Document Type : Original Research Paper

Authors

• Abdelrahem Khalefa Embaby ¹
• Ashraf Ismael ²
• Faissal Ali ²
• Hussin Farag ³
• Bahaa Mousa ⁴
• Sayed Gomaa ⁵
• Mohamed Elwageeh ⁶

¹ Associate professor, Al-Azhar University, Faculty of Engineering Mining and Petroleum Engineering Department

² Associate Professor at the Department of Mining and Petroleum Engineering, Al-Azhar University, Cairo, Egypt

³ Lecturer at the Department of Mining and Petroleum Engineering, Al-Azhar University, Cairo, Egypt

⁴ Lecturer at the Department of Mining and Petroleum Engineering, Al-Azhar University, Cairo, Egypt.

⁵ Faculty of Engineering, Al-Azhar University, Cairo, Egypt

⁶ Professor at the Department of Mining Engineering, Cairo University, Cairo, Egypt.

Abstract

The mineral resource estimation process necessitates a precise prediction of the grade based on limited drilling data. Grade is crucial factor in the selection of various mining projects for investment and development. When stationary requirements are not met, geo-statistical approaches for reserve estimation are challenging to apply. Artificial Neural Networks (ANNs) are a better alternative to geo-statistical techniques since they take less processing time to create and apply. For forecasting the iron ore grade at El-Gezera region in El- Baharya Oasis, Western Desert of Egypt, a novel Artificial Neural Network (ANN) model, geo-statistical methods (Variograms and Ordinary kriging), and Triangulation Irregular Network (TIN) were employed in this study. The geo-statistical models and TIN technique revealed a distinct distribution of iron ore elements in the studied area. Initially, the tan sigmoid and logistic sigmoid functions at various numbers of neurons were compared to choose the best ANN model of one and two hidden layers using the Levenberg-Marquardt pure-linear output function. The presented ANN model estimates the iron ore as a function of the grades of Cl%, SiO₂%, and MnO% with a correlation factor of 0.94. The proposed ANN model can be applied to any other dataset within the range with acceptable accuracy.

Keywords

• Artificial neural network
• Iron ore grade
• El-Gezera Area
• Geostatistics, GIS

Main Subjects

• GIS & Remote Sensing