Experimental and industrial method of synthesis of optimal control of the temperature region of cupola melting

Keywords: cupola melting, temperature control, Pontryagin maximum principle, level controller, idle charge, fuel charge, air consumption, tuyere box

Abstract

The object of research is the temperature regime of melting in a cupola. The synthesis of optimal control of such an object is associated with the presence of a problem consisting in the complexity of its mathematical description and the absence of procedures that allow one to obtain optimal control laws. These problems are due to the presence of links with a pure delay, non-additive random drift, and difficulties in controlling the process parameters, in particular, accurately determining the temperature profile along the horizons and the periphery of the working space of the cupola.

The proposed conceptual solution for the synthesis of optimal temperature control allows the use of two levels of control: the level controller solves the problem of maintaining the constant height of the idle charge, and the problem of increasing the temperature of cast iron is solved by controlling the air supply to the tuyere box.

It is shown that the problem of regulating the upper level of an idle charge can be solved by reducing the model of the regulation process to a typical form, followed by the use of the Pontryagin maximum principle.

A procedure for the synthesis of optimal air flow control is proposed, which makes it possible to obtain the temperature regime control law on the basis of experimental industrial studies preceding the synthesis process. This takes into account the time delay between the impact on the object and its reaction, which makes it possible to predict the temperature value one step acharge, equal to the time interval during which the lower surface of the fuel charge reaches the upper surface of the level of the idle charge.

A procedure for temperature profile control based on the use of D-optimal plans for selecting sensor installation points is proposed. Due to this, it becomes possible to determine the temperature profile of the cupola according to its horizons and the periphery of the working space of the cupola with maximum accuracy.

The proposed synthesis method can be used in iron foundries equipped with cupolas, as it is a tool for studying a real production process, taking into account its specific conditions. This will allow developing or improving control systems for cupola melting, implementing different control modes: manual, automated or automatic

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References

Frolova, L. (2023). Search procedure for optimal design and technological solutions to ensure dimensional and geometric accuracy of castings. Technology Audit and Production Reserves, 1 (1 (69)), 18–25. doi: https://doi.org/10.15587/2706-5448.2023.271860

Lysenkov, V., Demin, D. (2022). Reserves of resource saving in the manufacture of brake drums of cargo vehicles. ScienceRise, 3, 14–23. doi: https://doi.org/10.21303/2313-8416.2022.002551

Luis, C. J., Álvarez, L., Ugalde, M. J., Puertas, I. (2002). A technical note cupola efficiency improvement by increasing air blast temperature. Journal of Materials Processing Technology, 120 (1-3), 281–289. doi: https://doi.org/10.1016/s0924-0136(01)01053-6

O’Brien, W. A. (1948). Pat. No. 2443960 USA. Control means for cupola furnaces. United States Patent Office. Available at: https://patents.google.com/patent/US2443960

Isnugroho, K., Birawidha, D. C. (2018). The production of pig iron from crushing plant waste using hot blast cupola. Alexandria Engineering Journal, 57 (1), 427–433. doi: https://doi.org/10.1016/j.aej.2016.11.004

Larsen, E., Clark, D., Moore, K., King, P. (1997). Intelligent control of Cupola Melting. Available at: https://digital.library.unt.edu/ark:/67531/metadc675024/m2/1/high_res_d/484517.pdf

Moore, K. L., Abdelrahman, M. A., Larsen, E., Clark, D., King, P. (1998). Experimental control of a cupola furnace. Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207). doi: https://doi.org/10.1109/acc.1998.703360

Jezierski, J., Janerka, K. (2011). Selected Aspects of Metallurgical and Foundry Furnace Dust Utilization. Polish Journal of Environmental Studies, 20 (1), 101–105. Available at: http://www.pjoes.com/Selected-Aspects-of-Metallurgical-and-Foundry-r-nFurnace-Dust-Utilization,88535,0,2.html

Demin, D. A., Pelikh, V. F., Ponomarenko, O. I. (1995). Optimization of the method of adjustment of chemical composition of flake graphite iron. Liteynoe Proizvodstvo, 7-8, 42–43.

