DEVELOPMENT OF MACHINE LEARNING MODEL OF PREDICTION HIGH-TEMPERATURE METALLURGICAL PROPERTIES OF COKE

R. V. Kovalchik

doi:10.24412/2413-7383-68-78

R. V. Kovalchik

About the authors

R. V. Kovalchik

Ковальчик Роман Владиславович - кандидат технических наук, заведующий кафедрой информатики и вычислительной техники ФГБОУ ВО «Приазовский государственный технический университет», Российская Федерация, ДНР, г. Мариуполь. Область научных интересов: системный анализ, математическое моделирование, искусственный интеллект, автоматизация технологических процессов черной металлургии.

UDC: 004.932.2:004.622
DOI: 10.24412/2413-7383-68-78
Language: Russian

Abstract: The results of coke high -temperature metallurgical properties prediction models developed by the author are presented. The optimal model of machine learning was selected, which allowed to achieve the highest accuracy of prediction of the target index, as which the coke reactivity index CRI is used. The peculiarity of the models is the use of coal charge grade composition as input values. The proposed approach does not require additional laboratory studies of physicochemical properties of coal charge for coke production.

Keywords: machine learning, neural networks, optimizers, metallurgical coke, forecasting.

References

Improvement of blast furnace coke quality by means of special reagents / V.A.Tamko, I.V.Zolotarev, E.I.Zbykovsky et al. // Uglekhimicheskiy Zhurnal. - 2009. - № 5. - С. 13-20. Разработка модели машинного обучения для прогнозирования… К
Blast-furnace coke quality estimation / A.S. Afanasyev, Yu.V. Konovalova, S.G. Gagarin et al. https://www.researchgate.net/publication/293525211_Estimation_of_metallurgical_coke_quality
Kovalchik, R.V. Variation of the blast furnace smelting intensity when using coke with different hot strength (in Russian) / R.V. Kovalchik, A.A. Tomash, Yu.A. Zinchenko // Metal and casting of Ukraine. - 2008. - № 7-8. - С. 27-30.
Doroganov, V. S. Sukhanova, E. Yu. Forecasting of the coke characteristics on the basis of the charge indicators (in Russian) // Proceedings of the All-Russian youth school-seminar “Analysis, geometry and topology”. Barnaul: AltSU, 2013. С. 46 - 50.
Stankevich A. S. Gilyazetdinov R. R. R. Popova N. K. Koshkarov D. A. Model for prediction of coke CSR and CRI indicators on the basis of chemical and petrographic parameters of coal charge and conditions of their coking // Coke and Chemistry. 2008. № 9. С. 37 - 44.
Influence of reactivity and “hot” strength of coke on technical and economic indicators of blast furnace melting in conditions of JSC “AZOVSTAL IRON & STEEL WORKS” / A.A. Tomash, V.P. Tarasov, R.V. Kovalchik [et al.] // Bulletin of Priazov State Technical University. - 2007. - № 17. - С. 9-13.
Kovalchik, R.V. Tomash, A.A. Theoretical evaluation of the influence of hot strength of coke CSR on its specific consumption in blast furnace melting (in Russian) // Vestnik of Priazov State Technical University. Series: Technical sciences. - 2008. - № 18. - С. 15-18.

Issue: 1(36)'2025
Section: SYSTEM ANALYSIS, CONTROL AND INFORMATION PROCESSING, STATISTICS
How to cite: R. V. Kovalchik. DEVELOPMENT OF MACHINE LEARNING MODEL OF PREDICTION HIGH-TEMPERATURE METALLURGICAL PROPERTIES OF COKE // Problems of Artificial Intelligence. - 2025. - № 1 (36). - P. 68-78. - https://paijournal.guiaidn.ru/en/2025/1(36)-6.html


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	DEVELOPMENT OF MACHINE LEARNING MODEL OF PREDICTION HIGH-TEMPERATURE METALLURGICAL PROPERTIES OF COKE
About the magazine News Goals and sphere Founder and publisher Editorial Board Licensing conditions Confidentiality Attitude towards plagiarism Publication ethics Archiving Policy Subscription For authors Instructions for authors The review process Copyright Agreement on the transfer of rights Editorial fees Archive All issues Search Contacts Contacts	R. V. Kovalchik About the authors R. V. Kovalchik Ковальчик Роман Владиславович - кандидат технических наук, заведующий кафедрой информатики и вычислительной техники ФГБОУ ВО «Приазовский государственный технический университет», Российская Федерация, ДНР, г. Мариуполь. Область научных интересов: системный анализ, математическое моделирование, искусственный интеллект, автоматизация технологических процессов черной металлургии. UDC: 004.932.2:004.622 DOI: 10.24412/2413-7383-68-78 Language: Russian Abstract: The results of coke high -temperature metallurgical properties prediction models developed by the author are presented. The optimal model of machine learning was selected, which allowed to achieve the highest accuracy of prediction of the target index, as which the coke reactivity index CRI is used. The peculiarity of the models is the use of coal charge grade composition as input values. The proposed approach does not require additional laboratory studies of physicochemical properties of coal charge for coke production. Keywords: machine learning, neural networks, optimizers, metallurgical coke, forecasting. References Improvement of blast furnace coke quality by means of special reagents / V.A.Tamko, I.V.Zolotarev, E.I.Zbykovsky et al. // Uglekhimicheskiy Zhurnal. - 2009. - № 5. - С. 13-20. Разработка модели машинного обучения для прогнозирования… К Blast-furnace coke quality estimation / A.S. Afanasyev, Yu.V. Konovalova, S.G. Gagarin et al. https://www.researchgate.net/publication/293525211_Estimation_of_metallurgical_coke_quality Kovalchik, R.V. Variation of the blast furnace smelting intensity when using coke with different hot strength (in Russian) / R.V. Kovalchik, A.A. Tomash, Yu.A. Zinchenko // Metal and casting of Ukraine. - 2008. - № 7-8. - С. 27-30. Doroganov, V. S. Sukhanova, E. Yu. Forecasting of the coke characteristics on the basis of the charge indicators (in Russian) // Proceedings of the All-Russian youth school-seminar “Analysis, geometry and topology”. Barnaul: AltSU, 2013. С. 46 - 50. Stankevich A. S. Gilyazetdinov R. R. R. Popova N. K. Koshkarov D. A. Model for prediction of coke CSR and CRI indicators on the basis of chemical and petrographic parameters of coal charge and conditions of their coking // Coke and Chemistry. 2008. № 9. С. 37 - 44. Influence of reactivity and “hot” strength of coke on technical and economic indicators of blast furnace melting in conditions of JSC “AZOVSTAL IRON & STEEL WORKS” / A.A. Tomash, V.P. Tarasov, R.V. Kovalchik [et al.] // Bulletin of Priazov State Technical University. - 2007. - № 17. - С. 9-13. Kovalchik, R.V. Tomash, A.A. Theoretical evaluation of the influence of hot strength of coke CSR on its specific consumption in blast furnace melting (in Russian) // Vestnik of Priazov State Technical University. Series: Technical sciences. - 2008. - № 18. - С. 15-18. Issue: 1(36)'2025 Section: SYSTEM ANALYSIS, CONTROL AND INFORMATION PROCESSING, STATISTICS How to cite: R. V. Kovalchik. DEVELOPMENT OF MACHINE LEARNING MODEL OF PREDICTION HIGH-TEMPERATURE METALLURGICAL PROPERTIES OF COKE // Problems of Artificial Intelligence. - 2025. - № 1 (36). - P. 68-78. - https://paijournal.guiaidn.ru/en/2025/1(36)-6.html
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