Synthesis and investigation of the control system for the process of сarbon article molding

Authors

DOI:

https://doi.org/10.15587/1729-4061.2020.200573

Keywords:

manufacture of carbon products, molding process, hydraulic press, control system, optimality criterion, MPC

Abstract

Among the most energy-intensive industries is the production of carbon articles, therefore, the improvement of its efficiency is a relevant scientific and technical task. One of the ways to resolve the set task is to create a modern production management system.

This paper considers the creation of a control system for one of the essential technological processes in carbon articles production – the process of their formation. Underlying the control system is the optimality criterion based on the specific cost of products taking into consideration their quality indicators. The control method used is Model Predictive Control (MPC). The results of studying the dependence of an optimality criterion on the setting parameters of an MPC-controller have made it possible to determine the optimum values for the prediction and control horizons, which could ensure the minimization of the products’ specific cost. The structure of the proposed control system, developed in the Simulink programming environment, makes it possible to investigate a given control system through computer simulations.

The efficiency of the proposed system to control the process of carbon product molding was examined by comparing the quality of control by a given system and by the system that uses the classic PID control law. To this end, a three-circuit control system based on the PID-controllers was synthesized in the Simulink programming environment. Each controller was set, using a Powell method, for a minimum value of the integrated criterion. The results of the comparative study have demonstrated that at each operation cycle the optimality criterion value in the control system employing an MPC-controller was 8.8 % less than that in the system with PID-controllers at the same indicators of product quality. That testifies to the improvement in the technical and economic indicators of the formation process. This fact is of particular importance when taking into consideration the circularity of the technological process of carbon product formation

Author Biographies

Oleksii Zhuchenko, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Peremohy ave., 37, Kyiv, Ukraine, 03056

Doctor of Technical Sciences, Associate Professor

Department of Automation of Chemical Productions

Mykola Khibeba, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" Peremohy ave., 37, Kyiv, Ukraine, 03056

