Assessment of Self-Heating Susceptibility of Indian Coal Seams - A Neural Network Approach
IR@CIMFR: CSIR-Central Institute of Mining and Fuel Research, Dhanbad
View Archive Info| Field | Value | |
| Title |
Assessment of Self-Heating Susceptibility of Indian Coal Seams - A Neural Network Approach
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| Creator |
Ray, S.K.
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| Subject |
Mine Ventilation
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| Description |
The paper addresses an electro-chemical method called wet oxidation potential technique for determining the susceptibility of coal to spontaneous combustion. Altogether 78 coal samples collected from thirteen different mining companies spreading over most of the Indian Coalfields have been used for this experimental investigation and 936 experiments have been carried out by varying different experimental conditions to standardize this method for wider application. Thus for a particular sample 12 experiments of wet oxidation potential method were carried out. The results of wet oxidation potential (WOP) method have been correlated with the intrinsic properties of coal by carrying out proximate, ultimate and petrographic analyses of the coal samples. Correlation studies have been carried out with Design Expert 7.0.0 software. Further, artificial neural network (ANN) analysis was performed to ensure best combination of experimental conditions to be used for obtaining optimum results in this method.
All the above mentioned analysis clearly spelt out that the experimental conditions should be 0.2 N KMnO4 solution with 1 N KOH at 45°C to achieve optimum results for finding out the susceptibility of coal to spontaneous combustion. The results have been validated with Crossing Point Temperature (CPT) data which is widely used in Indian mining scenario.
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| Publisher |
PAS
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| Date |
2014-12
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| Type |
Article
PeerReviewed |
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| Format |
application/pdf
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| Identifier |
http://cimfr.csircentral.net/1698/1/%5BArchives%20of%20Mining%20Sciences%5D%20Assessment.pdf
Ray, S.K. (2014) Assessment of Self-Heating Susceptibility of Indian Coal Seams - A Neural Network Approach. Archives of Mining Science , 59 (4). pp. 1061-1076. ISSN 0860-7001 |
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| Relation |
https://doi.org/10.2478/amsc-2014-0073
http://cimfr.csircentral.net/1698/ |
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