A Study of Air Pollution load assessment around opencast coal project in India

Opencast mining technology results in the release of a huge amount of air borne dust. The air borne dust peculiarly below 100 micron in size, are environmentally nuisance and cause health hazards. Total suspended particulate matter (TSPM) and respiratory particulate mater (PM10) are the major pollutants in the air environment of opencast coal mines. Therefore, dust generation, its dispersion, and pollution load assessment have been found to be major concer4ns in air quality modeling of opencast coal mines. The present paper focuses on the quantification of sourcewise emission inventory for different point, area and line sources considering the background dust concentration at one of opencast coal project (OCP), nakmely Hindustan Lalpet of Western Coalfields Limited (WCL). The 24 hr average concentrations of TSPM and PM10 were monitored at three monitoring stations during winter season. On an average the PM10 concentration in the ambient air constituted 17.00 to 60.3% of TSPM concentration. TSPM concentration ranged from 313.11 to 565.57 µg/m3 and 79.48 to 270.61 µg/m3

APPLICATION OF ARTIFICIAL NEURAL NETWORK FOR BLAST PERFORMANCE EVALUATION

Despite of technological advancement in the field of rock breakage, blasting is still an economical means of rock excavation for mining or civil engineering projects. Blasting has some environmental as well as societal side effects such as ground vibrations, air blasts, noises, back breaks, fly rocks, dusts and, of course, annoyance of inhabitants living surrounding the mining areas. Recent developments in the field of blasting techniques can minimize such ill effects by optimizing rock friendly explosive and blast design parameters. The purpose of this paper is to present the techniques, advances, problems and likely direction of future developments in exploring the applications of Artificial Neural Networks (ANN) in rock fragmentation by blasting and its significance in minimizing side effects to environment in particular and society at large. Many researchers have found the back-propagation algorithm in ANN is especially capable of solving predictive problems in rock blasting. ANN has been successfully applied in predicting, controlling, assessing impact of blast design parameters, ground vibrations, air blasts, back breaks etc. in mines. ANN needs to be applied in certain grey areas like predicting and controlling fly rock hazards in opencast mines, blast induced dust, blasting in jointed rockmass etc

Simultaneous prediction of blast-induced flyrock and fragmentation in opencast limestone mines using back propagation neural network

Goal of blasting operations is to achieve desired fragment size to operate the mine and plant economically while maintaining safety that includes prevention of flyrock accidents. This paper focuses on the simultaneous prediction of flyrock distance and fragmentation using back propagation neural network techniques. Thus, linear charge concentration, burden, spacing, stemming length, specific charge, unconfined compressive strength and rock quality designation are taken as input. Flyrock distance and fragment size are chosen as output. The predicted outputs by back propagation neural network (BPNN), multi variate regression analysis (MVRA) have been compared. The quite lower root mean square error (RMSE) and mean absolute error (MAE) in BPNN than MVRA prove that BPNN is a better prediction method. Also, the predicted output in BPNN correlates better with the observed output than MVRA. Sensitivity analysis for both independent variables for BPNN and MVRA is also included in this paper

Exploring Rock-Explosive Interaction Through Cross Blasthole Pressure Measurements

The detonation of the explosive in a blasthole generates pressure to the tune of several giga-pascals that in turn drives the breakage of rockmass in several complex ways. However, nature and the interactions of such pressure with the surrounding rockmass are not explicitly known. This is primarily due to the destructive and rapid nature of the process that hinders measurements in or around the blasthole. Accordingly, such tests are quite cumbersome, complex and costly. The instrumentation has to have standard specifications with several mega-samples per second monitoring capacity. Also, the sensors deployed for the purpose should have a steep rise time because of the fact that the event is transitory in nature. This may be one of the reasons that the focus of researchers has been on determination of energies involved in the process by indirect means. There have been attempts to understand the blasthole pressure and its interactions with the nearby rockmass but due to expensive nature of testing, the progress is not as anticipated. This study attempted the cross blasthole pressure measurements by placing pressure sensors at a distance from the blasthole. The paper presents some examples of cross borehole pressures measured in varied type of rock masses with a good range of acoustic impedance of the rockmass and its joint spacing. Attempt has been made to explain the pressure traces with the aim that further studies in this domain will lead to better understanding of rock–explosive interaction. The method is quite in infancy and it is expected that future studies shall aid in standardisation of the method thus developed

