<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The process line of concentrating iron ore materials is considered as a sequence of connected concentration units, some of which partially return ore materials to the previous unit. The output product of the final concentration unit in the process line is the end product of the whole line. Characteristics of ore, such as distribution of ore particles by size and distribution of iron content by size classes, are considered. Processing of iron ore materials by process units (a cycle, a scheme) is characterised by a separation characteristic – namely the function of extracting elementary fractions depending on physical properties of ore particles. The results of fraction analysis of ore samples in different points of the process line provide an experimental definition of separation characteristics and numerical values of the Rosin–Rammler equation factors. To identify dependencies that cannot be analytically described, the hybrid approach accompanied by the Takagi–Sugeno fuzzy models, in accompaniment with triangular membership functions determining fuzzy sets in preconditions, are used. To identify fuzzy sets in rule preconditions, triangular membership functions are used. Introduction of a-priori data on iron ore concentration as constraints for model parameters is a promising trend of further research, since it enables increased accuracy of identification despite limited availability of experimental data.</p>
</abstract>

The process line of concentrating iron ore materials is considered as a sequence of connected concentration units, some of which partially return ore materials to the previous unit. The output product of the final concentration unit in the process line is the end product of the whole line. Characteristics of ore, such as distribution of ore particles by size and distribution of iron content by size classes, are considered. Processing of iron ore materials by process units (a cycle, a scheme) is characterised by a separation characteristic – namely the function of extracting elementary fractions depending on physical properties of ore particles. The results of fraction analysis of ore samples in different points of the process line provide an experimental definition of separation characteristics and numerical values of the Rosin–Rammler equation factors. To identify dependencies that cannot be analytically described, the hybrid approach accompanied by the Takagi–Sugeno fuzzy models, in accompaniment with triangular membership functions determining fuzzy sets in preconditions, are used. To identify fuzzy sets in rule preconditions, triangular membership functions are used. Introduction of a-priori data on iron ore concentration as constraints for model parameters is a promising trend of further research, since it enables increased accuracy of identification despite limited availability of experimental data.

Modelling of Iron Ore Processing in Technological Units Based on the Hybrid Approach

Department of Automation, Computer Science and Technologies

Acta Mechanica et Automatica

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The process line of concentrating iron ore materials is considered as a sequence of connected concentration units, some of which partially return ore materials...

Modelling of Iron Ore Processing in Technological Units Based on the Hybrid Approach

Publié en ligne: 04 févr. 2022

Pages: 82 - 90

Reçu: 07 nov. 2020

Accepté: 22 déc. 2021

DOI: https://doi.org/10.2478/ama-2022-0010

Mots clés
iron ore, Rosin–Rammler, fuzzy model, separation characteristics

© 2022 Vladimir Morkun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Modelling of Iron Ore Processing in Technological Units Based on the Hybrid Approach

Publié en ligne: 04 févr. 2022

Pages: 82 - 90

Reçu: 07 nov. 2020

Accepté: 22 déc. 2021

DOI: https://doi.org/10.2478/ama-2022-0010

Mots clésiron ore, Rosin–Rammler, fuzzy model, separation characteristics

© 2022 Vladimir Morkun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
iron ore, Rosin–Rammler, fuzzy model, separation characteristics