Temperature Model And Optimal Setting Research Of Regenerative Reheating Furnace

Reheating furnace is one of the important equipment in the production line of steel rolling. The reasonable control of slab temperature distribution can assure rolling quality and reduce energy consume in reheating furnace. However, in the actual industrial production, the slab temperature detection is a complex problem that industrial process parameters related to product quality are not easy to be reached. The most effective way to improve the heating quality of slab and reduce energy consumption of the reheating furnace is establishing a mathmatic model of slab temperature and calculating the optimal setting value for furnace temperature by using advanced modeling and control technology.This paper does the research on regenerative reheating furnace system. Firstly based on heat transfer mechanism, a slab temperature prediction model is established to realize the soft measurement of slab temperature in the furnace. Then the discrete method of solving the model is discussed, and the advantages and disadvantages of explicit difference and C-N difference are also compared in this paper. In the process of solving the model, the variation of thermal physical parameters of slab is considered to further improve the precision of the model.The total heat exchange factor is vital to improve the precision of the model. In order to overcome the non-stationary of total heat exchange factor and enhance the adaptive ability of the model, a fuzzy inference systerm employing the discharging slab surface temperature as the feedback signal is designed to compensate total heat exchange factor under the condition of the steady-state of the furnace. Due to the proper dynamic compesation for the total heat exchange factor, the model has a more accurate prediction precision.Based on the established slab temperature prediction model, the optimal setting method of furnace temperature is also discussed in this paper. Firstly, the objective function is established according to the requirements of the heating furnace production target. And then by using genetic algorithm, we can obtain the optimal furnace temperature and the best slab heating process while the furnace is in steady-state. Finally, a dynamic compensation strategy for the steady-state optimal temperature is presented in order to realize furnace temperature dynamic optimization settings.Simulation results indicate that the slab temperature can be quite accurately calculated by the slab temperature model. The absolute errors of slab temperature predicted by the modified model are within the±5℃limits, which can meet the technological requirements. After the realization of dynamic optimization settings for furnace temperature, we can greatly improve the heating quality of slabs and reduce the energy consumption of the reheating furnace in the premise to ensure normal rolling process, which indicates that the the optimal setting of furnace temperature is verified to be valid and efficient.