Demin, D., Koval, O., Kostyk, V. (2013). Technological audit of modifying cast iron for casting autombile and road machinery. Technology Audit and Production Reserves, 5 (1 (13)), 58–63. doi: https://doi.org/10.15587/2312-8372.2013.18398

Demin, D. A. (1998). Change in cast iron's chemical composition in inoculation with a Si-V-Mn master alloy. Litejnoe Proizvodstvo, 6, 35. Available at: https://www.scopus.com/record/display.uri?eid=2-s2.0-0032098470&origin=inward&txGid=ee3a0ac5c584374e009ec710ca4c2824

Zraychenko-Polozentsev, A., Koval, O., Domin, D. (2011). Evaluation of potential reserves of production for melting synthetic iron. Technology audit and production reserves, 1 (1), 7–15. doi: https://doi.org/10.15587/2312-8372.2011.4081

Demin, D. (2017). Strength analysis of lamellar graphite cast iron in the «carbon (C) – carbon equivalent (Ceq)» factor space in the range of C = (3,425-3,563) % and Ceq = (4,214-4,372) %. Technology Audit and Production Reserves, 1 (1 (33)), 24–32. doi: https://doi.org/10.15587/2312-8372.2017.93178

Demin, D. (2018). Investigation of structural cast iron hardness for castings of automobile industry on the basis of construction and analysis of regression equation in the factor space «carbon (C) - carbon equivalent (Ceq)». Technology Audit and Production Reserves, 3 (1 (41)), 29–36. doi: https://doi.org/10.15587/2312-8372.2018.109097

Frolova, L., Shevchenko, R., Shpyh, A., Khoroshailo, V., Antonenko, Y. (2021). Selection of optimal Al–Si combinations in cast iron for castings for engineering purposes. EUREKA: Physics and Engineering, 2, 99–107. doi: https://doi.org/10.21303/2461-4262.2021.001694

Frolova, L., Barsuk, A., Nikolaiev, D. (2022). Revealing the significance of the influence of vanadium on the mechanical properties of cast iron for castings for machine-building purpose. Technology Audit and Production Reserves, 4 (1 (66)), 6–10. doi: https://doi.org/10.15587/2706-5448.2022.263428

Nikolaiev, D. (2022). Procedure for selecting a rational technological mode for the processing of cast iron melt on the basis of graph-analytical processing of the data of serial smeltings. ScienceRise, 5, 3–13. doi: https://doi.org/10.21303/2313-8416.2022.002774

Demin, D. (2017). Synthesis of nomogram for the calculation of suboptimal chemical composition of the structural cast iron on the basis of the parametric description of the ultimate strength response surface. ScienceRise, 8, 36–45. doi: https://doi.org/10.15587/2313-8416.2017.109175

Popov, S., Frolova, L., Rebrov, O., Naumenko, Y., Postupna, О., Zubko, V., Shvets, P. (2022). Increasing the mechanical properties of structural cast iron for machine-building parts by combined Mn – Al alloying. EUREKA: Physics and Engineering, 1, 118–130. doi: https://doi.org/10.21303/2461-4262.2022.002243

Barsuk, A. (2022). Optimization of the composition of cast iron for cast parts operating under abrasive friction, according to the criterion of maximum wear resistance. ScienceRise, 5, 14–20. doi: https://doi.org/10.21303/2313-8416.2022.002775

Vasenko, Iu. A. (2012). Technology for improved wear iron. Technology Audit and Production Reserves, 1 (1 (3)), 17–21. doi: https://doi.org/10.15587/2312-8372.2012.4870

Kharchenko, S., Barsuk, A., Karimova, N., Nanka, A., Pelypenko, Y., Shevtsov, V. et al. (2021). Mathematical model of the mechanical properties of Ti-alloyed hypoeutectic cast iron for mixer blades. EUREKA: Physics and Engineering, 3, 99–110. doi: https://doi.org/10.21303/2461-4262.2021.001830

