Modeling and optimization of complex ore pretreatment by disintegration in autogenous mills

The study aims to optimize multi-component polymineral ore mixture ratio after coarse grinding by the criterion of autogenous mill productivity gain. The influence of grain-size composition, as well as physical and mechanical properties of multi-component polymineral ore mixture on autogenous mill productivity are determined using the crushing laws are determined, the effect of the material constitution of the mixture components on the physical and mechanical properties is revealed, and the elements of ore mixture algorithm are developed. The optimization model is elaborated for the polymineral ore mixture ratio in autogenous milling. The optimization model can increase the number of milling bodies and decrease the difficult size grade, which further on can ensure the autogenous mill productivity gain. The model was used as a framework of the productivity control algorithm. The outlines are proposed for the autogenous mill productivity control algorithm by means of iron ore ratio adjustment in the ore mixture as a mathematical model. Since automated control system of ore pretreatment circuit at the studied factory is incompletely equipped with required sensors to provide sufficient information for the mixture making optimization, the mathematical model was simplified. This model will be further used as a framework for the autogenous mill productivity control algorithm.

Keywords: autogenous mill, multi-component ore mixture, complex ore, ore mixture making optimization, productivity, density, coarseness.
For citation:

Melekhina K.A.,Ananyev P. P., PlotnikovaA. V., TimofeevA. S., Shestak S.A. Modeling and optimization of complex ore pretreatment by disintegration in autogenous mills. MIAB. Mining Inf. Anal. Bull. 2020;(10):95-105. [In Russ]. DOI: 10.25018/0236-1493-2020-10-0-95-105.

Acknowledgements:
Issue number: 10
Year: 2020
Page number: 95-105
ISBN: 0236-1493
UDK: 004.942, 622.7, 622.795
DOI: 10.25018/0236-1493-2020-10-0-95-105
Article receipt date: 10.03.2020
Date of review receipt: 21.06.2020
Date of the editorial board′s decision on the article′s publishing: 20.09.2020
About authors:

K.A. Melekhina1, Research Assistant, e-mail: k.melekhina@mail.ru,
P.P. Ananyev1, Cand. Sci. (Eng.), General Director, e-mail: cigt@mail.ru,
A.V. Plotnikova1, Deputy General Director, e-mail: cigt@mail.ru,
A.S. Timofeev, Cand. Sci. (Eng.), Senior Researcher, Institute of Problems of Comprehensive Exploitation of Mineral Resources of Russian Academy of Sciences, 111020, Moscow, Russia, e-mail: timofeev_ac@mail.ru,
S.A. Shestak, Chief Specialist of Technical Department of Technical Management, Kola MMC JSC, Zapolyarny, Russia, e-mail: schestaksa@kolagmk.ru,
1 Association of Subjects of Innovative Activity in Mining Industry «Innovative Mining Technologies», Moscow, Russia.

For contacts:

K.A. Melekhina, e-mail: k.melekhina@mail.ru.

Bibliography:

1. Gupta A., Yan D. Autogenous and semi-autogenous mills. Mineral Processing Design and Operations. Ch. 9. 2-nd edition, 2016. Pp. 263—285.

2. Morrell S. Modelling the influence on power draw of the slurry phase in Autogenous (AG), Semi-autogenous (SAG) and ball mills. Minerals Engeneering. 2016. Vol. 89. Pp. 148—156.

3. Yu. P., Xie. W., Liu L. X., Powell M. S. Analytical solution for the dynamic model of trumbling mills. Powder Technology. 2018. Vol. 337. Pp. 111—118.

4. Steyn C. W., Sandrock C. Benefits of optimisation and model predictive control on a fully autogenous mill with variable speed. Minerals Engineering. 2013. Vol. 53. Pp. 113—123.

5. Gzogyan T. N., Gzogyan S. R. Droblenie, izmel'chenieipodgotovka syr'ya kobogashcheniyu: uchebnoe posobie [Crushing, milling and preparation of rough stock for processing: Teaching aid: Educational aid], Belgorod, 2017, 217 p.

6. Taranenko M. E. Automated control of ore milling circuit in autogenous wet mills. MIAB. Mining Inf. Anal. Bull. 2010, no 10, pp. 369—372. [In Russ].

7. Taranov V.A. Ore strength assessment as a factor of milling efficiency enhancement. MIAB. Mining Inf. Anal. Bull. 2015, no 4, pp. 119—123. [In Russ].

8. Khomunov E.A. Modeling ore disintegration processes. Izvestiya vysshikh uchebnykh zavedeniy. Gornyy zhurnal. 2016, no 3, pp. 104—114. [In Russ].

9. Khopunov E.A. Causes of low energy efficiency of mineral disintegration processes. Sovremennaya tekhnika i tekhnologii. 2014, no 10. [In Russ].

10. Shishkin A.A., Yastrebov K. L. Analysis of autogenous mill performance parameters. Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta. 2012, no 11 (70), pp. 100—110. [In Russ].

11. Shishkin A.A., Yastrebov K. L. Features of disintegration technology for crushed rocks. Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta. 2012, no 11 (70), pp. 176—180. [In Russ].

12. Shishkin A.A. Influence of friction force on kinematics of autogenous mill feed elements. Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta. 2012, no 9 (68), pp. 211—213. [In Russ].

Подписка на рассылку

Раз в месяц Вы будете получать информацию о новом номере журнала, новых книгах издательства, а также о конференциях, форумах и других профессиональных мероприятиях.