تعیین قابلیت خردایش ناشی از انفجار بر اساس نسبت سرعت موج طولی و بعد فرکتال بلوکهای انفجاری | ||
| روش های تحلیلی و عددی در مهندسی معدن | ||
| مقاله 9، دوره 10، شماره 24، آبان 1399، صفحه 105-117 اصل مقاله (1.33 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.29252/anm.2020.11479.1376 | ||
| نویسندگان | ||
| مرتضی باغستانی1؛ مسعود منجزی1؛ علیرضا یاراحمدی بافقی* 2، 3؛ پیمان افضل4 | ||
| 1دانشکده فنی و مهندسی، دانشگاه تربیت مدرس | ||
| 2دانشکده مهندسی معدن و متالورژی، دانشگاه یزد | ||
| 3پژوهشکده فناوریهای معدنکاری، دانشگاه یزد | ||
| 4گروه مهندسی نفت و معدن، دانشگاه آزاد اسلامی، واحد تهران جنوب | ||
| چکیده | ||
| انفجار یکی از مهمترین عملیات در معادن و فعالیتهای عمرانی است و قابلیت انفجار نیز از مهمترین پارامترهای تودهسنگ محسوب میشود. نتایج انفجار را میتوان به پدیدههای مطلوب و نامطلوب تقسیمبندی نمود. خردایش سنگ یکی از نتایج مطلوب انفجار است که بر فرآیندهای بعدی (بارگیری و حمل) و صنایع پایین دست تاثیرگذار است. در زمینه تعیین شاخص خردایش ناشی از انفجار تحقیقات گستردهای صورت گرفته است. عوامل موثر بر انفجار را میتوان به دو دسته عوامل قابل کنترل و غیر قابل کنترل تقسیمبندی نمود. شکل و ابعاد بلوکهای برجا در جبههکارهای انفجاری از جمله عوامل غیر قابل کنترل است که در پژوهشهای انجام شده، مورد توجه قرار نگرفته است. در این مقاله از دادههای انفجاری و ویژگیهای تودهسنگ معادن چغارت، چادرملو و سهچاهون استفاده شده است. بعد فرکتال جهت کمیسازی اندازه ذرات به روش شمارش جعبه مورد استفاده قرار گرفته است. جهت بررسی ارتباط بین شاخص خردایش (D80) به عنوان پارامتر وابسته و پارامترهای مستقل خرج ویژه، مقاومت فشاری تک محوری، نسبت سرعت موج طولی و نسبت شکل بلوکها، از مدلهای رگرسیون خطی، رگرسیون غیرخطی و شبکه عصبی استفاده شده است. نتایج حاصل از رگرسیون خطی نشاندهنده آن است که شاخص خردایش با نسبت شکل بلوکها دارای ارتباط معنادار است. ضریب تعیین بین شاخص خردایش و پارامترهای مستقل در رگرسیون خطی، غیرخطی و شبکه عصبی به ترتیب برابر 52/0، 70/0 و 96/0 به دست آمده است؛ همچنین ضریب VAF مدلهای رگرسیون خطی، غیرخطی و شبکه عصبی به ترتیب برابر 18/3، 33/70 و 28/95 است که نشاندهنده توانایی شبکه عصبی جهت پیشبینی خردایش ناشی از انفجار است. | ||
| کلیدواژهها | ||
| خردایش سنگ؛ بعد فرکتال؛ رگرسیون؛ سرعت موج طولی؛ شبکه عصبی | ||
| عنوان مقاله [English] | ||
| Determination of Rock Fragmentation Based on Longitude Wave Velocity and Fractal Dimension | ||
| نویسندگان [English] | ||
| Morteza Baghestani1؛ Masoud Monjezi1؛ Alireza Yarahmadi Bafghi2، 3؛ Peyman Afzal4 | ||
| 1Dept. of Mining, Tarbiat Modares University, Tehran, Iran | ||
| 2Dept. of Mining and Metallurgy, Yazd University, Yazd, Iran | ||
| 3Dept. of Mining and Metallurgy, Yazd University, Yazd, Iran | ||
| 4Dept. of Petroleum and Mining Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran | ||
| چکیده [English] | ||
| Summary In this paper, the blasting data and rock mass characteristics of Chogart, Chadormalu, and Sechahum mines were used to predict the size distribution of rock fragmentation (D80). Rock fragmentation is affected by various parameters such as rock mass properties, in-situ blocks shape, blasting geometry, etc. To quantify the shape of in-situ blocks, fractal geometry is a suitable method. To predict the rock fragmentation (D80) based on independent variables (rock mass characteristics, in-situ block shape, and blasting geometry); linear/nonlinear regression and neural networks were used. The results showed that the nonlinear regression and neural network were the ability to predict the size distribution of rock fragmentation. Introduction Due to economic reasons, drilling and blasting methods have been used in mining, quarrying industries, and civil projects. The results of blasting can be categorized into two i.e. favorable results (rock fragmentation, heave and move material) and unfavorable results (air overpressure, back break, ground vibration, and fly rock). Rock fragmentation due to blasting is influenced by several factors that are classified into three namely; explosive parameters, rock mass characteristics, and blast geometry. In recent decades, several empirical models have been proposed to predict rock fragmentation due to blasting. Nowadays, based on computer science advances, regression analysis and artificial intelligence (AI) have been employed for rock fragmentation prediction. Methodology and Approaches In this research, fractal geometry was used to describe the rock mass shape. The fractal dimension of in-situ blocks was determined by the box-counting method. On the other hand, the uniaxial compressive strength (UCS) and longitude wave velocity (laboratory and in-situ) were considered as rock mass characteristics. Also, the powder factor (PF) was representative of blasting geometry and explosive parameters. The linear/nonlinear regression and neural network were used to investigate the relationship between the rock fragmentation and independence variables (rock mass characteristics, blasting geometry, rock mass shape). Results and Conclusions In this research, an attempt was made to predict the size distribution of rock fragmentation (D80)at the Central iron ore mines (Chogart, Chadormalu, and Sechahun) by linear/nonlinear regression and neural network. Linear regression results revealed that the independent variables have a significant effect on the dependent variable (D80). The results were shown the neural network has the superiority to the prediction of rock fragmentation. | ||
| کلیدواژهها [English] | ||
| Rock Fragmentation, Fractal Dimension, Wave Velocity, Regression, Neural Network | ||
| اصل مقاله | ||
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عملیات حفاری و انفجار از لحاظ فنی و اقتصادی، در استخراج معادن و کارهای عمرانی به عنوان یک جزء اصلی از چرخه عملیاتی مطرح است[1-3]. نتایج انفجار را میتوان به دو دسته نتایج مطلوب (خردایش سنگ، جابجایی توده خردشده) و نتایج نامطلوب (لرزش زمین، پرتاب سنگ، لرزش هوا و عقبزدگی) تقسیمبندی نمود[4-8]. خردایش ناشی از انفجار بر صنایع پایین دست معدنی (خردایش، پرعیارسازی) تاثیرگذار است[9،10]. عوامل موثر بر خردایش ناشی از انفجار را میتوان به عوامل قابل کنترل (سیستم آتشکاری، طراحی الگوها، مواد منفجره مصرفی) و عوامل غیر قابل کنترل (پارامترهای تودهسنگ) تقسیمبندی نمود[11-15] (شکل 1). | ||
| مراجع | ||
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