A hybrid geometallurgical study using coupled Historical Data (HD) and Deep Learning (DL) techniques on a copper ore mine

• Autorzy: Gholami Alireza , Asgari Kaveh , Khoshdast Hamid , Hassanzadeh Ahmad

Identyfikatory

DOI

10.37190/ppmp/147841

• Języki publikacji: EN

Abstrakty

EN

This research work introduces a novel hybrid geometallurgical approach to develop a deep and comprehensive relationship between geological and mining characteristics with metallurgical parameters in a mineral processing plant. This technique involves statistically screening mineralogical and operational parameters using the Historical Data (HD) method. Further, it creates an intelligent bridge between effective parameters and metallurgical responses by the Deep Learning (DL) simulation method. In the HD method, the time and cost of common approaches in geometallurgical studies were minimized through the use of available archived data. Then, the generated DL-based predictive model was enabled to accurately forecast the process behavior in the mineral processing units. The efficiency of the proposed method for a copper ore sample was practically evaluated. For this purpose, six representative samples from different active mining zone were collected and used for flotation tests organized using a randomizing code. The experimental results were then statistically analyzed using HD method to assess the significance of mineralogical and operational parameters, including the proportions of effective minerals, particle size, collector and frother concentration, solid content and pH. Based on the HD analysis, the metallurgical responses including the copper grade and recovery, copper kinetics constant and iron grade in concentrate were modeled with an accuracy of about 90%. Next, the geometallurgical model of the process was developed using the long short-term memory neural network (LSTM) algorithm. The results showed that the studied metallurgical responses could be predicted with more than 95% accuracy. The results of this study showed that the hybrid geometallurgy approach can be used as a promising tool to achieve a reliable relationship between the mining and mineral processing sectors, and sustainable and predictable production.

Słowa kluczowe

EN

hybrid geometallurgy; historical data; deep learning; copper ore; flotation

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Physicochemical Problems of Mineral Processing
• Rocznik: 2022
• Tom: Vol. 58, iss. 3
• Strony: art. no. 147841
• Opis fizyczny: Bibliogr. 56 poz., rys., wykr.

Twórcy

autor

Gholami Alireza

• Department of Mineral Processing, Faculty of Engineering, Tarbiat Modares University, 14115111, Tehran, Iran

autor

Asgari Kaveh

• FOCUS Minerals Engineering Research Center, Research and Development Division, Zarand, Iran

autor

Khoshdast Hamid

• Department of Mining Engineering, Higher Education Complex of Zarand, 7761156391, Zarand, Iran

autor

Hassanzadeh Ahmad

• Department of Geoscience and Petroleum, Faculty of Engineering, Norwegian University of Science and Technology, 7031 Trondheim, Norway
• Maelgwyn Mineral Services Ltd, Ty Maelgwyn, 1A Gower Road, Cathays, Cardiff CF24 4PA, United Kingdom

• https://orcid.org/0000-0002-6410-4736

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