Prediction of Roof Failure in Pre-driven Entries and Selecting a Suitable Type of Recovery Room Method in Longwall Mining
محل انتشار: مجله معدن و محیط زیست، دوره: 15، شماره: 1
سال انتشار: 1403
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 73
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شناسه ملی سند علمی:
JR_JMAE-15-1_012
تاریخ نمایه سازی: 20 دی 1402
چکیده مقاله:
In this work, two rock engineering system (RES)-based models are presented, the first model to predict the roof failure when a longwall face advances toward a pre-driven recovery room (PDRR) and the second model to select the type of recovery room method for longwall mining. For the first model, an international database of ۴۳ case histories from the pre-driven rooms including technical parameters and type of corresponding operation outcome of each case history is considered. In this regard, a vulnerability index (VI) that refers to the risk of roof failure is calculated for each case history and the VIs are compared with the type of the corresponding outcomes. The obtained results indicate that the calculated VIs have a good adaptation with the corresponding outcomes. This approach could be used to analyze the risk of failure in PDRR, and determine the critical VI that specifies the boundary between the hazard range and the safe range that leads to an accurate operational planning. In the following, a method called multi-options RES-based model (MORESM) is adopted for the selection of recovery room methods in longwall operation. By this model, selecting the optimum option from several options in terms of many effective parameters on the system is possible. Based on the evaluations, CRR, PDRR۳, and PDRR۲&۳ are the suitable options for the case study. This model could introduce the suitable option based on geotechnical conditions but the final decision depends on the economic policy of the managing team.
کلیدواژه ها:
Recovery room ، pre-driven entries ، Longwall Mining ، Rock Engineering system ، Multiple-options RES-based model (MORESM)
نویسندگان
Sajjad Aghababaei
Shahid Bahonar University of Kerman, Department of Mining Engineering, Kerman, Iran
Hossein Jalalifar
Shahid Bahonar University of Kerman, Department of Mining Engineering, Kerman, Iran
Ali Hosseini
Department of Mining and Metallurgical Eng., Yazd University, Yazd, Iran
Farhad Chinaei
Department of Mining Engineering, Meymeh Branch, Islamic Azad University, Meymeh, Iran
Mehdi Najafi
Department of Mining and Metallurgical Eng., Yazd University, Yazd, Iran
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