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

Panigrahi, Durga Charan; Sahu, H. B.

doi:10.1023/b:gege.0000047040.70764.90

Cited by 37 publications

(14 citation statements)

References 7 publications

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“…The decreasing ash content affects the oxidation potential by causing a relatively high spontaneous combustion liability for both coals and coal-shales analysed. This is in-line with the studies reported on coal by Beamish and Arisoy (2008), Humphreys et al (1981), Panigrahi and Sahu (2004); Smith et al (1988).…”

Section: Effects Of Intrinsic Properties On Spontaneous Combustion Ofsupporting

confidence: 89%

“…The occurrence of various intrinsic and extrinsic parameters influencing the start and development of spontaneous combustion is the reason for the difficulties in understanding the mechanism of spontaneous combustion. Most of these parameters have been reported by many researchers (Kaymakci and Didari 2002;Banerjee 1985;Kim 1977;Beamish and Blazak 2005;Falcon 2004;Panigrahi and Sahu 2004;Panigrahi and Saxena 2001;Ren et al 1999) to predict the self-heating characteristics of coal.…”

Section: Introductionmentioning

confidence: 99%

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Modelling spontaneous combustion liability of carbonaceous materials

Onifade

Genc

2018

Int J Coal Sci Technol

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This paper presents predictive models to determine spontaneous combustion liability of carbonaceous materials (coals and coal-shales) using statistical analysis. The intrinsic properties and spontaneous combustion liability index were determined by testing 14 coals and 14 coal-shales from Witbank coalfields, South Africa. The relationship between these intrinsic properties (obtained from proximate, ultimate and petrographic analysis) and spontaneous combustion liability indices (the Wits-Ehac Index and Wits-CT Index) were established. The influence of the intrinsic properties of coal-shales in relation to coal properties affecting spontaneous combustion has been established using a statistical method. The linear regression analysis indicates better linear relationships between some of the selected intrinsic properties and spontaneous combustion liability index and thus, identifies the major intrinsic factors affecting their liability toward spontaneous combustion. It was found that a definite positive or negative correlation coefficient exists between the intrinsic factors and spontaneous combustion liability. A set of models to predict the spontaneous combustion liability was derived. The best significant correlation along with the most appropriate model as indicated by R-squared values, the coefficient of correlations and standard error was used to predict the incident of spontaneous combustion.

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Section: Effects Of Intrinsic Properties On Spontaneous Combustion Ofsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Modelling spontaneous combustion liability of carbonaceous materials

Onifade

Genc

2018

Int J Coal Sci Technol

View full text Add to dashboard Cite

show abstract

“…It has been demonstrated that the methodology outperforms existing clustering techniques as far as the present data set is concerned. A similar classification has been obtained when adaptive resonance theory was applied to a slightly large data set (Panigrahi and Sahu, 2004). However, computational efforts in adaptive resonance theory are quite high.…”

Section: Resultssupporting

confidence: 69%

“…All these parameters considered were on air dried basis. It was observed from the correlation study that the constituents of proximate analysis show better correlation with CPT than that of the ultimate and petrographic analyses (Panigrahi and Sahu, 2004). In addition, normality test, a pre-requisite for application of PCA, conducted on the sample data using MINITAB R14 software reveals that the above four parameters follow a near-normal distribution pattern.…”

Section: Selection Of Variablesmentioning

confidence: 96%

An empirical approach for classification of coal seams with respect to the spontaneous heating susceptibility of Indian coals

Sahu

Mahapatra

Panigrahi

2009

International Journal of Coal Geology

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“…Basma and Kallas (2004) have employed BP neural networks to model soil collapse. Panigrahi and Sahu (2004) have applied adaptive resonance theory of ANNs to the classification of coal seams with respect to their proneness to spontaneous heating.…”

Section: Introductionmentioning

confidence: 99%

The displacement computation and construction pre-control of a foundation pit in Shanghai utilizing FEM and intelligent methods

2006

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Controlling the displacements of foundation pit walls and supporting structures is one of the most effective measures for ensuring safety of foundation pit construction. Because of this, the accurate displacement computation of foundation pit walls and supporting structures is paid great attention by geotechnical scientists and engineers. In this paper, the three-dimensional displacement field of Deping Station foundation pit, which is part of Shanghai Track Traffic Line 6 project, is computed by means of ANSYS software. Artificial neural networks (ANNs) are a broad category of computer algorithms which have the ability to learn some target values (desired output) from a set of chosen input data that has been introduced to the network. The ANNs have very excellent ability in simulating nonlinear and complicated problems. In the current paper, the authors try to combine FEM method with ANNs so as to improve the computational accuracy. Basing on the computed results, fuzzy control theory is applied to construction pre-control. The application effect is satisfactory. The research result of current paper is very helpful for geotechnical construction and the development of geotechnical theory.

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

Cited by 37 publications

References 7 publications

Modelling spontaneous combustion liability of carbonaceous materials

Modelling spontaneous combustion liability of carbonaceous materials

An empirical approach for classification of coal seams with respect to the spontaneous heating susceptibility of Indian coals

The displacement computation and construction pre-control of a foundation pit in Shanghai utilizing FEM and intelligent methods

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