IR@CIMFR - Central Institute of Mining and Fuel Research (CSIR)
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A novel approach of high-voltage low-current electric energy input to synthesise cost-effective ultra-strong ductile material
No full textAdopting a unique electrical circuit design, here we treat a significant low-cost engineering material (eutectoid steel not containing costly alloying elements) with a high-voltage (100 kV) low-current (150 mA) energy input (energy level exceeding cohesive energy). A distinctive structural evolution is ascertained with treatment duration of only 5 min as an outcome of lamellar fragmentation and matrix supersaturation. This envisages an origin of dispersed nano-sized hard cementite spheroids embedded in nano-thick martensite crystals of stratified-tile-morphology along with distributed α-ferrite regions containing sub-microscopic cementite particles of various shapes. Apart from the conjoint effect of nano-scale dispersion strengthening and martensitic strengthening overhauling the effect of conventional lamellar strengthening on a gross scale; high dislocation density and systematically arranged dislocations of similar sign at incoherent cementite particle-matrix interface provides a unique combination of ultra-high strength (UTS ∼ 1.5 GPa), significantly high specific strength (188 MPa/g cm−3) and large ductility (%Elongation = 20). Therefore, in terms of the adopted synthesis route, structural evolution and mechanical property achieved, a new dimension is hereby added to the next-generation material development so as to meet the ever increasing demand for low-cost structural application. In turn, we elucidate a fundamental conceptualization for the first time which exemplifies disproportionate atomic migration at highly incoherent nano-sized cementite particle-martensite matrix interface in steel under high-voltage low-current energy input, resulting in accumulation of dislocations of similar sign so as to significantly enhance strength along with retention of substantial ductility
Assessment of Naphthalene Absorption Efficiency from Coke Oven Gas
No full textThe quality and quantity of coke oven gas depend on the properties of charged coal blend and cokemaking technology. In general, the raw coke oven contains naphthalene vapor in the range of 1500–2000 g per 100 Nm3 of gas which starts condensing directly into the solid form if the temperature of the gas goes down. Therefore, removal of naphthalene from coke oven gas is vital for ease of transporting through pipelines and its use as fuel. The removal process consists of scrubbing system followed by a stripping system. The scrubbing system requires efficient wash oil for absorbing the naphthalene, and the rich wash oil needs to be stripped for recycling the wash oil in the scrubbing system. In the present study, four different sources of wash oils were chosen for detailed characterization followed by aspen simulation. The stripping of naphthalene from rich wash oils shows that the HWO has better efficiency, followed by CTWO. The SWO and IWO are also having satisfactory performance. The SWO contains less sulfur and specific gravity compared to CTWO, HWO and IOWO. The characterization temperature of SWO at 5% distilled is very high (347.5°C) as compared to IOWO (330.6°C), HWO (280.3°C), and CTWO (252.1°C). Also, the temperature at 95% distilled is 373.3°C is low compared to other tested oils. The result shows that properties of SWO oil are superior for absorbing naphthalene
Beneficiation of High ash Non-coking Coals of Talcher Coalfields for Sponge Iron Industry
No full textPrediction of blast induced ground vibration for a greenfield coal mining project using numerical simulation and mathematical modelling
No full textPrediction of fly-rock during boulder blasting on infrastructure slopes using CART technique
No full textBoulder blasting is a different process from conventional bench blasting. Fly-rock produced in boulder blasting is a major safety con�cern due to the presence of 360� free-face which may result into excessive throw of the fragments radially up to 900 m distance caus�ing accidents. Many researchers have attempted to predict the fly�rock using empirical and soft computing tools in bench blasting.
But, there is paucity of literature to predict the extent of fly-rock in boulder blasting. Machine learning techniques are frequently used in bench blasting to predict ground vibrations, air overpressure, fly�rocks, but it has been rarely used in boulder blasting. In this study, an attempt has been made to use Classification and Regression Trees (CART) technique to predict the fly-rock distance in boulder blasting. Multiple linear regression (MLR) technique has been used to compare the results obtained by the CART technique. Sixty-one boulder blasting events were monitored while excavating the acci�dent-prone slope areas of Konkan Railways. The performance of the developed models using both the techniques has been evaluated using the coefficients of determination (R2
) and root-mean-square error (RSME) values. The results indicate that CART model (R2 ¼ 0.9555 and RMSE ¼ 1.141) provides better output than MLR model.