Prediction of Blast-induced flyrock in Indian limestone mines using neural networks

Frequency and scale of the blasting events are increasing to boost limestone production. Mines are approaching close to inhabited areas due to growing population and limited availability of land resources which has challenged the management to go for safe blasts with special reference to opencast mining. The study aims to predict the distance covered by the flyrock induced by blasting using artificial neural network (ANN) and multi-variate regression analysis (MVRA) for better assessment. Blast design and geotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge, unconfined compressive strength (UCS), and rock quality designation (RQD), have been selected as input parameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets of experimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used for testing and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA, as well as further calculated using motion analysis of flyrock projectiles and compared with the observed data. Back propagation neural network (BPNN) has been proven to be a superior predictive tool when compared with MVRA

Optimum Utilization of Continuous Miner for Optimization of Production in Board and Pillar Mining Method in Indian Underground Coal Mines

In present contest with the past, production optimization has become important goal for industries. With reference to past few decades’ underground coal mining operations uses continuous mining technology to extract coal reserves from the underground coal seams. Matching with the continuous miner shuttle cars are used to transport the coal from the face to a feeder breaker. After that, coal is generally tilted over the conveyor system in underground that transport to the surface for distribution to consumers most of power generation plant of India. The availability of coal and methods of mining plays the key role, but in this area of continuous technology, miner feels a safe and environment-friendly path for increasing the overall production. Technology suits for obtaining the target production according to Indian coal seams occurrence as per geological structures. Productivity may improve by effective management of breakdown of the using machineries. This paper is purpose to identify the various factor and problems affecting the productivity of the underground coal mine

Shovel-truck optimization study in an opencast min-A queueing approach

153-157The shovel-truck combination is the versatile work horse in small as well as large opencast mining projects. The optimization of shovel-truck combination is imperative in order to increase productivity from this system. The optimization studies aim at eliminating the excess shovel or excess truck capacity in order to minimise the shovel and/or truck idling period. The present case-study is the approach of queueing theory to the optimization of shovel-truck combination in an Indian open cast mine. The formulation of appropriate queueing model has been undertaken and the (M/M/C); (FIFO/K/K) model has been adjudged to be the best model to suit the shovel-truck optimization problem. Relationships between the length of queues, waiting time in queue, utilisation of shover, approximate output from the shovel-truck combination and the costs involved in the system are critically analysed with respect to variation in the number of trucks. The optimum total cost and the optimum number of trucks in the case-study are deduced from the proposed queueing model.</span

Cycle time and idle time analysis of draglines for increased productivity —A case study

77-81<span style="font-size: 16.0pt;mso-bidi-font-size:9.0pt;font-family:" times="" new="" roman","serif""="">In India, massive opencast mines are being planned, with dragline as an excavator, to strip out large volumes of overburden <span style="font-size:15.0pt; mso-bidi-font-size:8.0pt;font-family:" arial","sans-serif""="">(O/B ) at a high stripping ratio. The increased use of draglines is primarily because the draglines have tremendous flexibility of operation at varying OIB <span style="font-size:16.0pt;mso-bidi-font-size:9.0pt;font-family: " times="" new="" roman","serif""="">depths. Besides, it is capable of giving higher rate of <span style="font-size:15.0pt;mso-bidi-font-size:8.0pt;font-family: " arial","sans-serif""="">OIB <span style="font-size:16.0pt;mso-bidi-font-size: 9.0pt;font-family:" times="" new="" roman","serif""="">removal at relatively low operating cost. However, at the same time, it must be distinctly borne in mind that the dragline is single most expensive equipment to be procured by a mine. Hence, analysis of cycle time and idle time is of tremendous significance with such capital intensive equipment. Such analysis paves the way for improvement in the utilization, production and productivity of this equipment. The present study is based on field investigation conducted on draglines in one of the most prestigious opencast coal mines of Coal India Limited. The study critically analyses the cycle time and the idle time distribution of the draglines, as operating in the field. </span