A Chart for Judging Optimal Sample Spacing for Ore Grade Estimation: Part II

AbstractIn order to study the influence of distance weight on ore-grade estimation, the inverse distance weighted (IDW) is used to estimate the Ni grade and MgO grade of serpentinite ore based on a three-dimensional ore body model and related block models. Manhattan distance, Euclidean distance, Chebyshev distance, and multiple forms of the Minkowski distance are used to calculate distance weight of IDW. Results show that using the Minkowski distance for the distance weight calculation is feasible. The law of the estimated results along with the distance weight is given. The study expands the distance weight calculation method in the IDW method, and a new method for improving estimation accuracy is given. Researchers can choose different weight calculation methods according to their needs. In this study, the estimated effect is best when the power of the Minkowski distance is 3 for a 10 m × 10 m × 10 m block model. For a 20 m × 20 m × 20 m block model, the estimated effect is best when the power of the Minkowski distance is 9.

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Relevance vector machines using weighted expected squared distance for ore grade estimation with incomplete data

International Journal of Machine Learning and Cybernetics ◽

10.1007/s13042-016-0535-x ◽

2016 ◽

Vol 8 (5) ◽

pp. 1655-1666 ◽

Cited By ~ 1

Author(s):

Yukui Zhang ◽

Shiji Song ◽

Keyou You ◽

Xunan Zhang ◽

Cheng Wu

Keyword(s):

Incomplete Data ◽

Grade Estimation ◽

Relevance Vector Machines ◽

Vector Machines ◽

Ore Grade

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

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339) ◽

10.1109/ijcnn.1999.830780 ◽

2003 ◽

Cited By ~ 4

Author(s):

I.K. Kapageridis ◽

B. Denby ◽

G. Hunter

Keyword(s):

Resource Modeling ◽

Grade Estimation ◽

Ore Grade

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Comparative Evaluation of Neural Network Learning Algorithms for Ore Grade Estimation

Mathematical Geology ◽

10.1007/s11004-005-9010-z ◽

2006 ◽

Vol 38 (2) ◽

pp. 175-197 ◽

Cited By ~ 11

Author(s):

B. Samanta ◽

S. Bandopadhyay ◽

R. Ganguli

Keyword(s):

Neural Network ◽

Comparative Evaluation ◽

Learning Algorithms ◽

Neural Network Learning ◽

Network Learning ◽

Grade Estimation ◽

Ore Grade

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Comparison of machine learning methods for copper ore grade estimation

Computational Geosciences ◽

10.1007/s10596-018-9758-0 ◽

2018 ◽

Vol 22 (5) ◽

pp. 1371-1388 ◽

Cited By ~ 3

Author(s):

Bahram Jafrasteh ◽

Nader Fathianpour ◽

Alberto Suárez

Keyword(s):

Machine Learning ◽

Learning Methods ◽

Copper Ore ◽

Grade Estimation ◽

Machine Learning Methods ◽

Ore Grade

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Advanced System for Control Milling and Flotation Processes Based on an Estimation of Ore Quality Grade

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.651.981 ◽

2013 ◽

Vol 651 ◽

pp. 981-985

Author(s):

Valery Morozov ◽

Zorigt Ganbaatar ◽

Lodoy Delgerbat ◽

Ludmila Bokányi ◽

Valeriy Stoliarov

Keyword(s):

Mineral Processing ◽

Process Models ◽

Valuable Component ◽

Flotation Process ◽

Intellectual Content ◽

Grade Estimation ◽

Ore Processing ◽

Quality Grade ◽

Advanced System ◽

Ore Grade

Ore processing plays a very important linking role between the mining and the metallurgical steps. The recent R&D achievements in this field resulted in the increase of the intellectual content of technological processes of mineral processing, as well as its automatic control. The main reason of mineral processing fluctuations is a mixing of ores from various sections of a deposit. Instability and non-optimized parameters of milling and flotation take 3% to 6% of losses of valuable component. Milling and flotation process are characterized by considerable fluctuations of all input, output and intermediate parameters. In these conditions, applying various mathematic models the process controlling by action on input parameters with further precise reaching the required values of output parameters of the process is not always possible. The application of the multilevel process models allows elaborating and using methods of the ore grade estimation and evaluation of the technological process situation by adaptive control.

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