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ArticleName Application of case-based reasoning in hazard evaluation in complex process flow control
DOI 10.17580/em.2023.02.09
ArticleAuthor Trofimov V. B., Temkin I. O., Solodov S. V.

National University of Science and Technology—NUST MISIS, Moscow, Russia

Trofimov V. B., Associate Professor, Candidate of Engineering Sciences
Temkin I. O., Head of Department, Doctor of Engineering Sciences,
Solodov S. V., Director of College of Information Technologies and Computer Sciences, Candidate of Engineering Sciences


The article discusses the Case-Based Reasoning method which enables solving new problems that may arise during decision-making by using or adapting solutions of the similar known problems on the basis of accumulated data and knowledge on past situations or cases contained in a knowledge base. The metrics of similarity between the parameters of a current situation and previous cases, and the methods to retrieve and adapt the cases are described. The case information model used for the process management is presented and exemplified. The conditions and ranges of efficient case-based reasoning application in the socio-technical system control in case of nonstationary, nonlinear and sluggish processes are discussed. The authors propose the procedure for searching similar cases using the classical metrics and the Random Forest method, and describe the generalized control of a complex process or an object using the concepts of the industrial internet of things and the case-based reasoning.

keywords Control, decision-making, case, metric, library of cases, case-based search of solutions

1. Osipov G. S. Methods of Artificial Intelligence. Moscow : Fizmatlit, 2015. 295 p.
2. Sokolov I. A. Theory and practice of application of artificial intelligence methods. Vestnik RAN. 2019. Vol. 89, No. 2. pp. 115–119.
3. Vassilyev S. N., Novikov D. A., Bakhtadze N. N. Intelligent control of industrial processes. IFAC Proceedings Volumes (IFAC-Papers Online). 2013. Vol. 46, No. 9. pp. 49–57.
4. Okhtilev M. Yu., Sokolov B. V., Yusupov R. M. Intelligent technologies for monitoring and controlling the structural dynamics of complex objects. Moscow : Nauka, 2006. 410 p.
5. Gu M.-Q., Xu A.-J., Yuan F., He X.-M., Cui Z.-F. An improved CBR model using time-series data for predicting the end-point of a converter. ISIJ International. 2021. Vol. 61, No. 10. pp. 2564—2570.
6. Trofimov V. B. An approach to intelligent control of complex industrial processes: An example of ferrous metal industry. Automation and Remote Control. 2020. Vol. 81, No. 10. pp. 1856–1864.
7. Cheng Y., Xing J., Dong J., Wang Z., Wang X. Improved CBR for endpoint carbon content prediction of BOF steelmaking. The 9th International Conference on Intelligent Control and Information Processing (ICICIP). 2018. pp. 168–175. DOI: 10.1109/ICICIP.2018.8606688

8. Khosravani M. R., Nasiri S. Injection molding manufacturing process: review of case-based reasoning applications. Journal of Intelligent Manufacturing. 2020. Vol. 31. pp. 847–864.
9. Bolender T., Burvenich G., Dalibor M., Rumpe B., Wortmann A. Self-adaptive manufacturing with digital twins. Proceedings—The 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). 2021. pp. 156–166. DOI: 10.1109/SEAMS51251.2021.00029
10. Xu L., Huang C., Li C., Wang J., Liu H. et al. A novel intelligent reasoning system to estimate energy consumption and optimize cutting parameters toward sustainable machining. Journal of Cleaner Production. 2020. Vol. 261. DOI: 10.1016/j.jclepro.2020.121160
11. Hadjiski M., Deliiski N., Tumbarkova N. Intelligent hybrid control of thermal treatment processes of wood. The 2020 IEEE 10th International Conference on Intelligent Systems, IS 2020 – Proceedings. 2020. pp. 482–489.
12. Kuzyakov O. N., Andreeva M. A. Applying case-based reasoning method for decision making in IIoT system. The 2020 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon. 2020. DOI: 10.1109/FarEastCon50210.2020.9271301
13. Yan H., Wang F., Yan G., He D. Hybrid approach integrating case-based reasoning and Bayesian network for operational adjustment in industrial flotation process. Journal of Process Control. 2021. Vol. 103. pp. 34–47.
14. Su W., Lei Z. Rough case-based reasoning system for continues casting. Proceedings of SPIE—The International Society for Optical Engineering. 2018. Vol. 10696. DOI: 10.1109/IS48319.2020.9200096
15. Li Q., Lu S., Yu H., Wane X., Liu H. Research on optimal setting of cement decomposing furnace temperature based on case reasoning. Proceedings—The 2020 35th Youth Academic Annual Conference of Chinese Association of Automation, YAC. 2020. pp. 626–631.
16. Zaytseva E. V., Medyanik N. L. Automated integrated production and selling planning at processing plant in the cement industry. GIAB. 2022. No. 2. pp. 111–123.
17. Zaytseva E. V. Strategic management in the cement industry. GIAB. 2019. No. 2. pp. 214–220.
18. Li Y., Shao L., Liu S. Case-based reasoning forecast method of coal mine emergencies. Liaoning Gongcheng Jishu Daxue Xuebao (Ziran Kexue Ban). Journal of Liaoning Technical University (Natural Science Edition). 2014. Vol. 33, No. 7. pp. 902–906.
19. Kupriyanov V. V., Temkin I. O., Bondarenko I. S. Study of the time characteristics for emergency situations in the coal mines. Occupational Safety in Industry. 2022. Vol, No. 1, pp. 39–45.
20. Jinsheng S., Haigang F. Implementation of CBR strategy for combustion control of blast furnace stoves. Chinese Control and Decision Conference. 2008. DOI: 10.1109/CCDC.2008.4597517
21. Qiu H., Tian J., Jian L. Parameter fusion modeling method for hot strip rolling process and its application. Proceedings of the 33rd Chinese Control and Decision Conference. 2021. Vol. 10696. pp. 374–378.