Demin, D. (2019). Development of «whole» evaluation algorithm of the control quality of «cupola – mixer» melting duplex process. Technology Audit and Production Reserves, 3 (1 (47)), 4–24. doi: https://doi.org/10.15587/2312-8372.2019.174449

Dymko, I. (2018). Choice of the optimal control strategy for the duplex-process of induction melting of constructional iron. EUREKA: Physics and Engineering, 4, 3–13. doi: https://doi.org/10.21303/2461-4262.2018.00669

Demin, D. (2020). Constructing the parametric failure function of the temperature control system of induction crucible furnaces. EUREKA: Physics and Engineering, 6, 19–32. doi: https://doi.org/10.21303/2461-4262.2020.001489

Trufanov, I. D., Chumakov, K. I., Bondarenko, A. A. (2005). Obshheteoreticheskie aspekty razrabotki stokhasticheskoi sistemy avtomatizirovannoi ekspertnoi otsenki dinamicheskogo kachestva proizvodstvennykh situatsii elektrostaleplavleniia. Eastern-European Journal of Enterprise Technologies, 6 (2 (18)), 52–58.

Trufanov, I. D., Metelskii, V. P., Chumakov, K. I., Lozinskii, O. Iu., Paranchuk, Ia. S. (2008). Energosberegaiushhee upravlenie elektrotekhnologicheskim kompleksom kak baza povysheniya energoeffektivnosti metallurgii stali. Eastern-European Journal of Enterprise Technologies, 6 (1 (36)), 22–29.

Dotsenko, Y., Dotsenko, N., Tkachyna, Y., Fedorenko, V., Tsybulskyi, Y. (2018). Operation optimization of holding furnaces in special casting shops. Technology Audit and Production Reserves, 6 (1 (44)), 18–22. doi: https://doi.org/10.15587/2312-8372.2018.150585

Domina, O. (2020). Features of finding optimal solutions in network planning. EUREKA: Physics and Engineering, 6, 82–96. doi: https://doi.org/10.21303/2461-4262.2020.001471

Domina, O. (2021). Solution of the compromise optimization problem of network graphics on the criteria of uniform personnel loading and distribution of funds. Technology Audit and Production Reserves, 1 (4 (57)), 14–21. doi: https://doi.org/10.15587/2706-5448.2021.225527

Tseng, Y.-T., Ward, J. D. (2017). Comparison of objective functions for batch crystallization using a simple process model and Pontryagin’s minimum principle. Computers & Chemical Engineering, 99, 271–279. doi: https://doi.org/10.1016/j.compchemeng.2017.01.017

Demin, D. (2012). Synthesis process control elektrodugovoy smelting iron. Eastern-European Journal of Enterprise Technologies, 2 (10 (56)), 4–9. doi: https://doi.org/10.15587/1729-4061.2012.3881

Demin, D. A. (2012). Synthesis of optimal temperature regulator of electroarc holding furnace bath. Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 6, 52–58.

Ozatay, E., Ozguner, U., Filev, D. (2017). Velocity profile optimization of on road vehicles: Pontryagin’s Maximum Principle based approach. Control Engineering Practice, 61, 244–254. doi: https://doi.org/10.1016/j.conengprac.2016.09.006

Saerens, B., Van den Bulck, E. (2013). Calculation of the minimum-fuel driving control based on Pontryagin’s maximum principle. Transportation Research Part D: Transport and Environment, 24, 89–97. doi: https://doi.org/10.1016/j.trd.2013.05.004

Bauer, S., Suchaneck, A., Leon, F. P. (2014). Thermal and energy battery management optimization in electric vehicles using Pontryagin’s maximum principle. Journal of Power Sources, 246, 808–818. doi: https://doi.org/10.1016/j.jpowsour.2013.08.020

Onori, S., Tribioli, L. (2015). Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt. Applied Energy, 147, 224–234. doi: https://doi.org/10.1016/j.apenergy.2015.01.021

Fang, H., Wei, X., Zhao, F. (2015). Structural optimization of double-tube once-through steam generator using Pontryagin’s Maximum Principle. Progress in Nuclear Energy, 78, 318–329. doi: https://doi.org/10.1016/j.pnucene.2014.09.008