Postgraduate Student

Department of Automation of Chemical Productions

References

  1. Kuznetsov, D. M., Fokin, V. P. (2001). Protsess grafitatsii uglerodnyh materialov. Sovremennye metody issledovaniya. Novocherkassk: YURGTU, 132.
  2. Panov, E. N., Matvienko, A. A., Karvatskiy, A. Ya. et. al. (2011). Sovremennoe sostoyanie problemy polucheniya grafitirovannogo napolnitelya elektrodnyh izdeliy v elektrokal'tsinatorah. Visnyk NTUU “KPI”. Khimichna inzheneriia, ekolohiya ta resursozberezhennia, 1 (7), 49–55.
  3. Pirogov, V. I., Seleznev, A. N. (2006). Primenenie antratsita kak napolnitelya uglerodnoy produktsii. Rossiyskiy Himicheskiy Zhurnal, L (1), 12–16.
  4. Karvatskii, A. Y., Lazarev, T. V. (2014). Evaluation of the Discrete Element Method for Predicting the Behavior of Granular Media Using Petroleum Coke as an Example. Chemical and Petroleum Engineering, 50 (3-4), 186–192. doi: https://doi.org/10.1007/s10556-014-9877-y
  5. Karvatskiy, A. Ya., Leleka, S. V., Kutuzov, S. V., Dudnikov, P. I., Chizh, A. N. (2008). Chislennoe modelirovanie trehmernyh nestatsionarnyh temperaturnyh poley v pechah grafitatsii i alyuminievyh elektrolizerah. Visnyk NTUU “KPI”. Khimichna inzheneriia, ekolohiya ta resursozberezhennia, 1 (1), 46–51.
  6. Panov, Ye. M., Karvatskyi, A. Ya., Kutuzov, S. V. et. al. (2011). Modeliuvannia hrafituvannia naftovoho koksu v shakhtniy elektropechi neperervnoi diyi. Visnyk NTUU “KPI”. Khimichna inzheneriia, ekolohiya ta resursozberezhennia, 1 (7), 48–52.
  7. Karvatskiy, A. YA., Leleka, S. V., Pulinets, I. V., Lazarev, T. V. (2011). Development of burning regulations take into account the dynamics of gas emission of burning blanks. Eastern-European Journal of Enterprise Technologies, 6 (5 (54)), 42–45. Available at: http://journals.uran.ua/eejet/article/view/2281/2085
  8. Karvatskii, A., Lazariev, T., Korzhyk, M. (2016). Numerical investigation of large size carbon products formation process using the extrusion method. Bulletin of the National Technical University «KhPI» Series: New solutions in modern technologies, 25, 99–106. doi: https://doi.org/10.20998/2413-4295.2016.25.15
  9. Andrienko, P. D., Yarymbash, D. S. (2007). Povyshenie energoeffektivnosti pri avtomatizirovannom upravlenii induktorami pressa. Zbirnyk naukovykh prats Dniprodzerzhynskoho derzhavnoho tekhnichnoho universytetu (tekhnichni nauky), 212–213.
  10. Martynova, D. V., Popov, V. P., Vanshin, V. V. (2017). Application of mathematical modeling and control system of the extrusion process with the purpose of energy and resource saving and ensuring production of high-quality extruded food and foodstuffs. Intelekt. Innovatsii. Investitsii, 6, 78–81.
  11. Paoletti, S. (2012). Pat. No. US 9.434,099 B2. Extrusion machine with improved temperature control system. No. 14/360,278; declareted: 19.11.2012; published: 22.05.2014. Available at: https://patentimages.storage.googleapis.com/fc/c2/73/fdd749c5aae594/US9434099.pdf
  12. Markl, D., Wahl, P. R., Menezes, J. C., Koller, D. M., Kavsek, B., Francois, K. et. al. (2013). Supervisory Control System for Monitoring a Pharmaceutical Hot Melt Extrusion Process. AAPS PharmSciTech, 14 (3), 1034–1044. doi: https://doi.org/10.1208/s12249-013-9992-7
  13. Meintanis, I., Halikias, G., Giovenco, R., Yiotis, A., Chrysagis, K. (2017). Identification and Model Predictive Control Design of a Polymer Extrusion Process. 27th European Symposium on Computer Aided Process Engineering, 1609–1614. doi: https://doi.org/10.1016/b978-0-444-63965-3.50270-1
  14. Ravi, S., Balakrishnan, P. A. (2010). Modelling and control of an anfis temperature controller for plastic extrusion process. 2010 International conference on communication control and computing technologies. doi: https://doi.org/10.1109/icccct.2010.5670572
  15. Ravi, S., Sudha, M., Balakrishnan, P. A. (2011). Design of Intelligent Self-Tuning GA ANFIS Temperature Controller for Plastic Extrusion System. Modelling and Simulation in Engineering, 2011, 1–8. doi: https://doi.org/10.1155/2011/101437
  16. Zhuchenko, O., Khibeba, M. (2017). Formation Process in the Production of Carbon Products Control Task Setting. Konstruiuvannia, vyrobnytstvo ta ekspluatatsiya silskohospodarskykh mashyn, 47 (2), 81–88.
  17. Geyer, T. (2016). Model predictive control of high power converters and industrial drives. John Wiley & Sons. doi: https://doi.org/10.1002/9781119010883
  18. Nikolaou, M. (2001). Model predictive controllers: A critical synthesis of theory and industrial needs. Advances in Chemical Engineering, 131–204. doi: https://doi.org/10.1016/s0065-2377(01)26003-7
  19. García, M. R., Vilas, C., Santos, L. O., Alonso, A. A. (2012). A robust multi-model predictive controller for distributed parameter systems. Journal of Process Control, 22 (1), 60–71. doi: https://doi.org/10.1016/j.jprocont.2011.10.008
  20. Hedengren, J. D., Shishavan, R. A., Powell, K. M., Edgar, T. F. (2014). Nonlinear modeling, estimation and predictive control in APMonitor. Computers & Chemical Engineering, 70, 133–148. doi: https://doi.org/10.1016/j.compchemeng.2014.04.013
  21. Mayne, D. Q., Rawlings, J. B., Rao, C. V., Scokaert, P. O. M. (2000). Constrained model predictive control: Stability and optimality. Automatica, 36 (6), 789–814. doi: https://doi.org/10.1016/s0005-1098(99)00214-9
  22. Camacho, E. F., Ramirez, D. R., Limon, D., Muñoz de la Peña, D., Alamo, T. (2010). Model predictive control techniques for hybrid systems. Annual Reviews in Control, 34 (1), 21–31. doi: https://doi.org/10.1016/j.arcontrol.2010.02.002
  23. Müller, M. A., Allgöwer, F. (2012). Improving performance in model predictive control: Switching cost functionals under average dwell-time. Automatica, 48 (2), 402–409. doi: https://doi.org/10.1016/j.automatica.2011.11.005
  24. Press, W. H., Teukolsky, S. A., Vetterling, W. T., Flannery, B. P. (2007). Numerical Recipes: The Art of Scientific Computing. New York: Cambridge University Press, 1256.
  25. Zhuchenko, O., Khibeba, M. (2018). Model of carbon products forming in preparation and pressing modes. Vcheni zapysky TNU imeni V.I. Vernadskoho. Seriya: tekhnichni nauky, 29 (6), 149–156. Available at: http://www.tech.vernadskyjournals.in.ua/journals/2018/6_2018/part_1/28.pdf

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Published

2020-04-30

How to Cite

Zhuchenko, O., & Khibeba, M. (2020). Synthesis and investigation of the control system for the process of сarbon article molding. Eastern-European Journal of Enterprise Technologies, 2(2 (104), 45–51. https://doi.org/10.15587/1729-4061.2020.200573