Faculty Publications
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Item Impact of Enhanced Production from the Opencast Coal Mines on Ambient Particulate Emissions(Springer Science and Business Media Deutschland GmbH, 2022) Podicheti, R.K.; Ram Chandar, K.R.Coal still continues to be the major source of energy needs. It is estimated that power sector alone would require about 900 Mt of coal by the year 2025. Such large quantity requires more surface mines causing the load on environment. Among the various environmental factors, air pollution is one of the most important parameters to be considered and the estimation of emissions from a mine at various stages of its operations is vital which helps in taking preventive measures against pollution. Given the significance of mining as a source of particulates, accurate characterization of emissions is important for the development of appropriate emission estimation techniques for use in modeling predictions and for regulatory decisions. Estimation of emissions in the ambient air quality with regard to enhanced production needs to be established regionally. Keeping this in view, two opencast coal mines in South India are selected to evaluate the emissions of PM10 and PM2.5 in the ambient air due to increased coal production and overburden removal. Emissions for these two mines have been studied from the year 2012 to 2019. It was observed that the PM10 values ranged from 152 to 229 µg/m3 in the core zone and 71 to 98.2 µg/m3in the buffer zone. Similarly, PM2.5 values ranged from 49.4 to 80.9 µg/m3in the core zone and 25.3 to 55.3 µg/m3 in the buffer zone. Particulate emissions have increased proportionately with respect to the quantity of coal produced and overburden removed. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Analysis of Concentration of Ambient Particulate Matter in the Surrounding Area of an Opencast Coal Mine using Machine Learning Techniques(Shahrood University of Technology, 2024) Podicheti, R.K.; Karra, R.C.Opencast coal mines play a crucial role in meeting the energy demands of a country. However, the operations will result in deterioration of ambient air quality, particularly due to particulate emissions. The dispersion of particulate matter will vary based on the mining parameters and local meteorological conditions. There is a need to establish a suitable model for predicting the concentration of particulate matter on a regional basis. Though a number of dispersion models exist for prediction of dust concentration due to opencast mining, machine learning offers several advantages over traditional modeling techniques in terms of data driven insights, non-linearity, flexibility, handling complex interactions, anomaly detection, etc. An attempt has been made to assess the dispersion of particulate matter using machine learning techniques by considering the mining and meteorological parameters. Historical data comprising of mine working parameters, meteorological conditions, and particulate matter pertaining to one of the operating opencast coal mines in southern India has been utilized for the study. The data has been analyzed using different machine learning techniques like bagging, random forest, and decision tree. The performance metrics of test data are compared for different models in order to find the best fit model among the three techniques. It is found that for PM10, many of the times bagging technique gave a better accuracy, and for PM2.5, decision tree technique gave a better accuracy. Integration of mine working parameters with meteorological conditions and historical data of particulate matter in developing the model using machine learning techniques has helped in making more accurate predictions. © 2024, Shahrood University of Technology. All rights reserved.