Cândido, J. J., Justino, P. A. P. S. (2011). Modelling, control and Pontryagin Maximum Principle for a two-body wave energy device. Renewable Energy, 36 (5), 1545–1557. doi: https://doi.org/10.1016/j.renene.2010.11.013

Ohsawa, T. (2015). Contact geometry of the Pontryagin maximum principle. Automatica, 55, 1–5. doi: https://doi.org/10.1016/j.automatica.2015.02.015

Blot, J., Koné, M. I. (2016). Pontryagin principle for a Mayer problem governed by a delay functional differential equation. Journal of Mathematical Analysis and Applications, 444 (1), 192–209. doi: https://doi.org/10.1016/j.jmaa.2016.06.027

Pereira, F. L., Silva, G. N. (2011). A Maximum Principle for Constrained Infinite Horizon Dynamic Control Systems. IFAC Proceedings Volumes, 44 (1), 10207–10212. doi: https://doi.org/10.3182/20110828-6-it-1002.03622

Štecha, J., Rathouský, J. (2011). Stochastic maximum principle. IFAC Proceedings Volumes, 44 (1), 4714–4720. doi: https://doi.org/10.3182/20110828-6-it-1002.01501

Arutyunov, A. V., Karamzin, D. Yu., Pereira, F. (2012). Pontryagin’s maximum principle for constrained impulsive control problems. Nonlinear Analysis: Theory, Methods & Applications, 75 (3), 1045–1057. doi: https://doi.org/10.1016/j.na.2011.04.047

Khlopin, D. V. (2016). On the Hamiltonian in infinite horizon control problems. Trudy Instituta Matematiki i Mekhaniki UrO RAN, 22 (4), 295–310. doi: https://doi.org/10.21538/0134-4889-2016-22-4-295-310

Ballestra, L. V. (2016). The spatial AK model and the Pontryagin maximum principle. Journal of Mathematical Economics, 67, 87–94. doi: https://doi.org/10.1016/j.jmateco.2016.09.012

Demin, D. (2014). Mathematical description typification in the problems of synthesis of optimal controller of foundry technological parameters. Eastern-European Journal of Enterprise Technologies, 1 (4 (67)), 43. doi: https://doi.org/10.15587/1729-4061.2014.21203

Demin, D. (2013). Adaptive modeling in problems of optimal control search termovremennoy cast iron. Eastern-European Journal of Enterprise Technologies, 6 (4 (66)), 31–37. doi: https://doi.org/10.15587/1729-4061.2013.19453

Demin, D., Domin, O. (2021). Adaptive technology for constructing the kinetic equations of reduction reactions under conditions of a priori uncertainty. EUREKA: Physics and Engineering, 4, 14–29. doi: https://doi.org/10.21303/2461-4262.2021.001959

Domina, O. (2020). Selection of alternative solutions in the optimization problem of network diagrams of project implementation. Technology Audit and Production Reserves, 4 (4 (54)), 9–22. doi: https://doi.org/10.15587/2706-5448.2020.210848

Chibichik, O., Sil’chenko, K., Zemliachenko, D., Korchaka, I., Makarenko, D. (2017). Investigation of the response surface describing the mathematical model of the effects of the Al/Mg rate and temperature on the Al-Mg alloy castability. ScienceRise, 5 (2), 42–45. doi: https://doi.org/10.15587/2313-8416.2017.101923

Makarenko, D. (2017). Investigation of the response surfaces describing the mathematical model of the influence of temperature and BeO content in the composite materials on the yield and ultimate strength. Technology Audit and Production Reserves, 3 (3 (35)), 13–17. doi: https://doi.org/10.15587/2312-8372.2017.104895

Experimental and industrial method of synthesis of optimal control of the temperature region of cupola melting

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Published
2023-03-06
How to Cite
Demin, D. (2023). Experimental and industrial method of synthesis of optimal control of the temperature region of cupola melting. EUREKA: Physics and Engineering, (2), 68-82. https://doi.org/10.21303/2461-4262.2023.002804
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Engineering