ANN modeling of tincal ore dehydration

Mustafa Engin Kocadağistan

doi:10.1515/mt-2024-0141

Article

ANN modeling of tincal ore dehydration

Mustafa Engin Kocadağistan
Dr. Mustafa Engin Kocadağistan, born in 1965, works at the University of Ataturk, Faculty of Engineering, Department of Metallurgy and Materials Engineering, Erzurum, Türkiye. He graduated in Mining Engineering from the Technical University of İstanbul, Türkiye, in 1989. He received his MSc and PhD degrees from Ataturk University, Erzurum, Türkiye, in 2015. He studied open-pit chrome mining, bioleaching and hydrometallurgy techniques, solid-state welding methods, boron ores, and nature restoration in open-pit mining.

Published/Copyright: August 14, 2024

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From the journal Materials Testing Volume 66 Issue 10

Abstract

Tincal ore is a preferred material in many industrial applications, especially without water. It is important to dehydrate boron ores so that they can be used in materials engineering. For this purpose, some heat treatments must be carried out. Heat treatments are associated with additional costs. It is possible to model heat treatments using artificial intelligence methods, determine optimal process conditions and achieve the desired results with much less processing effort. In this study, a dehydration process was first carried out to dehydrate tincal ore and ANN (artificial neural networks) modeling of this process was investigated using the parameters of temperature, time and amount of ore. The possibility of achieving the desired H₂O, B₂O₃ and Na₂O concentration values in the dewatering process in the shortest time and by the shortest route was investigated using the ANN model. In the modeling, a single model was designed for the changes in concentrations and this model was trained separately for each. The result of the modeling was that the R ² values for all three models were close to each other and were approximately 0.98. It was thus shown that the ANN method can be successfully modeled for dewatering processes.

Keywords: anhydrous boron; ANN; dehydration; modeling; tincal ore

Corresponding author: Mustafa Engin Kocadağistan, Ataturk University, Faculty of Engineering, Department of Metallurgical and Materials Engineering, 25240 Erzurum, Türkiye, E-mail: mengink@atauni.edu.tr

About the author

Mustafa Engin Kocadağistan

Dr. Mustafa Engin Kocadağistan, born in 1965, works at the University of Ataturk, Faculty of Engineering, Department of Metallurgy and Materials Engineering, Erzurum, Türkiye. He graduated in Mining Engineering from the Technical University of İstanbul, Türkiye, in 1989. He received his MSc and PhD degrees from Ataturk University, Erzurum, Türkiye, in 2015. He studied open-pit chrome mining, bioleaching and hydrometallurgy techniques, solid-state welding methods, boron ores, and nature restoration in open-pit mining.

Acknowledgments

Associate Professor Dr. for making some corrections and comments on ANN modelling. I would like to thank Emin Argun Oral.

Research ethics: It is not a study that requires ethical approval. Not applicable.
Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: The author states no conflict of interest. (The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.)
Research funding: None declared.
Data availability: The raw data can be obtained on request from the corresponding author.

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Published Online: 2024-08-14

Published in Print: 2024-10-28

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https://doi.org/10.1515/mt-2024-0141

Keywords for this article

anhydrous boron; ANN; dehydration; modeling; tincal ore