Executive Summary : | Objective: Real-time monitoring of ground control parameters without human involvement before, during and after the progressive roof fall in the CM deployed depillaring panels 2) Acoustic (high frequency) and micro-seismic (low frequency) monitoring of roof rock in the CM depillaring panel and its surroundings; before, during and after the progressive roof falls.
3) Development of prediction model on the basis of outcomes of (1) and (2) for progressive roof falls.
4) Validation of predictive model in CM depillaring panels in different geo-mining conditions.
5) Technical guideline for warning of progressive roof falls along with quantification of time margins for roof falls in different geo-mining conditions in CM depillaring panels Summary: In underground coal mines, roof-predictions are an essential exercise to achieve zero accidents and downtime during extraction of coal. However, there is very limited study and information is available on prediction of failure of roof or rock mass during the depillaring operation in underground coal mines. Furthermore, there are no clear-cut methodologies or technologies available to predict the roof-falls prior to their occurrences suiting to the geomining conditions of Indian coal mines. Therefore, this project aims to develop prediction model and technical guideline which can give warning of progressive roof falls along with quantification of time margins for roof falls in underground coal mines in different geo- mining conditions which are being liquidated with continuous miner. This project involves the continuous monitoring, as opposed to the point monitoring, of the depillaring operations in underground coal mines using state-of-the-art vibrating wire based geotechnical sensors and real-time ground safety analysis system developed at CSIR-CIMFR Dhanbad. There will be two categories of these sensors-one for crack initiation and propagation and another for post separation of strata in the roof. To assess the crack behavior, high and low frequency sensors will be utilized. On the other hand, to know the behavior of rock separation, stress, convergence, and load sensors will be used. All these sensor data will be analysed using big data analytics to arrive at a prediction model which will be validated in the working CM panels at chosen sites in Indian coalfields. Further, a technical guideline will be formulated for the mine operators to predict the roof fall without taking any help from the experts. In sum, the proposed research project shall lead to mass production technology with maximum recovery of coal and better safety for extraction panels of underground coal mines. |
