简介概要

Prediction of operational parameters effect on coal flotation using artificial neural network

来源期刊:International Journal of Minerals Metallurgy and Materials2008年第5期

论文作者:E. Jorjani Sh. Mesroghli S. Chehreh Chelgani

文章页码:528 - 533

摘    要:Artificial neural network procedures were used to predict the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density,pH,rotation rate,coal particle size,dosage of col-lector,frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

详情信息展示

Prediction of operational parameters effect on coal flotation using artificial neural network

E. Jorjani,Sh. Mesroghli,S. Chehreh Chelgani

摘 要:Artificial neural network procedures were used to predict the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density,pH,rotation rate,coal particle size,dosage of col-lector,frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.

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