Abstract
Aiming at the difficulty of continuous measurement of charge temperature in intermediate frequency furnace smelting process, this paper proposes a method of continuous measurement of charge temperature in intermediate frequency furnace smelting process by using Transformer model, which makes use of the characteristic that the resistivity of metal changes nonlinearly with temperature in the melting process. Analyze the circuit structure of the medium frequency furnace, build the charge resistance data acquisition system on the medium frequency furnace, collect a large number of charge temperature, equivalent resistance and other data with the help of instruments, train one part of the collected data with the transformer model, and take another part of the collected data for performance evaluation. After the model is trained, import the resistance data inferred by the built resistance acquisition system into the transformer model, in this way, the temperature data of the current charge of the medium frequency furnace can be calculated in real time, and the temperature data can be transmitted to the upper computer for real-time display.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Shi, M., Chen, R.: Application of medium frequency furnace in cast steel production. Casting 67(1), 49–50, 54 (2018). https://doi.org/10.3969/j.issn.1001-4977.2018.01.012
Xi, W.: Intermediate Frequency Electric Stove Energy-Saving Principle and Method of Research. Shandong University of Technology, Shandong (2012). https://doi.org/10.7666/d.D586340
Saxena, M.R., Maurya, R.K., Mishra, P.: Assessment of performance, combustion and emissions characteristics of methanol-diesel dual-fuel compression ignition engine: a review. J. Traffic Transp. Eng. (Engl. Ed.) 8(05), 638–680 (2021)
Lu, Q.: Application and maintenance of medium frequency furnace. Equip. Manag. Maint. (18), 64–65 (2018). https://doi.org/10.16621/j.cnki.issn1001-0599.208.09D.37
Tang, L., et al.: Unravelling the precipitation evolutions of AZ80 magnesium alloy during non-isothermal and isothermal processes. J. Mater. Sci. Technol. 75(16), 184–195 (2021)
Zhou, D., Cheng, S.: A new method to detect the high temperature distribution in the ironmaking and steelmaking industry. In: Hwang, J.-Y., et al. (eds.) 8th International Symposium on High-Temperature Metallurgical Processing. The Minerals, Metals & Materials Series, pp. 497–505. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-51340-9_49
Sa'id Waladin, K., Jasim Omar, F., Raafat Omar, F.: Estimation of induction furnace charge temperature using multiple model adaptive estimator (MMAE). In: 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 207–212. IEEE (2019)
Soares Fabio, M., Oliveira Roberto, C.L.: Modelling of temperature in the aluminium smelting process using neural networks. In: International Joint Conference on Neural Networks. IEEE (2010)
Li, J., Ma, B.: Parameters adjustment for VOD endpoint carbon content and endpoint temperature prediction model. In: International Symposium on Instrumentation & Measurement. IEEE (2014)
Zhai, N., Zhou, X.: Temperature prediction of heating furnace based on deep transfer learning. SENSORS 20(17), 1–27 (2020)
Zhou, P., Guo, D., Wang, H., Chai, T.: Data-driven robust M-LS-SVR-based NARX modeling for estimation and control of molten iron quality indices in blast furnace ironmaking. IEEE Trans. Neural Netw. Learn. Syst. 29, 4007–4021 (2018)
Wang, X.: Ladle furnace temperature prediction model based on large-scale data with random forest. IEEE/CAA J. Autom. Sinica 4, 770–774 (2017)
Lee, S.Y., Tama, B.A., Choi, C., et al.: Spatial and sequential deep learning approach for predicting temperature distribution in a steel-making continuous casting process. IEEE Access PP(99), 1 (2020)
Zhang, X., Kano, M., Matsuzaki, S.: A comparative study of deep and shallow predictive techniques for hot metal temperature prediction in blast furnace ironmaking. Comput. Chem. Eng. 130(1), 106575 (2019)
Hua, B., Xu, H.: Development and application of non—contact thermometry in combustion process. Instrum. Anal. Monit. (2) (2021). https://doi.org/10.3969/j.issn.1002-3720.2021.02.005
Leon-Medina, J.X., Camacho, J., et al.: Temperature prediction using multivariate time series deep learning in the lining of an electric arc furnace for ferronickel production. Sensors, 21, 6894 (2021)
Roy, S., Ramana, C.V.: Effect of sintering temperature on the chemical bonding, electronic structure and electrical transport properties of β-Ga_(1.9)Fe_(0.1)O_3 compounds. J. Mater. Sci. Technol. 67(08), 135–144 (2021)
Wang, W., et al.: Microstructure and properties of novel Al-Ce-Sc, Al-Ce-Y, Al-Ce-Zr and Al-Ce-Sc-Y alloy conductors processed by die casting, hot extrusion and cold drawing. J. Mater. Sci. Technol. 58(23), 155–170 (2020)
Cheng, Y., Ma, D., Guo, C., Yang, F., Mu, T., Gao, Z.: An experimental study on the conductivity changes in coal during methane adsorption-desorption and their influencing factors. Acta Geologica Sinica (Engl. Ed.) 93(03), 704–717 (2019)
Bajorek, A., Chekowska, G.: Microstructure and electrical resistivity in the GdNi_(5–x)Cu_x intermetallic series. J. Rare Earths 35(01), 71–78 (2017)
Xu, W., Hou, Y., Song, W., Zhoum Y., Yin, T.: Resistivity and thermal infrared precursors associated with cemented backfill mass. J. Cent. South Univ. 23(09), 2329–2335 (2016)
Ashish, V., Noam, S., Niki, P., et al.: Attention Is All You Need. arXiv (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ma, S., Li, Y., Luo, D., Song, T. (2022). Temperature Prediction of Medium Frequency Furnace Based on Transformer Model. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_35
Download citation
DOI: https://doi.org/10.1007/978-981-19-6142-7_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6141-0
Online ISBN: 978-981-19-6142-7
eBook Packages: Computer ScienceComputer Science (R0)