Abstract
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.
Highlights
In the context of the intense development of the world, the demand for machinery, paint, paper, and plastic is virtually unending with titanium minerals labelled as one of the primary materials [1,2,3]
Motivated from the significance of this type of mineral on the local economy [35], this study aimed at evaluating the feasibility of predicting it using different artificial intelligence (AI) techniques
It is known as a useful tool for classification and regression
Summary
In the context of the intense development of the world, the demand for machinery, paint, paper, and plastic is virtually unending with titanium minerals labelled as one of the primary materials [1,2,3]. This commodity can be exploited from the placer and hard rocks deposits [4]. The major mineral components are ilmenite, rutile, anatase, leucoxene, zircon, and monazite [7] Of those minerals, ilmenite and rutile are normally considered as the main types containing titanium because of their high proportion in the total heavy minerals [8,9]. Each known as a useful tool for classification and regression issues.issues
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