The study uses multi-layer neural network perceptron model and neural network regression models for predicting the grade of the ore on the basis of Petro-physical data that was collected by doing borehole geophysical survey capturing twenty-one properties of the ore.
Authors:
Gaurav Nagpal, BITS Pilani, India.
Singh Shrikant Ramesh, Business Excellence, Hindustan Zinc Limited, India and BITS Pilani, India.
Naga Vamsi Krishna Jasti, Department of Core Engineering, BITS Pilani, India.
Ankita Nagpal, Department of Management, BITS Pilani, India.
Gunjan Mohan Sharma, Professor, Jindal Global Law School, O.P. Jindal Global University, Sonipat, Haryana, India.
Summary
The grade of the ore in mining industry plays a very important role. From the petro-physical data, the grade of the ore can be predicted with reasonable accuracy. However, the existing literature is silent on the techniques of data analytics that can be used for ore-grade estimation with the help of data.
The study uses multi-layer neural network perceptron model and neural network regression models for predicting the grade on the basis of Petro-physical data that was collected by doing borehole geophysical survey capturing twenty-one properties of the ore. The research study is able to estimate the grade of the ore with reasonable accuracy using the data.
Published in: ACM International Conference Proceeding Series23 December 2022 Article number 754th International Conference on Information Management and Machine Intelligence, ICIMMI 2022Jaipur23 December 2022through 24 December 2022
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