22. Belyakov S. L., Bozhenyuk A. V., Belyakova M. L. Case based analysis of geo-information models of logistics projects. Nauka i tekhnologii zheleznykh dorog. 2019. Vol. 3, No. 2(10). pp. 53–63.
23. Belyakov S. L., Bozhenyuk A. V., Belyakova M. L., Zubkov S. A. Case based reasoning in intelligent geographic information systems for the management of logistics projects. CEUR Workshop Proceedings: 2nd International Scientific and Practical Conference on Fuzzy Technologies in the Industry—FTI. 2018. pp. 1–10.
24. Gordienko L. V., Samoylov L. K. Geoinformation model for definition of proximity of logistic precedents. Sovremennye innovatsionnye tekhnologii podgotovki inzhenernykh kadrov dlya gornoi promyshlennosti i transporta. 2016. No. 1(3). pp. 294–299.
25. Bondarenko I. S. Elaboration of plans–forecasts based on engineering-andeconomic performance of mines. GIAB. 2022. No. 3 pp. 97–107.
26. Rintala L., Leikola M., Sauer Ch., Aromaa J., Roth-Berghofer T., Forsén O., Lundström M. Designing gold extraction processes: Performance study of a case-based reasoning system. Minerals Engineering. 2017. Vol. 109. pp. 42–53.
27. Leikola M., Sauer C., Rintala L., Aromaa J., Lundström M. Assessing similarities between gold ores, concentrates, and tailings with casebased reasoning. Minerals Engineering. 2020. Vol. 146. ID. 106113.
28. Tadubana G., Sigweni B., Suglo R. Case-based reasoning system for prediction of fuel consumption by haulage trucks in open-pit mines. International Journal of Electrical and Computer Engineering. 2021. Vol. 11, No. 4. pp. 3129–3136.
29. Biswas S., Devi D., Chakraborty M. A hybrid case based reasoning model for classification in internet of things (IoT) environment. Journal of Organizational and End User Computing. 2018. Vol. 30, No. 4. pp. 104–122.
30. Rodionov I. V., Sozontov A. N. On confidence estimation based on quantitative similarity coefficients. Automation and Remote Control. 2020. Vol. 81, No. 2. pp. 320–332.
31. Temkin I. O., Klebanov D. A., Deryabin S. A., Konov I. S. Building an intelligent geoinformation system of a mining enterprise using predictive analytics methods. GIAB. 2020. No. 3. pp. 114–125.
32. Vagin V. N., Golovina E. Yu., Zagoryanskaya A. A., Fomina M. V. Reliable and plausible inference in intelligent systems. Moscow : Fizmatlit, 2008. 712 p.
33. Luger G. F. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. USA : Pearson Education, 2008. 754 p.
34. Kabiesz J., Sikora B., Sikora M., Wrobel L. Application of rule-based models for seismic hazard prediction in coal mines. Acta Montanistica Slovaca. 2013. Vol. 18, No. 4. pp. 262–277.

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