Modelling of Artificial Neural Network to Validate the Experimental Data of a Laboratory Scale ROT

Abstract

Run Out Tables (ROTs) are critical in the metallurgical sector for producing unique steel grade. The cooling rate controls the fine structure of steel, which is influenced by a number of factors such as the convective heat transfer coefficient, mean film temperature and many others. As a result, achieving a new steel grade necessitates the optimum combination of all of these factors. The cooling rate as a function of steel characteristics is obtained employing laboratory data such as convective heat transfer coefficient, mean film temperature, and mass flow rate of coolant at preset upper and lower nozzle distances from the experimental setup. Three Artificial Neural Network programs have been used to validate and check the performance of the experimental setup for optimize the cooling rate.