Integration of a neural ore grade estimation tool in a 3D resource modeling package

Author

Kapageridis, Ioannis K. ; Denby, Bryan ; Hunter, Graham

Author_Institution

Sch. of Chem. Environ. & Min. Eng., Nottingham Univ., UK

Volume

6

fYear

1999

fDate

36342

Firstpage

3908

Abstract

Ore grade estimation is a key aspect in the evaluation of a mineral deposit. In this paper an alternative approach to currently applied methods of ore grade estimation is presented. This alternative approach involves a modular neural network system integrated in a state of the art 3D resource modelling package. The need for a new method of ore grade estimation comes from the difficulties in applying conventional methods such as geostatistics. These methods require a lot of assumptions, knowledge, skills and time to be effectively applied while their results are not always easy to justify. The aim of the proposed system, called GEMNet II is to provide fast and reliable ore grade estimation, with minimum assumptions and minimum requirements for modelling skills. GEMNet II has been tested on a number of real deposits. The results obtained so far have shown that it can provide with a very fast and robust alternative to the existing time-consuming methodologies for ore grade estimation

Keywords

engineering computing; mineral processing industry; neural nets; 3D resource modeling package; GEMNet II; geostatistics; mineral deposit evaluation; modular neural network system; neural ore grade estimation tool; Artificial neural networks; Chemical engineering; Electronic mail; Function approximation; Neural networks; Ores; Packaging; Radial basis function networks; Scholarships; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1999. IJCNN '99. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-5529-6

Type

conf

DOI

10.1109/IJCNN.1999.830780

Filename

830780