Central Mine Planning and Design Institute Ltd

Ranchi, India

Central Mine Planning and Design Institute Ltd

Ranchi, India
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Singh R.,Central Mine Planning and Design Institute Ltd | Syed T.H.,IIT ISM | Kumar S.,Central Ground Water Board | Kumar M.,Central Mine Planning and Design Institute Ltd | Venkatesh A.S.,IIT ISM
Arabian Journal of Geosciences | Year: 2017

The present study assesses the impact of coal mining on surface and groundwater resources of Korba Coalfield, Central India. Accordingly, water samples collected from various sources are analyzed for major ions, trace elements, and other mine effluent parameters. Results show that the groundwater samples are slightly acidic, whereas river water and mine water samples are mildly alkaline. Elevated concentrations of Ca2+, Na+, HCO3 −, and SO4 2− alongside the molar ratios (Ca2++Mg2+)/(SO4 2−+HCO3 −) <1 and Na+/Cl− >1 suggest that silicate weathering (water-rock interaction) coupled with ion exchange are dominant solute acquisition processes controlling the chemistry of groundwater in the study area. The overall hydrogeochemistry of the area is dominated by two major hydrogeochemical facies (i.e., Ca–Cl–SO4 and Ca–HCO3). Analysis of groundwater and river water quality index (GRWQI) elucidates that majority (82%) of samples are of “excellent” to “good” category, and the remaining 12% are of “poor” quality. Similarly, the effluent water quality index (EWQI) indicates that 6 out of 8 samples belong to excellent quality. Concentration of trace element constituents such as As, Zn, Cu, Cr, and Cd is found to be well within the stipulated limits for potable use, except for Fe, Mn, and Pb. Suitability of water samples for irrigation purpose, established using standard tools like Wilcox and USSL diagrams, reveal “excellent to permissible” category for majority of the samples. The present study also substantiates the effectiveness of the measures implemented for the treatment of mine effluent water. © 2017, Saudi Society for Geosciences.

Singh R.,Central Mine Planning and Design Institute Ltd | Venkatesh A.S.,Indian School of Mines | Syed T.H.,Indian School of Mines | Reddy A.G.S.,National Water Research Institute | And 2 more authors.
Environmental Earth Sciences | Year: 2017

Exploiting coal by open-cast mining often poses a threat to groundwater chemistry due to leachate of contaminants from the mine drainage water. In order to assess this, groundwater, river water and mine water samples were collected from Korba Coalfield and analysed for potentially toxic trace elements (PTEs) along with in situ parameters. Thereby, an integrated approach of pollution evaluation indices [heavy metal pollution index (HPI), heavy metal evaluation index (HEI) and contamination index (Cd)] and statistical techniques are applied to the results. Paired-sample t tests reveal that the PTE concentrations of the pre-monsoon samples are higher than those of the post-monsoon samples. At a few locations, the concentration of Fe (56%), As (56%), Al (26%), Mn (19%) in pre-monsoon and Mn (46%), Ni (15%), Ba (15%), Pb (8%) in post-monsoon seasons exceeds the acceptable limit of Indian drinking standards. However, HPI values are below the critical pollution index value of 100 in both seasons in spite of an excess of these elements. The pollution indices evaluated by the multiple of the mean values approach reveal that 100, 94, 87% of the pre-monsoon and 92, 92, 85% of the post-monsoon samples belong to the “low-to-medium” category with respect to the HPI, HEI and Cd indices. The thematic map depicting the spatial distribution of the contamination index (Cd) testifies the role of a “dilution effect” that results in lower pollution loads in the post-monsoon samples than the pre-monsoon ones. The results of cluster analysis confirm that the quality of the water is mainly controlled by geogenic processes and anthropogenic inputs, besides dilution effects. © 2017, Springer-Verlag GmbH Germany.

Ghosh S.,Central Mine Planning and Design Institute Ltd | Chatterjee R.,Indian School of Mines | Shanker P.,Central Mine Planning and Design Institute Ltd
Fuel | Year: 2014

This paper aims to design plug-in at matrix laboratory (MATLAB) for estimation of coal proximate parameters and coal seam correlation from well logs. Five major sub-functions such as: normalisation of single point resistance log, shifting of logs for depth matching, lithology marking, calibration for estimation of coal proximate parameters and coal seam correlation are included with graphical user interfaces. The geophysical log data of Jharia and Bishrampur coalfields, India are calibrated visually with the laboratory analysed data where recovery is considerable for a certain number of scattered boreholes designated as master boreholes. Multiple linear regression relationships are obtained between geophysical logging parameters: density, natural gamma ray, normalised single point resistance and coal proximate parameters: ash, moisture content and fixed carbon. The software then proceeds towards the estimation of ash, moisture and fixed carbon (band by band) for the other boreholes except master boreholes. In the present study the coal seams are correlated from total 5 and 15 numbers of exploratory wells in Jharia and Bishrampur coalfields respectively. The correlation is made and accordingly correlation table is prepared using graphic display mode of the software by matching the lithology of all the boreholes and correlation plot is generated during the process. © 2014 Elsevier Ltd. All rights reserved.

