International Journal of Engineering and Advanced Technology Studies (IJEATS)

EA Journals

mineral economics

Prediction of Gold associated Mineral worth: An application of mathematically driven artificial neural network technique (Published)

The elemental composition of other associate minerals existing with gold is a significant asset that defines the amount of additional economic contribution that can be obtained from the gold tailings. The elemental composition is a needed factor in increasing the economic value of gold run-off and getting a clear estimation for the quantity of value-added elements in each tonne of gold sand scooped during the separation process. In this study, the artificial neural network (ANN) modeling technique was used to develop an economic worth prediction model for 10 gold-associated minerals. The developed models have a 1:7:10 architecture and were trained using the ANN Bayesian regularization training algorithm. According to the root mean square error values, the results revealed that the predicted values of the associated minerals are closer to the measured values. Also, the developed model prediction performance was found to be appropriate for the estimation of gold-associated mineral economic benefits based on the high coefficient of determination and variance account. The model performance evaluation results show that the developed ANN models are suitable for economic estimation of gold-associated mineral worth.

Keywords: Artificial Intelligence, Gold, Mining, Nigeria, machine learning algorithms, mineral economics

Statistical Investigation of the Relationship between Gold and Associate Minerals: A case Study of Kagara Area of Niger State Nigeria Soil (Published)

In the Kagara region of Niger State, north-central Nigeria, an investigation was conducted into the gold occurrence and availability of other economic-benefit associate minerals. 39 samples from the study area were subjected to fire assay analysis and multi-element analysis to determine the gold and other mineral recovery in the case study formation. Statistical examination Methods of Pearson correlation and R-mode varimax rotated factor analysis were used to interpret the results. The analysis revealed that the recovered gold (Au) had a grade between 0.01g per tonne and 0.19g per tonne. Li was also identified as the associate mineral with the lowest quantity, with a range of 1-20% and a mean value of 8.49%, whereas Manganes displayed some skewness with a minimum value of 156 and a maximum value of 3080 ppm. According to the Pearson correlation analysis, Lithium and Magnesium have a moderately positive correlation, indicating that they come from the same source. In addition, Mo and Ni have a strong positive correlation whereas Au and Na have a weak positive correlation. The factor analysis performed on the gold and associated mineral occurrences revealed that the deposit had been significantly altered by both environmental and mineralization factors in the study area’s soil. Importantly, the study demonstrates that an associated mineral with gold has substantial economic value. Considering the capital and operating costs required for the exploration and exploitation of gold-bearing soil and rock, it has been determined that the refining of other associate minerals to improve the cost-benefit ratio is highly advantageous.

Keywords: Factor analysis, Nigeria, gold occurrence, mineral economics, soil geochemical data, statistical analysis

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