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.
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 field 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 coefficient (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.
In both the Indian urban and the rural sectors, the demand for cotton textiles has not only been relatively stagnant, but the top 10 per cent of the population in terms of purchasing power has been raising its share of total expenditure on clothing at the expense of the poorer sections. While until 1973 the demand for finer varieties of cloth grew rapidly and that for coarse and medium varieties shrank, since the mid-70s competition from blended and synthetic fabrics and from the decentralized sector have hit all varieties of mill-made cotton cloth. Estimates of price elasticities of cloth and foodgrains indicate that it may be possible to stimulate cotton textile demand by reducing the margins on mill-made cloth. This would require some restructuring of the mill cloth distribution system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.