Ghosh S.,Central Mine Planning and Design Institute Ltd | Chatterjee R.,Indian School of Mines | Shanker P.,Central Mine Planning and Design Institute Ltd
Fuel | Year: 2016

Coal core samples and well log data of five exploratory wells of Korba Coalfield (CF), India have been used for prediction of coal facies. The Indian non-coking coal lithofacies are generally classified by analyzing the variation of the geophysical log parameters or by defining the ranges of various proximate parameters (mainly ash % and moisture %) obtained from coal core samples. The objective is to classify each layer as coal, shaly coal and shale depending upon the content of ash % and moisture % of the corresponding layer in coaly horizon. Hierarchical Cluster Analysis (HCA) is applied to classify the non-coal horizons and bands of identified coal seams of each well under the study area based on geophysical log responses: natural gamma ray (NG), high resolution density (HRD) and single point resistance (SPR). Hierarchical clustering separates the zones in a particular coal seam from five wells using the nature of the curve. These zones/clusters are further identified as coal, shaly coal, shale in three wells using regression and multilayer feed forward neural network. The log responses and coal core analyzed proximate parameters of these isolated bands/zones in two wells are used for establishing linear regression and neural network models. The observation shows very satisfactory fit (R2 = 0.84) between ash content and HRD and poor R2(<0.41) between moisture content and log responses. The MLFN model is based on study of two wells using NG, HRD and SPR log responses as inputs and coal proximate parameters, namely, ash and moisture content as outputs to classify the coal lithofacies. The bands within a coal seam are classified on the basis of the ash and moisture content while training as well as the validation of the model. These linear and MLFN models are used to determine the ash % and moisture % in the remaining three testing wells. MLFN predicted results are more closely to the laboratory analyzed proximate parameters as compared to the results obtained from regression modelling. © 2016 Elsevier Ltd. All rights reserved.

Ghosh S.,Central Mine Planning and Design Institute Ltd | Chatterjee R.,Indian School of Mines | Shanker P.,Central Mine Planning and Design Institute Ltd
Energy and Fuels | Year: 2016

The energy of coal is expressed by its useful heat value (UHV), and it is the major key player in coal pricing. The objectives of this paper are to obtain (a) the regression relationship between coal proximate parameters and UHVs, and (b) multilayered feed forward neural network (MLFN) models between geophysical log responses and UHVs. Six wells are used for training the networks, and three wells are used for validating the obtained results in the Bishrampur coalfield. The mean square error (MSE) of MLFN models along with correlation (R2) values at their training, validation, and testing stages are the criteria for selecting the best model for estimation of coal proximate parameters and UHVs using geophysical log responses. The final model is selected based on low MSE (≤0.07) and high R2 values (≥0.80) at training, validation, and testing stages. The predicted UHV obtained from the best MLFN model has excellent correlation (R2 = 0.98) with the laboratory determined UHVs of three major coal seams. The predicted UHV is further implemented to grade the three coal seams of this coalfield. © 2016 American Chemical Society.

Das K.C.,Indian Institute of Technology Kharagpur | Deb D.,Indian Institute of Technology Kharagpur | Jha A.K.,Central Mine Planning and Design Institute Ltd
Geosystem Engineering | Year: 2013

The stability of underground structure made especially in jointed rock mass is of the utmost important to designer, engineers and operators. Rock bolting is generally being practised to reinforced excavation walls and roofs by minimizing the movement of rock joints. In this study, a numerical procedure has been developed in extended finite element framework (XFEM) to analyze the behaviour of grouted bolt intersected by a joint. A solid finite element intersected by a bolt and a joint along any arbitrary direction is termed as 'doubly enriched' element. Nodes of an doubly enriched element have additional degrees of freedom for determining displacements, stresses developed in the bolt rod as well as the displacements jump and traction along the joint. The paper also provides verifications of this procedure by solving two known examples (i) direct shear test performed on a bolted joint sample-experimental verification and (ii) reinforcement of a joint located in the vicinity of a circular tunnel-analytical verification. © 2013 Copyright Taylor and Francis Group, LLC.

Mani D.,CSIR - Central Electrochemical Research Institute | Patil D.J.,CSIR - Central Electrochemical Research Institute | Dayal A.M.,CSIR - Central Electrochemical Research Institute | Prasad B.N.,Central Mine Planning and Design Institute Ltd
Marine and Petroleum Geology | Year: 2015