This paper suggests the use of CART technique in boulder blasting, which will be useful in execution at sensitive locations to predictand control the fly-rock distance
Energy efficiency assessment of electric shovel operating in opencast mine
No full textElectric shovels are used for loading in opencast mines. Energy efficiency assessment of electric shovel is important to minimise its electrical energy usage. Specific power consumption (SPC) has been used as performance indicator to assess energy efficiency of shovel. A modelling framework is developed for estimating SPC of electric shovel from operating time and power measured for each process. The model is illustrated with a case study of 42 cu. m. P & H shovel operating in a large opencast mine of India. The SPC of shovel is optimized for actual operating cycle time components (idle time and digging time). Results of field measurements show that digging operation consumes maximum power in comparison to other operations of electric shovel in a cycle. The model has been used to assess the energy saving potential of electric shovel by using the real time operational data. The minimum SPC of electric shovel is 0.12 kWh/cum for zero idle time and the energy saving potential is 13.45%. The optimization of SPC has also been done for different digging conditions. The model developed can help to set a target for energy consumption of electric shovel operating in a mine
Hybrid CNN-LSTM and IoT-based coal mine hazards monitoring and prediction system
No full textIoT-enabled sensor devices and machine learning methods have played an essential role in monitoring and forecasting mine hazards. In this paper, a prediction model has been proposed for improving the safety and productivity of underground coal mines using a hybrid CNN-LSTM model and IoT-enabled sensors. The hybrid CNN-LSTM model can extract spatial and temporal features from mine data and efficiently predict different mine hazards. The proposed model also improves the flexibility, scalability, and coverage area of a mine monitoring system to an underground mine's remote locations to minimize the loss of miners' lives. The proposed model efficiently predicts miner's health quality index (MHQI) for working faces and gases in goaf areas of mines. The experimental results demonstrated that the predicted mean square error of the proposed model is less than 0.0009 and 0.0025 for MHQI; 0.0011 and 0.0033 for CH4 in comparison with CNN and LSTM models, respectively. The less means square error indicates the better prediction accuracy of the trained. Similarly, the correlation coefficient (R2) value of the proposed model is found greater than 0.005 and 0.001 for MHQI; 0.007 and 0.001 for CH4 compared to CNN and LSTM models, respectively. Thus, the proposed CNN-LSTM model performed better than the two existing models
Influence of void ratio on “Blast Pull” for different confinement factors of development headings in underground metalliferous mines
No full textDevelopment headings such as adits, ramps, haulages, cross-cuts, ore drives and exploratory drives are the important structures of any underground metalliferous mines. Drilling and blasting is the most preferred excavation method used to create such drivages for extracting mineral deposits, since the progress rate of drivages impacts the overall productivity of mines. There are many controllable blast design parameters and uncontrollable geological-geotechnical parameters influencing the blast pull (i.e. advancement factor). This paper investigates the influence of void ratio (i.e. the ratio between total area of empty holes and cut area of a first shot hole) on blast pull for development faces having different confinement factors. The confinement factor is defined as the ratio of drill hole length and cross-sectional area of development headings. For this study, field investigations were performed in four lead–zinc mines. Thereafter, experimental blasts were conducted in haulage drives on the footwall side, ore drives and exploratory drives on the hanging wall side, for different confinement factors ranging from 0.10 to 0.37. Several parallel hole cut designs were experimented upon, with the void ratios varying from 19.80% to 75%. Results revealed that the void ratio plays an important role in the advancement factor of development headings. Furthermore, with an increasing confinement factor, the void ratio should be kept high for achieving maximum blast pull. Statistical analysis was carried out to establish trends between the void ratio and the confinement factor of development faces using accuracy indicators of MAPE, MAD and MSD. Eventually, a relationship was established for the effective parallel hole cut designs to achieve more than 90% advancement factor
Empirical approach-based estimation of charge factor and dimensional parameters in underground blasting
No full textUnderground blasting has many challenges. The primary
aim of blast designers is to optimize drilling and blasting pa�rameters to deal with the associated challenges. Optimizing
these parameters is currently based on various rules of thumb,
which have many site-specific limitations. Accordingly, com�putation of these parameters using empirical rock fragmenta�tion predictors can be useful for blast designers. This paper
presents the results of work on burden-spacing determination
by back-calculation from the empirical Kuz-Ram model for
underground stope ring blasting
Production of biodiesel from high free fatty acids content Jatropha curcas oil using environment affable K–Mg composite catalyst
No full textWorldwide chemical industries are concerned towards pollution‐free and green processes. In this regard, alternative energy resource having minimal pollutant and toxins emission would be preferred. Jatropha curcas (JCO) is one of the important and alternative feedstock for biodiesel production. In the present work, JCO with higher (5.5%) free fatty acid (FFA) was used for transesterification using K–Mg composite catalyst. It found that 1:2 ratio of K/Mg was the best combination for efficient conversion of JCO to corresponding methyl esters. Physico‐chemical characterization of this novel catalyst exhibited improved surface area with adequate porosity as well as basicity helping its exceptional catalytic activity. Under optimum condition, 99.5% conversion was achieved using 10:1 ratio of methanol to JCO for 3 h at 70°C using 6 wt% of catalyst. This catalyst was truly heterogeneous in nature and almost retained its activity for subsequent re‐using for next five batches. The developed catalyst is highly efficient and greener in terms of lesser leaching behaviour and retaining the basic catalytic sites in the course of transesterification. The fuel properties of the biodiesel are fully complied with ASTM protocols; thus, research findings are significant to fulfil the demand of biodiesel in present scenario.
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