Control regularities of the useful mineral extraction from ore feed stream with ball grinding. Correlation analysis
/ 0
- Details
- Category: Information technologies, systems analysis and administration
- Last Updated on 06 September 2017
- Published on 06 September 2017
- Hits: 3841
Authors:
I.K.Mladetsky, Dr. Sc. (Tech.), Prof., State Higher Education Institution “National Mining University”, Dnipro, Ukraine, e-mail: kuvaievig@gmail. com
Ya.H.Kuvaiev, Cand. Sc. (Tech.), Assoc. Prof., State Higher Education Institution “National Mining University”, Dnipro, Ukraine, e-mail: kuvaievig@gmail. com
N.S.Pryadko, Dr. Sc. (Tech.), Senior Research Fellow, Institute of Technical Mechanics of National Academy of Sciences of Ukraine, State Space Agency of Ukraine, Dnipro, Ukraine
Abstract:
Purpose. To define the indicative events characterizing a process state on “useful mineral content in ore –useful mineral content in a concentrate” control channel at an ore concentrating plant with ball grinding using correlation analysis method with the purpose of an operator exclusion out of a control circuit.
Methodology. Correlation analysis of the control object parameters.
Findings. According to the mutually correlation function a range of indicative events is determined which characterize a process state on “useful mineral content in ore –useful mineral content in a concentrate” control channel.
Originality. Comparison of numerical values of recession time and the equivalent delay of autocorrelation and correlation functions of the general iron content and a time constant of a dressing process line obtained in the normal operation of ore concentrating factory with ball grinding has been made out for the first time.
Practical value. The indicative events obtained by the type and parameters of the correlation functions between the content of a useful mineral in the feedstock and concentrate can be used for automatic system creation for situation-dependent process control of ferrous and non-ferrous ore dressing as both at separate ball grinding sections, and at separate plants which include these sections.
References.
1. Decisions for a mining and processing complex according to the Saturn company of a data international. [online] Available at: http://www.saturn-data.com/img/forall/file/Mining_rus_mod.pdf [Accessed 11 February 2017].
2. Automated process control systems of concentrating factories: according to the PROM-TECH company. [online] Available at: http://www.promtex.ru/custom-pages.html?layout=edit&id=185/ [Accessed 11 February 2017].
3. IRON ORE BENEFICIATION SOLUTIONS: according to the Outotec company. [online] Available at: http://www.outotec.com/en/Products-services/Ferrous-metals-and-ferroalloys-processing/Iron/Iron-ore-beneficiation/ [Accessed 5 February 2017].
4. Porkuyan, O.V., Davidenko, A.M. and Katz, M. D., 2010. Identification and suboptimization of technological process of iron ore dressing as object of automatic control. Visnik of East-Ukrainian University by Volodymyr Dahl, 6(148), Part 2, pp. 71‒75.
5. Khotskina, V.B., 2013. Process control automation of iron ore dressing technology complex with Petry net model use. Visnik of Krivorizky National University, 34, pp. 15‒18.
6. Khorolsky, V.P., Khotskina, V.B. and Khorolska, T.V., 2013. Intellectual control system of iron ore dressing technology complex. Mining electromechanic and automatic equipment, 91, pp. 47‒53.
7. Konokh, I.S. and Nayda, V.V., 2014. The analysis of relevance of development of information technologies for optimization of operation of the first stage of concentrating factory of Ferrexpo Poltava Mining. Visnik of Kremenchutsky National University named after Mikhail Ostrogradskyi, 5(88), pp. 68‒76.
8. Kuvaiev, Ya.H., 2006. Automatic expert energy saving management system closed loop of wet spherical grinding. Science and innovations, 3, pp. 48‒53.
9. Ivanov, A.B., Kuvaiev, V.N. and Kuvaiev, Ya.H., 2010. Perspective directions of process automation of ore crushing and grinding. Mining Magazine, 11, pp. 74‒77.
10. Kupin, A.I., 2008. Statistic parameter estimation of artificial neurostructures for control of nonlinear dynamic objects. Naukovy Visnik Chernivetsky University, 423, pp. 77‒82.
 04_2017_Mladetsky
|
|
 2017-09-05  1.84 MB  789 |
|
Archive