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Information Journal Paper

Title

Comparing the Capability of Various Models for Predicting the Bayer Process Parameters

Pages

  71-86

Abstract

 In the present study, prediction of Alumina recovery efficiency (A. R. E), the amount of produced Red mud (A. P. R), Red mud settling rate (R. S. R) and Bound-soda losses (B. S. L) in Bayer process Red mud has been carried out for the first time in the field. These predictions are based on lime to Bauxite ratio and chemical analyses of Bauxite and lime as the Bayer process feed materials. Radial basis function (RBF) and multilayer perceptron (MLP) as artificial neural network and the multiple linear regression (MLR) method have been used to predict these parameters in Iran Alumina Company. According to the obtained results, it is evident that the RBF method has outperformed the other two methods in the prediction of A. R. E, A. P. R and B. S. L. However, the multilayer perceptron (MLP) method can produce better and more precise results in the prediction of R. S. R. This research also exposes more effective variables on A. R. E, A. P. R, R. S. R, and B. S. L.

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    APA: Copy

    Mahmoudian, Mostafa, GHAEMI, AHAD, Hashemabadi, Hassan, & SHAHHOSSEINI, SHAHROKH. (2018). Comparing the Capability of Various Models for Predicting the Bayer Process Parameters. JOURNAL OF ADVANCED MATERIALS AND PROCESSING (JOURNAL OF MATERIALS SCIENCE), 6(1), 71-86. SID. https://sid.ir/paper/712346/en

    Vancouver: Copy

    Mahmoudian Mostafa, GHAEMI AHAD, Hashemabadi Hassan, SHAHHOSSEINI SHAHROKH. Comparing the Capability of Various Models for Predicting the Bayer Process Parameters. JOURNAL OF ADVANCED MATERIALS AND PROCESSING (JOURNAL OF MATERIALS SCIENCE)[Internet]. 2018;6(1):71-86. Available from: https://sid.ir/paper/712346/en

    IEEE: Copy

    Mostafa Mahmoudian, AHAD GHAEMI, Hassan Hashemabadi, and SHAHROKH SHAHHOSSEINI, “Comparing the Capability of Various Models for Predicting the Bayer Process Parameters,” JOURNAL OF ADVANCED MATERIALS AND PROCESSING (JOURNAL OF MATERIALS SCIENCE), vol. 6, no. 1, pp. 71–86, 2018, [Online]. Available: https://sid.ir/paper/712346/en

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