VNII Galurgy Stock Co., St. Petersburg, Russia:

V. I. Timofeev, Head of the Laboratory, timofeev@galurgy.spb.ru
K. S.Kirish, Junior Researcher
T. I. Rutkovskaya, Senior Researcher, Candidate of Engineering Sciences

The article highlights some of the ways to manage the process of dissolution of sylvinite ores at halurgical (hot leaching) factories. The object of the dissolution stage is to obtain the solution with high KCl saturation while maximizing KCl leaching from sylvinite ores. This requires that the amount of KCl, introduced into the process from sylvinite ores was equivalent by capacitance to the dissolving liquor for KCl subject to the temperature regime at the stage of dissolution. This problem is solved by controlling the ratio of ore: dissolving liquor. The article summarizes the current methods to manage the process of dissolution of sylvinite ores: the first - the traditional on the density of average liquor produced in the second solvent, and entering the first solvent, the second is used mainly at present, based on the stabilization of the potassium chloride consumption, entering the process depending on the composition of the ore. There are shortly stated the ways to control the use of mathematical models: a stochastic received by statistical processing of the actual parameters of the process of dissolution by the method of experimental design and analysis, built on probabilistic mathematical equations that describe the diagram of solubility of KCl-NaCl-H₂O in the presence of MgCl₂ and mass balance equations. Five most important technological parameters influencing the dissolution process in terms of process control were chosen when building a stochastic model. Analytical model most closely reflects the dissolution process. As it is based on the equation, obtained by approximation of the experimental data on the solubility in water-salt system, forming in the production conditions. In the work are reported some process management methods of sylvinite ore dissolution at hot leaching factories. The task of the dissolution stage is the solution obtainment with high saturation degree on KCl with simultaneous maximum hot leaching KCl from sylvinite ore. It is necessary, that the amount of KCl entered into process with the sylvinite ore be equivalent to the dissolutioning leach (shchelok) capacity for KCl with consideration for the temperature regime of the dissolution stage. This task is solved by management of the ore : liquor (shchelok) ratio. There are stated and analysed the following optimal management methods of the dissolution process: the management system of potassium chloride mass fraction in the “average” leach (shchelok), the management system by mass stabilization of potassium chloride entering in the process with ore and also management systems based on the mathematical models usage: the stochastic model, obtained by statistical processing of actual parameters of technological dissolution process by the experiment planning method and analytical model, set up on probabilistic mathematical equations, which are described dissolution diagram KCl-NaCl-H₂O in the presence of MgCl₂.

