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Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach

التنبؤ flyrock و backbreak في حفرة مفتوحة التفجير العملية : نهج الاعصاب الوراثية

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Abstract

An ideally performed blasting operation enormously influences the mining overall cost. This aim can be achieved by proper prediction and attenuation of flyrock and backbreak. Poor performance of the empirical models has urged the application of new approaches. In this paper, an attempt has been made to develop a new neuro-genetic model for predicting flyrock and backbreak in Sungun copper mine, Iran. Recognition of the optimum model with this method as compared with the classic neural networks is faster and convenient. Genetic algorithm was utilized to optimize neural network parameters. Parameters such as number of neurons in hidden layer, learning rate, and momentum were considered in the model construction. The performance of the model was examined by statistical method in which absolutely higher efficiency of neuro-genetic modeling was proved. Sensitivity analysis showed that the most influential parameters on flyrock are stemming and powder factor, whereas for backbreak, stemming and charge per delay are the most effective parameters.

Abstract

تنفيذ عملية التفجير يؤثر بشكل كبير من الناحية المثالية تكاليف التعدين عموما. ويمكن تحقيق هذا الهدف عن طريق التنبؤ السليم وتخفيف flyrock وbackbreak. وحثت ضعف الأداء من نماذج تجريبية لتطبيق النهج الجديد. في هذه الورقة ، وقد بذلت محاولة لوضع نموذج جديد الاعصاب الوراثية للتنبؤ flyrock وbackbreak في منجم للنحاس Sungun وإيران. الاعتراف النموذج الأمثل مع هذا الأسلوب بالمقارنة مع الشبكة العصبية الكلاسيكي هو أسرع ومريحة. واستخدم الخوارزمية الجينية لتحسين المعلمات الشبكة العصبية. واعتبرت معلمات مثل عدد الخلايا العصبية في طبقة خفية ، معدل التعلم والزخم في بناء النموذج. تم فحص أداء نموذج من الأسلوب الإحصائي الذي ثبت كفاءة أعلى على الاطلاق من وضع نماذج الاعصاب الوراثية. حساسية التحليل أظهرت أن المعلمات الأكثر تأثيرا على flyrock هي النابعة ومسحوق للعامل في حين backbreak الناشئة والمسؤول عن تأخير في والمعلمات الأكثر فعالية.

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Authors and Affiliations

  1. Faculty of Engineering, Tarbiat Modares University, Tehran, 14115-143, Iran

    M. Monjezi, H. Amini Khoshalan & A. Yazdian Varjani

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  1. M. Monjezi
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  2. H. Amini Khoshalan
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  3. A. Yazdian Varjani
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Correspondence to M. Monjezi.

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Monjezi, M., Amini Khoshalan, H. & Yazdian Varjani, A. Prediction of flyrock and backbreak in open pit blasting operation: a neuro-genetic approach. Arab J Geosci 5, 441–448 (2012). https://doi.org/10.1007/s12517-010-0185-3

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  • DOI: https://doi.org/10.1007/s12517-010-0185-3

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