Classification of coal seams with respect to their spontaneous heating susceptibility—a neural network approach

The paper presents the application of adaptive resonance theory of artificial neural networks (ANN) for classification of coal seams with respect to their proneness to spontaneous heating. In order to apply this technique, 31 coal samples have been collected from different Indian coalfields covering both fiery and non-fiery coal seams of varying ranks spreading over 8 different mining companies. The intrinsic properties of these samples have been determined by carrying out proximate, ultimate and petrographic analyses. The susceptibility indices of these samples have been studied by five different methods, viz. crossing point temperature, differential thermal analysis, critical air blast analysis, wet oxidation potential difference analysis and differential scanning calorimetric studies. Exhaustive correlation studies between susceptibility indices and the intrinsic properties have been carried out for identifying the appropriate spontaneous heating susceptibility indices and intrinsic properties to be used for classification of coal seams. The identified parameters are used as inputs and adaptive resonance theory of ANN has been applied to classify the coal seams into four different categories. This classification system will help the planners and practising mining engineers to take ameliorative measures in advance to prevent the occurrence of fire in mines.     

Optimum tracking of ship routes in 3g-WAM simulated rough weather using IRS-P4 (MSMR) analysed wind fields

Rough weather ship routing is studied using model hindcast wave climate. With the launch of IRS-P4 (OCEANSAT-I), it became possible to carry out routine wave forecasting over the Indian Ocean. The MSMR channel of the satellite gives scalar wind, which is analysed at National center for Medium Range Weather Forecasting (NCMRWF), India for converting to vector winds. The same is used as input to third generation wave model for the rough weather month of July 2000. Simulations are carried out using Cycle-4 of third generation spectral wave model WAM for regional grid system. This simulated wave climate formed the basis for computing effective ship velocity in the irregular seaway. This study gives a quantitative estimation of change in ship velocity in the open Indian Ocean for a Liberty type ship. The optimal route is charted using Dijkstra’s algorithm for minimal time path between Calcutta and Sumatra. The optimum track information has broad scope for obtaining a safer route, least time route by avoiding delay in schedule with minimum fuel consumption.     

Fluid evolution in quartz vein-hosted tungsten mineralization in Chhendapathar, Bankura District, West Bengal: Evidence from fluid inclusion study

Tungsten mineralization in Chhendapathar area is hosted by quartz veins that traverse mostly the metasediments in and around Jikhu Nala. Fluid inclusion microthermometric experiments reveal the presence of four distinct types of inclusions. These are: aqueous biphase, monophase carbonic, aqueouscarbonic and halite-bearing polyphase inclusions. Salinity-temperature variation points towards the presence of two fluids of contrasting salinities and both independently followed simple cooling paths without any indication of fluid mixing. The P-T of mineralization was calculated from the intersection of coexisting and coeval aqueous biphase, carbonic and halite-bearing inclusions. The deduced values range from 1.63kb/361°C to 2.30kb/385°C. However, the initial temperature must have been much higher as indicated from the high dissolution temperature (> 450°C) of halite. Transportation of tungsten in the high saline fluid was facilitated by cation-tungstate ion pairing, i.e., with the help of Na₂WO₄ and/or NaHWO₄ complexes. A rapid fall in solubility in such fluid with falling temperature (in the range of 300–400°C), and by occasional fluid-rock interaction triggered precipitation of wolframite.     

Development of an artificial neural network based multi-model ensemble to estimate the northeast monsoon rainfall over south peninsular India: an application of extreme learning machine

The south peninsular part of India gets maximum amount of rainfall during the northeast monsoon (NEM) season [October to November (OND)] which is the primary source of water for the agricultural activities in this region. A nonlinear method viz., Extreme learning machine (ELM) has been employed on general circulation model (GCM) products to make the multi-model ensemble (MME) based estimation of NEM rainfall (NEMR). The ELM is basically is an improved learning algorithm for the single feed-forward neural network (SLFN) architecture. The 27 year (1982–2008) lead-1 (using initial conditions of September for forecasting the mean rainfall of OND) hindcast runs (1982–2008) from seven GCM has been used to make MME. The improvement of the proposed method with respect to other regular MME (simple arithmetic mean of GCMs (EM) and singular value decomposition based multiple linear regressions based MME) has been assessed through several skill metrics like Spread distribution, multiplicative bias, prediction errors, the yield of prediction, Pearson’s and Kendal’s correlation coefficient and Wilmort’s index of agreement. The efficiency of ELM estimated rainfall is established by all the stated skill scores. The performance of ELM in extreme NEMR years, out of which 4 years are characterized by deficit rainfall and 5 years are identified as excess, is also examined. It is found that the ELM could expeditiously capture these extremes reasonably well as compared to the other MME approaches.     

Gold Prospectivity Mapping in the Sonakhan Greenstone Belt,Central India: A Knowledge-Driven Guide for Target Delineation in a Region of Low Exploration Maturity

Natural Resources Research - The Sonakhan greenstone belt in Central India is under-explored with respect to gold in spite of its similarity to auriferous greenstone belts in general, which...     

Micro and Macro Cosmological Model in Barber's Second Self-Creation Theory

A spatially homogeneous anisotropic Bianchi type-I cosmological model in the Barber's second self-creation theory is constructed when the gravitational field is generated by a mixture of micro and macro matter fields represented by meson field and perfect fluid respectively. The physical and geometrical features of the micro and macro cosmological model are discussed. This revised version was published online in July 2006 with corrections to the Cover Date.