دانلود مقاله ISI انگلیسی شماره 138550
کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
138550 2018 11 صفحه PDF سفارش دهید 7668 کلمه
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عنوان انگلیسی
Application of artificial neural networks and response surface methodology approaches for the prediction of oil agglomeration process
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Fuel, Volume 220, 15 May 2018, Pages 826-836

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چکیده انگلیسی

Oil agglomeration can be a promising technique to recover fines and ultra-fines coal particles from the discarded tailing generated from coal preparation plants. In the present study, an artificial neural network (ANN) and response surface methodology (RSM) was used to predict the behavior of coal oil agglomeration in terms of % ash rejection (% AR) and % combustible matter recovery (% CMR). A three layered Feed Forward Neural Network was developed by varying process variables such as solid concentration (SC), oil dosage (OD) and agglomeration time (AT). Waste soybean oil was used as bridging liquid. An approach of Multilayer Forward Back Propagation Neural Network has been used in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function. The network is well trained (learning) with Levenberg-Marquardt (LM) algorithm. For further improvement in the generalization of the developed ANN model Bayesian regularization technique has been adopted. Sensitivity analysis was performed using Garson’s algorithm, Pearson correlation coefficient and Connection weight approach. The % CMR values predicted from ANN have been in good agreement with the obtained experimental values (R2 0.9965) and shows better correlation between predicted and observed values than RSM (R2 0.9892). Also, for % AR the correlation (R2 0.9965) obtained using ANN found to be higher than RSM (R2 0.9956).

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