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Open AccessArticle

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

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Department of Electrical Engineering, Jiyuan Vocational and Technical College, Jiyuan 459000, China
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Department of Surface Mining, Mining Faculty, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
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Center for Mining, Electro-Mechanical research, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
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Faculty of Geosciences and Geoengineering, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
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Center for Excellence in Analysis and Experiment, Hanoi University of Mining and Geology, 18 Vien st., Duc Thang ward, Bac Tu Liem dist., Hanoi 100000, Vietnam
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Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
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Division of Computational Mathematics and Engineering, Institute for Computational Science, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam
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Civil and Environmental Engineering, Nagaoka University of Technology, 1603-1, Kami-Tomioka, Nagaoka, Niigata 940-2188, Japan
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Center for Spatial Information Science, the University of Tokyo, 5-1-5, Kashiwa 277-8568, Japan
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Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(2), 635; https://doi.org/10.3390/app10020635
Received: 17 November 2019 / Revised: 24 December 2019 / Accepted: 30 December 2019 / Published: 16 January 2020
In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research. View Full-Text
Keywords: titanium placer; beach placer; ilmenite content; artificial intelligence; applied soft computing titanium placer; beach placer; ilmenite content; artificial intelligence; applied soft computing
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LV, Y.; Le, Q.-T.; Bui, H.-B.; Bui, X.-N.; Nguyen, H.; Nguyen-Thoi, T.; Dou, J.; Song, X. A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer. Appl. Sci. 2020, 10, 635.

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