Automated control of complex metallurgical units based on the CBR method

The paper considers the actual problem of human-machine control of complex technological units and complexes, which are characterized by a large variety of states, multidimensionality, variability, and uncertainty. Such units in the ferrous metallurgy include coke batteries, blast furnaces, steelmaking units (arc furnaces, oxygen converters), foundry and rolling complexes, rolling mills, main workshops and production facilities. The effectiveness of the model approach to the creation of control systems for such objects is shown to be insufficient for the XXI century. Alternative approaches based on the concept of best reasoning (CBR) are considered. In particular, they include full-scale model and full-scale approaches to the development of support systems and management decision-making. The well-known full-scale model procedures for applying the best reasoning (methods of typical situations and exemplary technological cycles) are presented. The authors propose a new CBR method of automated selection and implementation of control actions with the participation of process operators for process control systems. A modified CBR-cycle of control selection and the corresponding functional scheme of the software control system for a cyclic technological unit were developed. The improved CBR-cycle includes the following additional operations: correction of control decisions for selected cases; retrospective optimization of implemented control decisions; preservation of not only the best and optimized, but also erroneous decisions; updating of the case base; formation of solutions in unique or previously unreported situations. The structure of the case information model is formed on the example of software control of steel melting in the conditions of an oxygen converter shop. It includes three sections: data on the specific situation in the control system, parameters of the selected control actions, and results of steel melting. An example of the control program formation for the preparation and execution of the upcoming steel melting is based on the data of a pre-selected melting case in the conditions of a modern oxygen converter process.

1. Rotach V.Ya. Theory of Automatic Control. Moscow: MEI, 2008, 400 p. (In Russ.).

2. Maiworm M., Bäthge T., Findeisen R. Scenario-based model predictive control: Recursive feasibility and stability. IFAC – Papers Online. 2015, vol. 48, no. 8, pp. 50–56. https://doi.org/10.1016/j.ifacol.2015.08.156

3. Rawlings J.B., Mayne D.Q. Model Predictive Control: Theory and Design. Nob Hill Publishings, LLC, 2009.

4. Dozortsev V.M., Kneller D.V. APC-advanced process control. Datchiki i sistemy. 2005, no. 10, pp. 56–62. (In Russ.).

5. Kulakov S.M., Bondar’ N.F., Zimin V.V. On structuring the space of approaches to the study of automated systems at different stages of their life cycle. In: Automation Systems in Education, Science and Production. Proceedings of the 8th All-Russ. Sci. and Pract. Conf. Novokuznetsk: ITs SibSIU, 2011, pp. 26–34. (In Russ.).

6. Emel’yanov S.V., Korovin S.K., Myshlyaev L.P., etc. Theory and Practice of Forecasting in Control Systems. Kemerovo; Moscow: Rossiiskie universitety; Kuzbassvuzizdat – ASTSh, 2008, 487 p. (In Russ.).

7. Myshlyaev L.P. Automation Systems Based on a Full-Scale Model Approach. Vol. 2. Automation Systems for Industrial Purposes. Novosibirsk: Nauka, 2006, 483 p. (In Russ.).

8. Bogushevskii V.S., Grabovskii G.G., Mikhailov V.M., etc. Compu­ter model for calculating the charge and purge of converter melting. Stal’. 2006, no. 1, pp. 18–21. (In Russ.).

9. Bogushevskii V.S., Grabovskii G.G., Tserkovnitskii N.S., Ushakov  V.A. Converter melting control system. Metallurgicheskaya i gornorudnaya promyshlennost’. 2007, no. 4, pp. 232–235. (In Russ.).

10. Pan R., Yang Q., Pan S.J. Mining competent case bases for case-based reasoning. Artificial Intelligence. 2007, vol. 171, no. 16-17, pp. 1039–1068. https://doi.org/10.1016/j.artint.2007.04.018

11. Varshavskii P.R., Alekhin R.V. Method of finding solutions in intelligent decision support systems based on CBR. International Journal “Information Models and Analyses”. 2013, vol. 2, no. 4, pp.  385–392. (In Russ.).

12. Avdeenko T.V., Makarova E.S. Integration of case-based and rule-based reasoning through fuzzy inference in decision support systems. Procedia Computer Science. 2017, vol. 103, pp. 447–453. https://doi.org/10.1016/j.procs.2017.01.016

13. Wan S., Li D., Gao J., Li J. A knowledge based machine tool maintenance planning system using case-based reasoning techniques. Robotics and Computer-Integrated Manufacturing. 2019, vol. 58, pp.  80–96. https://doi.org/10.1016/j.rcim.2019.01.012

14. Thike P.H., Xu Z., Cheng Y., Jin Y., Shi P. Materials failure analysis utilizing rule-case based hybrid reasoning method. Engineering Failure Analysis. 2019, vol. 95, pp. 300–311. https://doi.org/10.1016/j.engfailanal.2018.09.033

15. Guo Y., Chen W., Zhu Y.-X., Guo Y.-Q. Research on the integrated system of case-based reasoning and Bayesian network. ISA Transactions. 2019, vol. 90, pp. 213–225. https://doi.org/10.1016/j.isatra.2018.12.049

16. Relicha M., Pawlewskib P. A case-based reasoning approach to cost estimation of new product development. Neurocomputing. 2018, vol. 272, pp. 40–45. https://doi.org/10.1016/j.neucom.2017.05.092

17. Xia J., Chen G., Tan P., Zhang C. An online case-based reasoning system for coal blends combustion optimization of thermal power plant. International Journal of Electrical Power & Energy Systems. 2014, vol. 62, pp. 299–311. https://doi.org/10.1016/j.ijepes.2014.04.036

18. Karpov L.E., Yudin V.N. Adaptive management based on classification of controlled objects states. In: Proceedings of the Institute of System Programming of the Russian Academy of Sciences, vol. 13, part 2. Moscow: ITs ISP, 2007, pp. 37–58. (In Russ.).

19. Kulakov S.M., Trofimov V.B., Dobrynin A.S., Taraborina E.N. CBR approach to the formation of control programs for objects of cyclic action. In: Automation Systems in Education, Science and Production AS’2017. Proceedings of the 11th All-Russ. Sci. and Pract. Conf. (with int. participation), December 14-16, 2017. Novokuznetsk: ITs SibSIU, 2017, pp. 11–19. (In Russ.).

20. Monfeta D., Corsib M., Choinièreb D., Arkhipovab E. Development of an energy prediction tool for commercial buildings using case-based reasoning. Energy and Buildings. 2014, vol. 81, pp. 152–160. https://doi.org/10.1016/j.enbuild.2014.06.017