Abstract
Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine.
Abstract
يطير الصخرة هي واحدة من أكثر الأحداث الخطرة في عملية تفجير الألغام السطحية. هناك العديد من الطرق التجريبية للتنبؤ يطير الصخور. انخفاض أداء مثل هذه النماذج ومن المقرر أن تعقد يطير تحليل الصخور. وجود المعلمات الفعالة المختلفة وعلاقاتهم غير معروفة هي الأسباب الرئيسية لعدم دقة النماذج التجريبية. في الوقت الحاضر ، وتطبيق مناهج جديدة مثل الذكاء الاصطناعي (منظمة العفو الدولية) ينصح بشدة. في هذه الورقة ، وبذلت محاولة للتنبؤ ومراقبة الطيران في تفجير الصخور تشغيل منجم سنکان الحديد ، وإيران تتضمن خواص الصخور والمعلمات تصميم الانفجار باستخدام الشبكة العصبية الاصطناعية (ANN) الأسلوب. وهناك ثلاثة تغذية طبقة قدما اعادة نشر الشبكة العصبية بعد 13 الخلايا العصبية الخفية مع 9 معلمات الإدخال والإخراج 1 معلمة تم تدريب 192 باستخدام قواعد البيانات التجريبية الانفجار. لوحظ أيضا أن في ترتيب تصاعدي ، قدرة مؤشر الانفجار ، المسؤول في التأخير ، حفرة قطرها ، ووقف طول عامل مسحوق ، هي المعايير الأكثر فعالية على صخرة تطير. تخفيض رسوم التأخير تسبب في انخفاض كبير في الصخر الطيران من 165 م الى 25 م فى منجم سنکان الحديد.
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Monjezi, M., Bahrami, A., Varjani, A.Y. et al. Prediction and controlling of flyrock in blasting operation using artificial neural network. Arab J Geosci 4, 421–425 (2011). https://doi.org/10.1007/s12517-009-0091-8
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DOI: https://doi.org/10.1007/s12517-009-0091-8