Modelling of a steel ladle and prediction of its thermomechanical behavior by finite element simulation together with artificial neural network approaches

Research output: Contribution to conferencePaper


Refractory linings of industrial vessels are of decisive importance for high
temperature industries. To facilitate the lining design for various material properties and lining configurations, quantitative prediction of thermomechanical responses is of importance prior to industrial application. 192 lining configurations including 10 geometrical and material property variations of a steel ladle lining were defined by six orthogonal arrays for finite element (FE) simulations. The maximum compressive stress at the hot face of the working lining and the maximum tensile stress at the cold end of the steel shell were the selected responses of interest. The impact of geometrical and material property variations on thermomechanical performance of the selected ladle was assessed
by analysis of variance (ANOVA) and signal-to-noise (S/N) ratio using 32 lining concept results from one out of six orthogonal arrays. Two optimized lining concepts were proposed accordingly. Their responses were well predicted by a three-layer backpropagationartificial neural network (BP-ANN) model.
Original languageEnglish
Pages1109 - 1116
Number of pages7
Publication statusPublished - 4 Jul 2019
EventCongress on Numerical Methods in Engineering 2019 - Guimaraes, Portugal
Duration: 1 Jul 20193 Jul 2019


ConferenceCongress on Numerical Methods in Engineering 2019
Internet address


  • Steel ladle
  • Finite element simulation
  • Thermomechanical behavior
  • Artificial Neural Network

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