Результаты исследований: Глава в книге, отчете, сборнике статей › Материалы конференции › Рецензирование
Результаты исследований: Глава в книге, отчете, сборнике статей › Материалы конференции › Рецензирование
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TY - GEN
T1 - Models of Operational Forecasting of Arc Furnace Energy Consumption Using Machine Learning Methods
T2 - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research & Practice (PEAMI)
AU - Saidmurodov, Begmurod
AU - Zyuzev, Anatoly
AU - Lyukhanov, Egor
N1 - I sincerely thank Ismoil Nazrimadovich Odinaev for his valuable comments and quality editing of the text, which significantly improved the readability and conciseness of the article. I also express my deep gratitude to Murodbek Kholnazarovich Safaraliev for providing access to the necessary data and information resources, which allowed for a more in-depth analysis of the research.
PY - 2023
Y1 - 2023
N2 - This paper is an overview of the current research in the field of predicting the energy consumption of electric arc furnaces with emphasis on literature review. Electric arc furnaces play a key role in the metallurgical and metalworking industries, and optimizing their energy consumption is a crucial task to improve production efficiency. This paper analyzes the state-of-the-art machine learning methods used to predict the energy consumption of electric arc furnaces. Special attention is given to the literature review, which is a comparative analysis of previous research in this area. Various methods including neural networks, regression analysis, time series and others are reviewed. The literature review not only provides an assessment of the current state of research, but also identifies limitations and potential directions for future research. The conclusions and recommendations presented in the paper can serve as a basis for further research in the field of optimizing energy consumption in electric arc furnaces and improving the efficiency of metallurgical production. © 2023 IEEE.
AB - This paper is an overview of the current research in the field of predicting the energy consumption of electric arc furnaces with emphasis on literature review. Electric arc furnaces play a key role in the metallurgical and metalworking industries, and optimizing their energy consumption is a crucial task to improve production efficiency. This paper analyzes the state-of-the-art machine learning methods used to predict the energy consumption of electric arc furnaces. Special attention is given to the literature review, which is a comparative analysis of previous research in this area. Various methods including neural networks, regression analysis, time series and others are reviewed. The literature review not only provides an assessment of the current state of research, but also identifies limitations and potential directions for future research. The conclusions and recommendations presented in the paper can serve as a basis for further research in the field of optimizing energy consumption in electric arc furnaces and improving the efficiency of metallurgical production. © 2023 IEEE.
UR - http://www.scopus.com/inward/record.url?partnerID=8YFLogxK&scp=85178519037
U2 - 10.1109/PEAMI58441.2023.10299891
DO - 10.1109/PEAMI58441.2023.10299891
M3 - Conference contribution
SN - 979-835032215-6
SP - 205
EP - 210
BT - Proceedings - 2023 Russian Workshop on Power Engineering and Automation of Metallurgy Industry: Research and Practice, PEAMI 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 29 September 2023 through 1 October 2023
ER -
ID: 49270762