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

Publikation: KonferenzbeitragPaper


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.
Seiten1109 - 1116
PublikationsstatusVeröffentlicht - 4 Juli 2019
VeranstaltungCongress on Numerical Methods in Engineering 2019 - Guimaraes, Portugal
Dauer: 1 Juli 20193 Juli 2019


KonferenzCongress on Numerical Methods in Engineering 2019

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