2011
DOI: 10.1016/j.eswa.2011.04.084
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Evaluation of effect of coal chemical properties on coal swelling index using artificial neural networks

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Cited by 32 publications
(14 citation statements)
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“…Data-based modeling methods in the mineral processing literature are frequently applied as "soft sensors" for the prediction of variables measured infrequently (or difficult to measure), based on variables measured frequently [16][17][18]. Some applications include the prediction of indicators of the grinding phase, determining the chemical properties that have a greater impact on the milling capacity indices by configuring an artificial neural network [19,20] or a regression of support vectors [21], Martin et al [22]. On the other hand, they apply Random Forest (RF) for the prediction of the Hardgrove Grindability Index (HGI) based on a wide range of Kentucky coal samples.…”
Section: Data-based Modeling In Mineral Processingmentioning
confidence: 99%
“…Data-based modeling methods in the mineral processing literature are frequently applied as "soft sensors" for the prediction of variables measured infrequently (or difficult to measure), based on variables measured frequently [16][17][18]. Some applications include the prediction of indicators of the grinding phase, determining the chemical properties that have a greater impact on the milling capacity indices by configuring an artificial neural network [19,20] or a regression of support vectors [21], Martin et al [22]. On the other hand, they apply Random Forest (RF) for the prediction of the Hardgrove Grindability Index (HGI) based on a wide range of Kentucky coal samples.…”
Section: Data-based Modeling In Mineral Processingmentioning
confidence: 99%
“…This enables us to hierarchically recognize the most sensitive factor affecting the output (gold extraction). This is performed by incorporating values of 'relative strength of effect' (RSEs) [20,22]. After a BPNN has been trained successfully, the neural network is no longer allowed to adapt.…”
Section: Sensitivity Analysismentioning
confidence: 99%
“…The variance of O k with the change of O j for a network with n hidden layers can be calculated by the differentiation of the following equation:denote the hidden units in the n, n−1, n−2, …, 1 hidden layers, respectively[20,22]. Obviously, it is no matter what the neural network approximates, all items on the right hand side of Eq.…”
mentioning
confidence: 99%
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“…ANN consisting of a large number of interconnected processing units is a large-scale dynamic system that is adaptive and nonlinear. At present, ANN in the field of coal chemical industry is mainly used in modeling and prediction of coal blending, combustion, pyrolysis, gasification, and other systems (Carsky and Kuwornoo, 2001;Jebaraj et al, 2011;Khoshjavan et al, 2011). Back propagation (BP) neural network was developed by Rumelhart and McClelland.…”
Section: Introductionmentioning
confidence: 99%