Exploration
Hamed Norouzi; Aliakbar Daya
Abstract
Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting ...
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Estimating mineral reserves in exploration or extraction projects is a critical and challenging process. It must be conducted precisely, regardless of the mining scale and mineral type. With the growing significance of mineral resources in economic and industrial development, the importance of adopting advanced technologies in mineral assessment has also surged. Modern spatial grade modeling techniques can play a pivotal role in decision-making processes. This study aims to compare the performance and capabilities of two popular machine learning methods, including Gaussian Process Regression (GPR) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) in spatial grade modeling of copper at the Chehel Kureh Copper deposit. The dataset comprises 42 drill holes with an average copper grade of 0.18%. Each core sample data point includes seven variables: three spatial coordinates (X, Y, and Depth), lead grade, zinc grade, lithology and copper grade, which serves as the target variable. The Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP-ANN) neural network were employed for copper grade estimation. To make a better assessment, the hyperparameters of both models were optimized using the Bayesian Optimization algorithm. The results showed that the Gaussian Process Regression outperformed MLP-ANN, achieving an RMSE of 0.04 and a coefficient of determination (R²) of 0.89 compared to an RMSE of 0.05 and a coefficient of determination (R²) of for MLP-ANN, suggest the superiority of the Gaussian Process Regression method in estimating copper grade spatial variability.
H. Bejari; A. A. Daya; A. Roudini
Abstract
Based on existence of the chromite deposits in the Sistan and Baluchestan province in Iran, and also various applications of chromite in different industries, it is expected that the establishment of chromite processing plant is required in the erelong. The geographical location of a processing plant ...
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Based on existence of the chromite deposits in the Sistan and Baluchestan province in Iran, and also various applications of chromite in different industries, it is expected that the establishment of chromite processing plant is required in the erelong. The geographical location of a processing plant can have a strong influence on the success of an industrial venture. The processing plant site selection is a multi-criteria decision problem. The conventional methods used for a plant location selection are inadequate for dealing with the imprecise or vague nature of a linguistic assessment. To overcome this difficulty, the fuzzy multi-criteria decision-making methods are proposed. This paper presents an application of the analytic hierarchy process (AHP) method based on the fuzzy sets (Fuzzy AHP) used to select an appropriate site for a chromite processing plant in the Sistan and Baluchestan province. For this purpose, based on the concentration of chromite deposits in different regions of the province, four feasible alternatives including the Zahedan, Khash, Iranshahr, and Nikshahr cities are selected for a chromite processing plant. The quantitative and qualitative criteria such as availability of raw materials, availability of labors, education, climatic conditions, environmental impacts, infra-structural facilities and security, and local community considerations are used to compare the feasible alternatives. Finally, the alternatives are ranked, and a convenient location is recommended for the construction of the chromite processing plant. The results obtained show that the city of Zahedan is the best alternative.
