發(fā)布時間：2018-05-11 09:52

  本文選題：爆破 + 臺階　； 參考：《內(nèi)蒙古科技大學》2015年碩士論文

【摘要】：在白云鄂博西礦生產(chǎn)過程中，臺階爆破是一個非常重要的生產(chǎn)環(huán)節(jié)，爆破效果影響著生產(chǎn)過程中的采裝、運輸?shù)群罄m(xù)工序的效率和總的經(jīng)濟效益。不同爆區(qū)之間的巖石性質(zhì)的差異使原有爆破參數(shù)與分區(qū)后的爆區(qū)巖性不相匹配，結(jié)果導(dǎo)致爆破效果發(fā)生變化。大塊率、炸藥單耗偏高，殘留根底，直接影響著鏟裝、運輸、破碎等后續(xù)工序和采礦總成本。 在實際施工中，當?shù)刭|(zhì)條件和環(huán)境發(fā)生變化，爆破效果也會發(fā)生相應(yīng)的改變，實踐表明，，工程爆破效果與很多因素有關(guān)，比如礦巖巖性、炸藥性能以及爆破參數(shù)等，且這些因素之間存在著一定的客觀規(guī)律性。爆破工作所要解決的重要課題就是了解、描述這種客觀的規(guī)律性，并在此基礎(chǔ)上形成較為成熟的爆破經(jīng)驗并使之得以積累。 本文根據(jù)各巖石的物理力學性質(zhì)，通過分析各巖石可爆性將白云鄂博西礦臺階分為易爆區(qū)、中等難度爆破區(qū)和難爆區(qū)三個區(qū)域進行分區(qū)爆破。充分利用人工神經(jīng)網(wǎng)絡(luò)具有的自學習、自適應(yīng)、自組織和非線性動力學特性等特點，建立了面向MATLAB語言的爆破參數(shù)BP神經(jīng)網(wǎng)絡(luò)預(yù)測模型，對爆破參數(shù)進行優(yōu)化。對白云鄂博西礦爆破現(xiàn)場實驗數(shù)據(jù)進行統(tǒng)計、分析、研究，利用BP神經(jīng)網(wǎng)絡(luò)對爆破參數(shù)進行合理優(yōu)化，得出適合白云鄂博西礦的爆破參數(shù)。易爆區(qū)域孔網(wǎng)參數(shù)約為117m、最小抵抗線為6.5m、炸藥單耗降至0.548kg/m3；中等爆破難度區(qū)域孔網(wǎng)參數(shù)約為107m、最小抵抗線為6.0m、炸藥單耗降0.578kg/m3；難爆區(qū)域孔網(wǎng)參數(shù)約為106.5m、最小抵抗線為5.5m、炸藥單耗降0.657kg/m3。爆破后大塊率控制在2%以內(nèi)、基本無根底出現(xiàn)，有效的解決了之前礦山大塊率高、根底較多的問題，滿足礦山對精細化爆破的要求，爆破效果明顯提高。 通過分析和研究白云鄂博西礦爆破參數(shù)，提出利用BP神經(jīng)網(wǎng)絡(luò)來建立符合實際情況的爆破預(yù)測模型并進行訓(xùn)練，經(jīng)檢驗，模型的建立合理、精度符合要求，指導(dǎo)礦山的爆破生產(chǎn)效果良好，降低了采礦成本，提高了該礦的經(jīng)濟效益。
[Abstract]:Bench blasting is a very important production link in the production process of Bayan Obo West Mine. The blasting effect affects the efficiency and overall economic benefit of the subsequent processes such as mining and loading, transportation and so on. The difference of rock properties between different blasting areas makes the original blasting parameters do not match the lithology of the blasting area after the division, which results in the change of blasting effect. Bulk rate, high unit consumption of explosive, and residual base directly affect shovel loading, transportation, crushing and other follow-up processes and total mining costs. In the actual construction, when the geological conditions and environment change, the blasting effect will also change. The practice shows that the blasting effect is related to many factors, such as rock and ore properties, explosive properties and blasting parameters, etc. And there are some objective laws between these factors. The important task to be solved in blasting work is to understand and describe this objective regularity, and on this basis to form more mature blasting experience and make it accumulate. According to the physical and mechanical properties of each rock, the bench of Bayan Obo West Coal Mine is divided into three zones: explosive area, middle difficulty blasting area and difficult blasting area by analyzing the blasting property of each rock. Taking full advantage of the self-learning, self-adaptive, self-organizing and nonlinear dynamic characteristics of artificial neural network, a BP neural network prediction model of blasting parameters for MATLAB language is established, and the blasting parameters are optimized. The blasting field experiment data of Bayan Obo West Mine were analyzed and analyzed. The blasting parameters were optimized by BP neural network and the blasting parameters suitable for Bayan Obo West Mine were obtained. The parameters of hole net in explosive region are about 117 m, the minimum resistance line is 6.5 m, the unit consumption of explosive is decreased to 0.548 kg 路m ~ (3), the parameter of hole net is about 107 m, the minimum resistance line is 6.0 m, the unit consumption of explosive is 0.578kg / m ~ (3), the parameter of hole net is about 106.5 m, the minimum resistance line is 5.5 m, and the unit consumption of explosive is 0.657 kg 路m ~ (3). After blasting, the boulder rate is controlled within 2% and there is basically no root bottom, which effectively solves the problems of high boulder rate and more root and bottom before, and meets the requirements of fine blasting in mines, and the blasting effect is obviously improved. Based on the analysis and study of blasting parameters in Bayan Obo West Mine, a BP neural network is put forward to establish and train the blasting prediction model in accordance with the actual situation. After testing, the establishment of the model is reasonable and the precision meets the requirements. The blasting effect of the mine is good, the mining cost is reduced and the economic benefit of the mine is improved.
【學位授予單位】：內(nèi)蒙古科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TD235

【參考文獻】

相關(guān)期刊論文 前10條

1 周志華,張金山,劉勤;基于人工神經(jīng)網(wǎng)絡(luò)的爆破震動速度峰值的預(yù)報[J];包頭鋼鐵學院學報;2003年01期

2 鄒定祥;計算露天礦臺階爆破塊度分布的三維數(shù)學模型[J];爆炸與沖擊;1984年03期

3 莊世勇,盧文川,高洪亮;逐孔起爆在南芬露天礦深孔爆破中的應(yīng)用[J];金屬礦山;2002年08期

4 王德勝,龔敏,于亞倫,陳紹潘,焦永斌;首鋼孟家溝千噸級露天揭頂定向拋擲大爆破[J];金屬礦山;2003年12期

5 黃小明;張瑞新;翟守俊;丁新啟;尚文凱;常永剛;;安家?guī)X露天礦臺階爆破質(zhì)量優(yōu)化研究[J];金屬礦山;2007年12期

6 劉全貴;;爆破質(zhì)量對露天礦開采綜合成本的影響分析[J];現(xiàn)代礦業(yè);2009年04期

7 鄔慶生;現(xiàn)代巖石爆破數(shù)學模型的進展[J];長沙礦山研究院季刊;1986年04期

8 李廣悅,譚臻,李長山,黃永忠;原地爆破浸出筑堆塊度預(yù)測的神經(jīng)網(wǎng)絡(luò)方法研究[J];礦業(yè)研究與開發(fā);2003年06期

9 劉果;;平朔東露天煤礦工作面臺階高度的優(yōu)化[J];露天采礦技術(shù);2012年05期

10 王海;范忠勝;李淑艷;;高段爆破技術(shù)在露天生產(chǎn)中的應(yīng)用[J];煤炭技術(shù);2006年12期

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