A novel analytical–artificial neural network model to improve efficiency of high pressure descaling nozzles in hot strip rolling of steels

Abstract

Hot strip mills use hydraulic descaling to remove oxide scales from steel strip formed after the reheat furnace and during hot rolling. In the present work, a novel analytical–artificial neural network (AANN) model was developed to improve the efficiency of high pressure (HP) hydraulic descaling operation using flat spray nozzles. The AANN model is able to analytically compute the spray force and depth and to estimate the spray impact using an artificial neural network approach. The combined model was trained based on the industrial data from the hot strip rolling mills of Mobarakeh Steel Complex. The spray angle, spray pressure, vertical spray height and water flowrate were all considered as the main input parameters of the HP descaling operation. The AANN model can predict the spray force, impact and depth under any given descaling condition. A sensitivity analysis was carried out using the combined model. It is shown that, among all process parameters, the spray angle followed by the spray height are the most important parameters affecting the spray impact. The model developed can be used as a proper tool to improve the efficiency of the descaling system in terms of achieving the highest spray impact under any process condition.