Estimating the Convection Heat Transfer Coefficient of a Run-Out Cooling Table in a Steel-Making Process by Neural Networks

This paper presents a real-world application of neural networks. This application considers the estimation of the convection heat transfer coefficient of a run-out cooling table in a steel-making process. Firstly, data of several runs were collected considering the cooling table variables and the reached temperatures. Afterwards, using numerical models and optimization, the equivalent heat transfer coefficient is evaluated for each run. Finally, a neural network is applied to define the relationships between the process variables (thickness, water flow, among others) and the estimated heat transfer coefficient. The results are compared with some models derived from the process physics.