Parameter optimization for continuous casting of low carbon steel based on big data mining

As to the surface defects in hot rolled coils, based on the neural network prediction model in big data mining, a new method (prediction model method, PMM) for optimizing continuous casting process parameters was proposed. The multi sample continuous variation chart of the possibility of surface defects in each continuous casting parameter can be obtained by the PMM method. And based on it, corresponding influence law, key process parameters and critical values can also be obtained. The results show that among the parameters of argon blowing, the protective argon flow has the most obvious effect on the surface defects of low carbon steel hot rolled coils and has a negative correlation. The optimal argon blowing flow for the stopper rod and the nozzle position are 3.0L/min and 1.8L/min, respectively. Among the heat flow parameters of the mold, the influence of the water flow on the inner arc side is the most obvious, and the best range of water temperature difference on each surface is 7-9℃, the best water inlet temperature is about 35℃. At the same time, the possibility of surface defects increases significantly with the increase of the casting speed, the width of the slab, and the increase of the casting length, but it gradually decreases with the increase of the weight of the molten steel in the tundish. The casting speed, slab width, protective argon flow and mold cooling water flow are the key process parameters that affect the formation of surface defects in hot rolled coils. And the possibility of defect occurrence is the most sensitive to the fluctuation of the total cooling water flow of the crystallizer, and its critical lower limit is 8700L/min.