RH furnace endpoint temperature prediction model driven by digital twin and AO-ELM fusion
RH refining furnace is the key equipment in the process of iron and steel smelting,and its endpoint tem-perature has a great influence on the later casting and product quality.In order to predict the endpoint temperature of molten steel as accurately as possible,a prediction method of RH furnace endpoint temperature based on digital twin was proposed,and the accurate prediction of endpoint temperature was realized by digital twin model.Firstly,the endpoint temperature prediction virtual model was constructed by the Aquila optimizer-extreme learning machine(AO-ELM).According to the real-time steelmaking data obtained in the physical space,the initial prediction value was obtained by the AO-ELM model,and the twin database was updated at the same time.Then,similar smelting furnaces were found in the twin database through similarity search,and the predicted value and actual value under similar furnaces were compared,then the weighted error correction of the initial predicted value was carried out to obtain the final predicted value.The actual calculation results show that the proposed model is more accurate and re-liable than the traditional artificial intelligence endpoint temperature prediction model,and has good guiding signifi-cance for subsequent temperature control.
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