Working against nature and uncertain environment makes underground mining a hazardous profession. Every year hundreds of miners lose their valuable lives due to mine hazards. Increasing demand for coal necessitates the extraction of coal at a higher rate. As a result, easily minable shallow coal deposits are depleting speedily, and in near future, deep seated deposits will be left for mining by underground methods. With rising depth and deployment of high-capacity machines, increasing heat stress becomes a major hazard in underground mine environment posing threat to the miners' health, productivity and safety. Ignoring the effect of heat stress may lead to dangerous circumstances, even result in death. To avoid such unwanted event, it has become imperative to predict the heat stress to reduce its adverse impact in underground coal mines. Therefore, in this study a detailed eld survey is conducted to collect the environmental data of three underground coal mines. Genetic programming (GP) is done to develop relation between the environmental parameters and heat stress, by taking the mine survey data as input. The good correlation coe cient (R=0.9816) is obtained between the GP predicted heat stress and actually measured heat stress, which indicates that GP can be effectively used to predict the heat stress in underground mines. A sensitivity analysis (SA) is done to determine the effect of input parameters on heat stress. The SA results reveled that all six input parameters have considerable effect on the heat stress, however, dry-bulb temperature has the highest effect (0.98) on heat stress.
Geological sequestration of CO 2 in a coal seam is considered an attractive option to reduce the carbon footprint. It has an additional advantage of enhancing the recovery of coalbed methane, which has less sorption affinity toward coal in comparison to CO 2 . Desorption of gases from coal is controlled by various parameters, including reservoir depth and coal rank. A representative factor for desorption and diffusion in coal is the sorption time. It is an indicator which helps in estimation and evaluation of gas movement in the coal seam. Coals exhibiting high sorption time allow greater quantities of CO 2 injection and hold potential for CO 2 sequestration. Therefore, reliable and cost-effective estimation of sorption time is very important prior to investment in projects related to CO 2 sequestration. Generally, proximate and gas content analyses are part of the preliminary analysis of coal for the assessment of its potential as a coal-bed methane reservoir. In this study, data generated using these analyses were found very useful for estimating the sorption time and CO 2 sequestration potential of coal. The coal samples were collected from different depths of the Mand Raigarh coalfield for testing, and an empirical equation and artificial neural network (ANN)-based model have been developed to predict the sorption time of coal. The developed empirical equation predicts the sorption time with a coefficient of determination value of 0.88 and a root mean squared error value of ±1.07 days. Furthermore, the developed ANN model has been found to be very efficient in prediction with a correlation coefficient value of 0.97.
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