| dc.contributor.author |
Nagpal, Gaurav |
|
| dc.contributor.author |
Nagpal, Ankita |
|
| dc.contributor.author |
Jasti, Naga Vamsi Krishna |
|
| dc.date.accessioned |
2023-11-11T04:38:34Z |
|
| dc.date.available |
2023-11-11T04:38:34Z |
|
| dc.date.issued |
2022 |
|
| dc.identifier.uri |
https://dl.acm.org/doi/abs/10.1145/3590837.3590954 |
|
| dc.identifier.uri |
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/13024 |
|
| dc.description.abstract |
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. |
en_US |
| dc.language.iso |
en |
en_US |
| dc.publisher |
ACM Digital Library |
en_US |
| dc.subject |
Mechanical Engineering |
en_US |
| dc.subject |
Neural networks |
en_US |
| dc.subject |
Mining Industry |
en_US |
| dc.title |
Ore Grade Estimation in Mining Industry from petro-physical data using neural networks |
en_US |
| dc.type |
Article |
en_US |