ISSN print edition: 0366-6352
ISSN electronic edition: 1336-9075
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Machine learning-guided prediction and optimization of precipitation efficiency in the Bayer process

Abbas Bakhtom, Saeed Ghasemzade Bariki, Salman Movahedirad, and Mohammad Amin Sobati

School of Chemical Engineering, Iran University of Science and Technology, Tehran, Iran



Received: 26 September 2022  Accepted: 21 December 2022


Machine learning approaches were used to predict and optimize the precipitation efficiency in the Bayer process. One thousand five hundred and sixty real operating data points of the precipitation efficiency from Iran Alumina Company were used for the model’s development. Radial basis function (RBF) and support vector machine (SVM) networks were applied to develop a black-box model of the process. The input parameters of the models were the concentrations of sodium oxide (Na2Oc) and aluminum oxide (Al2O3), tank temperature, ambient temperature, residence time, and solid content. To create an optimal model, a trial-and-error strategy based on analyzing all potential configurations was used. The network’s prediction performance is further demonstrated through model generalization inside the training data domain. The outcomes of both RBF and SVM networks demonstrate a good agreement between the industrial data and the model predicted values when considering statistical measures such as correlation coefficients of more than 0.99999, mean square errors, the absolute average deviation, and the absolute average relative deviation of less than 0.01%. The outcome of the models was used to optimize the operating parameters in such a way as to maximize precipitation efficiency with a minimum concentration of sodium oxide. The results show that the average precipitation efficiency of 42% was increased to 47% at optimized conditions.

Keywords: Machine learning; Precipitation efficiency; Bayer process; Radial basis function; Support vector machine

Full paper is available at

DOI: 10.1007/s11696-022-02642-x


Chemical Papers 77 (5) 2509–2524 (2023)

Tuesday, July 23, 2024

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