Mobarakeh Steel Complex Research Articles

Artificial Neural Network Modeling of High Pressure Descaling Operation in Hot Strip Rolling of Steels

High pressure (HP) hydraulic descaling is usually used in hot strip mills to remove oxide scales from steel strip formed after the reheat furnace and during hot rolling. In the present work, an artificial neural network (ANN) model was developed to improve efficiency of the HP hydraulic descaling operation using flat spray nozzles. The model was trained based on the industrial data from the hot strip rolling mills of Mobarakeh Steel Complex. The spray angle, inclination angle, spray pressure, vertical spray height and water flow rate were all considered as the main input parameters of the HP descaling operation.The ANN model developed is able to predict spray impact, spray width and spray depth for any given spray nozzle system. The model can also analytically compute the spray overlap for a given spray nozzle arrangement. A sensitivity analysis was carried out using the ANN model. It was shown that, among all process parameters, the spray angle followed by the inclination angle 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 and an optimum spray overlap for any process condition.

Work roll thermal expansion control in hot strip mill

Work roll thermal expansion (crown) plays an important role in controlling strip profile and flatness of the strips. This can be done with a perfect and controllable cooling system which also takes care of the work roll against damage induced by fire cracking. In this paper, a computer model based on finite difference method is developed under transient condition to calculate the temperature and thermal crown profile in the work roll. The model has the ability of accepting variable boundary condition in circumferential and axial direction for different cooling configurations such as using different types and numbers of nozzles and headers in different direction. The results of simulation are compared and verified with an actual rolling program result for which the temperature of a work roll was measured at Mobarakeh Steel Complex.

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

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.

Oxide Inclusions at Different Steps of Steel Production

Abstract The formation of oxide inclusions in one of the carbon steel productions of Mobarakeh Steel Complex of Isfahan has been evaluated. Several samples from different steps of steel production were taken, from are furnace, ladle furnace, tundish, and continuous casting mold. Moreover, samples of slab and hot rolling products were prepared. The samples were investigated by optical and scanning electron microscopes equipped with the EDS system. According to the results, the number, composition, and kind of inclusions were directly influenced by the production variables. It was found that when the amount of dissolved oxygen was high (say more than 0.002 5%), the dissolved aluminum was able to reduce silicon oxide and react with the dissolved oxygen simultaneously, whilst, the dissolved aluminum could reduce the magnesium oxide only when the oxygen content was below 0.000 5%. Based on this research, a mechanism for forming the complex inclusions was suggested. It was also found that if the aluminum is added to the melt as late as possible, a cleaner melt with fewer inclusions is prepared; this method will be more effective, especially in the case of complex inclusions.