1. Permyakov R. S., Egorov S. V., Kolpikov G. G., Zlobinskiy A. G. Tekhnologiya i avtomatizatsiya proizvodstva kaliynykh udobreniy (Potassium fertilizers production technology and automation). Leningrad : Khimiya, 1973. pp. 33–43.
2. Golovkov B. Yu., Kolpikov G. G., Otsup R. R., Nuraev R. Kh., Matiyko L. N. Avtomatizatsiya tekhnologicheskikh protsessov kaliynykh fabrik. Seriya "Kaliynaya promyshlennost" (Automation of production processes at potassium plants. "potassium industry" series). Moscow: NIITEKhIM, 1988. 48 p.
3. Safrygin Yu. S., Paskina A. V., Buksha Yu. V., Timofeev V. I. Sposob upravleniya pro tsessom rastvoreniya silvinitovykh rud (Process management method for sylvinite ore dissolution). Patent RF, No. 2398620. Applied: March 04, 2009. Published: September 10, 2010. Bulletin No. 25.
4. Safrygin Yu. S., Buksha Yu. V., Timofeev V. I., Paskina A. V., Rutkovskaya T. I., Kirish K. S. Sposob upravleniya protsessom rastvoreniya silvinitovykh rud (Process management method for sylvinite ore dissolution). Patent RF, No. 254940320. Applied: September 03, 2013. Published: April 27, 2015. Bulletin No. 12.
5. Data science and big data analytics: discovering, аnalizing, visualizing and presenting data. Indianapolis : John Wiley & Sons, Inc., 2015. 432 p.
6. Douglas C. Montgomery. Design and analysis of experiments. Eighth edition. Indianapolis : John Wiley & Sons, Inc., 2013. 724 p.
7. Sautin S. N., Punin A. E., Stoyanov S. Primenenie elektronno-vychislitelnoy mashiny dlya planirovaniya eksperimenta: Uchebnoe posobie (Computer use for planning an experiment: tutorial). Leningrad : Lensovet Leningrad Technological Institute, 1988. 80 p.
8. Adler Yu. P., Markova E. V., Granovskiy Yu. V. Planirovanie eksperimenta pri poiske optimalnykh usloviy (Experiment planning when searching the optimum conditions). Moscow : Nauka, 1976. 279 p.
9. Santner T. J., Williams B. J., Notz W. I. The design and analysis computer experiments. 2003.
10. Mason R. L., Gunst R. F., Hess J. L. Statistical design and analysis of experiments: with applications to engineering and science. 2003.
11. Charles R. Hicks. Osnovnye printsipy planirovaniya eksperimenta (Fundamental Concepts in the Design of Experiments). Moscow: Kniga po Trebovaniyu, 2013. 203 p.
12. Timofeev V. I., Buksha Yu. V., Paskina A. V. Upravlenie protsessom rastvoreniya i kristallizatsii na galurgicheskikh fabrikakh (Control of dissolution and crystallization process at thermal-dissolving-and-crystallizing plants). Gornyi zhurnal = Mining journal. 2007. No. 8. pp. 96–98.
13. Spravochnik po rastvorimosti solevykh sistem (Reference book on saline system solubility). Leningrad : Khimiya, 1975. Vol. I–II. pp. 95–119.
14. Svoystva galurgicheskikh rastvorov i soley. Khloridy natriya, kaliya i magniya : Spravochnik (Properties of solutions and salts being used in thermal-dissolving-andcrystallizing processes. Sodium, potassium and magnesium chlorides : Reference book). Edited by Yu. V. Buksha and N. E. Shestakov. Saint Petersburg : Khimiya, 1997. 512 p.
15. Tyurin Yu. N., Makarov A. A. Analiz dannykh na kompyutere (Computer data analysis). Edited by V. E. Figurnov. 3^rd edition, revised and enlarged. Moscow: INFRA-M, 2003. 544 p.
16. Montgomery D. C., Peek E. A., Vining G. G. Introduction to Linear Regression Analysis. 5^th edition. Indianapolis. John Wiley & Sons, Inc., 2012. 672 p.
17. Kaganov V. I. Kompyuternye vychisleniya v sredakh Excel i MathCad (Computer calculations in Excel and MathCad). Moscow: Goryachaya liniya-Telekom, 2003. 328 p.
18. Kholodnov V. A., Dyakonov V. P., Ivanova E. I., Kiryanova L. S. Matematicheskoe modelirovanie i optimizatsiya khimiko-tekhnologicheskikh protsessov: Prakticheskoe rukovodstvo (Mathematical modeling and optimization of chemical and technological processes: Practical guidance). Saint Petersburg: Professional, 2003. 480 p.
19. Kholodnov V. А., Sirenek V. А., Chepikova V. N., Borovinskaya E. S., Krylov V. М. Reshenie zadach bezuslovnoy optimizatsii s ispolzovaniem sistemy kompyuternoy matematiki MathCad (Solving the problems of unconditional optimization using the computer mathematics system MathCad). Saint Petersburg :Saint Petersburg State University, 2010. 48 p.
20. Anders Rasmuson, Bengt Andersson, Louise Ollson, Ronnie Andersson. Mathematical Modeling in Chemical Engineering. University Printing House, Cambridge CB2 8BS, United Kingdom. Published in the United States of America by Cambridge University Press, New York, 2014. 183 p.
21. Morozov V. V., Ulitenko K. Ya., Ganbaatar Z., Delgerbat L. Razrabotka i primenenie avtomatizirovannykh sistem upravleniya protsessami obogashcheniya poleznykh iskopaemykh (Development and use of automated control systems for mineral concentration processes). Moscow: “Ore and Metals” Publishing House, 2013. 512 p.