Artificial neural network study of the electrical conductivity of mould flux

Qin, R. (2021). Artificial neural network study of the electrical conductivity of mould flux. Materials Science and Technology, 37(18) pp. 1476–1482.

DOI: https://doi.org/10.1080/02670836.2021.2016269

Abstract

The electrical conductivity of mould flux with chemical constitution of CaO-SiO₂-Al₂O₃-NaO-K2O-MgO-CaF₂-Cr₂O₃-FeO-MnO has been investigated. The assessed database contains one unitary, five binary, nine ternary, four quaternary, two quinary, two senary and one octonary subsystems. Each constitutional component is in connection to another via some direct or indirect links. A multilayer artificial neural network method was developed and implemented to the database. The work provides a method to calculate the relationships between the composition, temperature and electrical property of the mould flux within the defined parameter ranges. The results have been validated against those experimental data that are not included in the training of the neural networks.

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About

• Item ORO ID
• 81213
• Item Type
• Journal Item
• ISSN
• 0267-0836
• Project Funding Details

• Funded Project Name	Project ID	Funding Body
  Optimisation of Local Heat Transfer in the CC Mould for Casting Challenging and Innovative Steel Grades	847269	European Commission RFCS
  UKCOMES	EP/R029598/1	EPSRC

• Keywords
• Electrical conductivity; Mould flux; Multilayer neural network; Continuous casting
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Engineering and Innovation
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 2021 Rongshan Qin
• Depositing User
• Rongshan Qin