This study investigates the source rock characteristics of Permian shales from the Jharia sub-basin of Damodar Valley in Eastern India. Borehole shales from the Raniganj, Barren Measure and Barakar Formations were subjected to bulk and quantitative pyrolysis, carbon isotope measurements, mineral identification and organic petrography. The results obtained were used to predict the abundance, source and maturity of kerogen, along with kinetic parameters for its thermal breakdown into simpler hydrocarbons.The shales are characterized by a high TOC (>3.4%), mature to post-mature, heterogeneous Type II-III kerogen. Raniganj and Barren Measure shales are in mature, late oil generation stage (Rr%Raniganj=0.99-1.22; Rr%Barren Measure=1.1-1.41). Vitrinite is the dominant maceral in these shales. Barakar shows a post-mature kerogen in gas generation stage (Rr%Barakar=1.11-2.0) and consist mainly of inertinite and vitrinite. The δ13Corg value of kerogen concentrate from Barren Measure shale indicates a lacustrine/marine origin (-24.6--30.84‰ vs. VPDB) and that of Raniganj and Barakar (-22.72--25.03‰ vs. VPDB) show the organic provenance to be continental. The δ13C ratio of thermo-labile hydrocarbons (C1-C3) in Barren Measure suggests a thermogenic source.Discrete bulk kinetic parameters indicate that Raniganj has lower activation energies (δE=42-62kcal/mol) compared to Barren Measure and Barakar (δE=44-68kcal/mol). Temperature for onset (10%), middle (50%) and end (90%) of kerogen transformation is least for Raniganj, followed by Barren Measure and Barakar. Mineral content is dominated by quartz (42-63%), siderite (9-15%) and clay (14-29%). Permian shales, in particular the Barren Measure, as inferred from the results of our study, demonstrate excellent properties of a potential shale gas system. © 2015 Elsevier Ltd.

Roy M.P.,Indian Central Institute of Mining and Fuel Research | Singh P.K.,Indian Central Institute of Mining and Fuel Research | Jha A.K.,Central Mine Planning and Design Institute Ltd | Basu D.,Central Mine Planning and Design Institute Ltd
Performance of Explosives and New Developments: Workshop Hosted by FRAGBLAST 10 - The 10th International Symposium on Rock Fragmentation by Blasting | Year: 2013

The theoretical treatments of detonation process under both ideal and non-ideal conditions are noteworthy, but they are still based on somewhat hypothetical situations. The actual variables that are essential parts of normal blasting practice have not yet been taken into account in such treatments. These include the various initiation practices employed to detonate a column of explosive, from single point initiation to multi-point initiation in blast holes, and the effect on detonation characteristics of both detonators and explosives under multi-deck and multi-hole blasting conditions. The in-the-hole VOD of an industrial explosive is dependent on explosive's charge diameter and borehole diameter. The in-hole VOD of some standard commercial explosives was measured at four experimental sites for different borehole diameters i.e. 150 mm to 311 mm, withthe explosive parameters (i.e. composition, density, particle size, viscosity and confinement) being kept constant.The results of the studies demonstrated that there is definite relationship in in-the-hole VOD of the explosive and the diameter of the blast hole. The study also confirmed that the explosives initiated with concentrated boosters yielded higher VOD in comparison to those explosives that were initiated with multi-point priming. However, this result is clearly anomalous, and additional tests have to be performed to study this effect further. The measured increase in VOD of explosives for increasing diameter of holes was up to 24%. The rate of change in in-the-hole VOD of explosives increases with increasing borehole diameter. It can be further stated that the in-thehole VOD of the explosive reaches a fairly constant value after reaching a limiting/threshold diameter of 311 mm.

Roy S.,National Institute of Rock Mechanics | Adhikari G.R.,National Institute of Rock Mechanics | Renaldy T.A.,National Institute of Rock Mechanics | Jha A.K.,Central Mine Planning and Design Institute Ltd
Journal of Environmental Science and Technology | Year: 2011

Blasting is one of the essential operations at surface coal mines but it emits large amount of dust into the atmosphere. Prediction of dust concentrations can help in air quality management in mining. In this study, particulate matter generated due to blasting was monitored in different seasons at a large opencast coal mines in India. Blast design parameters, moisture content of the benches and distance of dust samplers from blast locations were recorded. Unlike ambient dust monitoring for industrial activities, blast monitoring was carried out for a period of dust accumulation at various locations in the downwind direction. Blast site varied from one location to other in the mine. Based on the observations of many blasts, monitoring period was determined. Scatterplots and correlation matrices for different variables were plotted. Stepwise regression procedure was carried out for selection of most influencing variables. Incorporating selected variables, multiple regression and neural network models were developed for prediction of particulate matter. The performance of the multiple regression models was assessed. For the development of neural network models, a feed forward with back propagation learning algorithm was used to train the network. The performance of neural network was determined in terms of correlation coefficient (R) and Mean Square Error (MSE). The optimum number of hidden neurons was found out for obtaining the lowest value of MSE and the highest value of R. The results indicated that the network can predict particulate concentrations better than multiple regression models. © 2011 Asian Network for Scientific Information.

Majumdar P.,Central Mine Planning and Design Institute Ltd.
Journal of Mines, Metals and Fuels | Year: 2010

The increasing consumption, coupled with the rising cost and scarcity of diesel has made it all the more necessary to scrutinize its use and find out opportunities for its conservation to the maximum extent possible. Dumpers take much share of diesel consumption, need attention and topmost priority for its conservation. The areas needing attention include loading and waiting time, speed and haul road condition, pay load, restrictions of unnecessary movement and maintenance. Proper action should be taken to prevent the diesel leakage. Because leaks in diesel lines mostly remain undetected/unattended till maintenance is taken up. A correct approach to information system is to link the consumption data with different output data. Only the comprehensive data and its analysis will enable the user to achieve effective conservation that is, efficient utilization of inputs to produce maximum output.

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