین و سیارات زمین شناسی اقتصادی
چکیده انگلیسی

The most important objective of blasting in open pit mines is rock fragmentation. Prediction of produced boulders (oversized crushed rocks) is a key parameter in designing blast patterns. In this study, the amount of boulder produced in blasting operations of Golegohar iron ore open pit mine, Iran was predicted via multiple regression method and artificial neural networks. Results of 33 blasts in the mine were collected for modeling. Input variables were: joints spacing, density and uniaxial compressive strength of the intact rock, burden, spacing, stemming, bench height to burden ratio, and specific charge. The dependent variable was ratio of boulder volume to pattern volume. Both techniques were successful in predicting the ratio. In this study, the multiple regression method was superior with coefficient of determination and root mean squared error values of 0.89 and 0.19, respectively.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Mining Science and Technology - Volume 26, Issue 2, March 2016, Pages 183–186
نویسندگان
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