Designing gold extraction processes: Performance study of a case-based reasoning system

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2017-08-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
42-53
Series
Minerals Engineering, Volume 109
Abstract
This paper presents a method for externalising and formalising knowledge involving the selection of hydrometallurgical process flowsheets for gold extraction from ores. A case-based reasoning (CBR) system was built using an open source software myCBR 3.0. The aim of the systems is to recommend flowsheet alternatives for processing a potential gold ore deposit. Nine attributes: Ore type, Gold ore grade, Gold distribution, Gold grain size, Sulfide present, Arsenic sulfide, Copper sulfide, Iron sulfide and Clay present were modelled and several literature sources of actual gold mines and processes were used for acquiring cases for the system. After preliminary testing, functional evaluation of the built CBR system was carried out by using five real mining projects as test cases. Additionally, human experts in the field of gold hydrometallurgy were interviewed to demonstrate the benefits of the CBR system as it holds no human biases towards any processing techniques. It was found that the suggestions of the CBR system provided useful information and direction for further process design and performed well compared to the interviewed human experts, thus confirming that the system is of practical relevance to the process engineer designing an industrial gold processing plant. The current model was found to be a functioning basis for further development through additional attributes, adjusted attribute weighting and increased number of cases.
Description
Keywords
Decision support system, Flowsheet recommendation, Knowledge modelling, Process design
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Citation
Rintala, L, Leikola, M, Sauer, C, Aromaa, J, Roth-Berghofer, T, Forsén, O & Lundström, M 2017, ' Designing gold extraction processes : Performance study of a case-based reasoning system ', Minerals Engineering, vol. 109, pp. 42-53 . https://doi.org/10.1016/j.mineng.2017.02.013