采礦外文翻譯---采礦工業(yè)中實用的神經(jīng)網(wǎng)絡(luò)應(yīng)用程序-采礦工程(已修改)
2025-02-04 01:34 本頁面
【正文】 英文原文 Practical Neural Network Applications in the Mining Industry L. MillerTait, R. Pakalnis Department of Mining and Mineral Process Engineering, University of British Columbia, Vancouver, ., Canada ABSTRACT The mining industry relies heavily upon empirical analysis for design and prediction. Neural works are puter programs that use parallel processing, similar to the human brain, to analyze data for trends and correlation. Two practical neural work applications in the mining industry would be rockburst prediction and stope dilution estimates. This paper summarizes neural work data analysis results for a 1995 Goldcorp/Canmet study on rockbursting and a 1986 UBC/Canmet study on open stope dilution at the Ruttan Mine. 1. INTRODUCTION Many aspects of mine design are based upon empirical data. Neural Networks analyze data and predictions based on previous results. Neural works have advantages over conventional empirical design approaches. These advantages include: ? Neural works can easily use multiple inputs to analyze data. ? By using multiple hidden layers and nodes neural works investigate the bined influence of inputs. ? Neural works can be easily retrained as new data bees available making them a more dynamic and flexible empirical estimation approach. ? Neural work software is inexpensive and easy to use. ? Neural works have demonstrated a more accurate empirical estimate over conventional methods. The advantages of using neural works are illustrated in a rockburst prediction example and an open stope dilution example. 2. ROCKBURST PREDICTION The first example of a potential situation where neural works could be useful in the mining industry is the prediction of rockbursts through physical inputs. To quote directly from the Ontario Ministry of Labor “...we do not have the ability to predict when and where rockbursts will occur, and the experts in the field agree that we are not close to make such predictions” [1]. Between 1984 and 1993 eight underground miners were killed in Ontario due to rockbursts. This accounted for approximately 10% of underground fatalities during this period. If neural works were to have success in predicting where rockbursts occur, additional ground support, remote equipment, and/or design modifications could reduce or possibly eliminate fatalities due to rockburst. As safety is the primary responsibility of mining engineers, the potential for neural works to assist in predicting rockburst inputs should be investigated. In 1995, a joint project was pleted by Goldcorp Inc. and Canmet called “Development of Empirical Design Techniques in Burst Prone Ground at A. W. White Mine” [2]. Part of the study was to collect input information on rockburst, caving, ground wedge, and roof fall failures at the A. W. White Mine between 1992 and 1995. This resulted in a failure database consisting of 88 ground failures with corresponding inputs for each failure. The six inputs collected for each failure were RMR [3], Q [4], span [5], SRF’[2],RMR adjustment, and depth. These input factors were set up and run in a neural work with 73 examples being used for training and 15 examples being used to test the work. The output factor, stability, can be one of four failures [2] PUNRF (potentially unstable roof fall), PUNGW (potentially unstable ground wedge), BUR (rock burst), and CAV (cave). A brief description of the input and output factors are listed below. . Input factors RMR The RMR system, initially developed by Bieniawski in 1973[3], bases rock mass quality o
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