Determination of the reduction coefficients in a continuous finishing group of stands in a hot rolling mill using artificial neural network

Abstract

There are many problems when creating digital doubles. One of which is the definition of the source data: in this case, the definition of private reductions (coefficient ratios) in a continuous finishing group of stands of the broadband hot rolling mill 2000 of PJSC Magnitogorsk Iron and Steel Work. Algorithms were developed to calculate the thickness of the breakdown bar, the number of stands involved, power required for rolling and coefficient ratios to solve this problem. The algorithms are based on well-known solutions (the Imai method) using neural networks. The training of neural networks was conducted on a sample collected for the period from 01.01.2017 to 01.01.2019 work of the mill in the PyTorch library of the interpreted programming language Python. The average error εME of the calculation of coefficient ratios (according to the developed algorithms with neural networks) does not exceed 8.9% and the standard deviation σ does not exceed 0.074.

An Experimental Study of the Thermal State of a Steel Billet during Hot Rolling

“…The heat,released at the moment of deformation,is evenly distributedthrough the volume, and it results in an average temperature increase(deformation perturbation) by 20...50 0 С. The received results correlate well with similar studies by the authors [21][22][23][24][25].…”

“…• provided convective air cooling, which, depending on the character of the movement (laminar or turbulent), is determined according to the calculated value of Nusselt number [21][22][23][24][25]:…”

“…where σ is Stefan-Boltzmann constant; T air is an ambient (air) temperature. According to the works [21][22][23][24][25], the coefficient of radiative heat transferbetween the strip and the ambient (air) temperature (for steel) is determined:…”