Faculty Publications

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Subject: search.filters.subject.Opencast coal mine

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    Evolution of the Probability Distribution Function of Shovel – Dumper Combination in Opencast Coal Mine Using ANN and RWB
    (Springer Nature, 2020) N. S., N.S.; Choudhary, R.P.; Murthy, C.S.N.
    This article presents a new analytic calculation for the shovel – dumper combination in opencast coal mine evolution of the one and two galaxy probability distribution function (PDF). To develop a nonparametric PDF for a combination of shovel and dumper in a opencast coal mine, the historical breakdown data such as time between failure (TBF) of a shovel and dumpers were collected from the mine. Based on the collected TBF, Weibull parameters such as the shape parameter (β), scale parameter (η) and location parameter (γ) were calculated under the K-S test (Kolmogorov–Smirnov test). A Weibull distribution model has been developed to obtain the one and two galaxy probability distribution function (PDF) for a collected failure data of shovel-dumper system using Reliability Isograph Workbench (RWB). Also, Artificial Neural Network (ANN) model has been developed to predict the PDF for the same shovel-dumper system and compared with the actual obtained value of Reliability Isograph Workbench (RWB). It was found that the values of RMSE and R2 were 0.00068 & 0.9465 for PDF. The statistical results showed that the proposed Reliability Isograph Workbench and ANN model successfully predicts PDF for the shovel-dumper system. © 2020, Springer Nature Switzerland AG.
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    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.