Research on a Method for Predicting Molten Steel Temperature Throughout the Entire Process of the Consteel Electric Arc Furnace

Molten steel temperature is a core indicator in Consteel arc furnace smelting. Its accurate prediction is crucial for enhancing control precision, optimizing processes, and reducing costs. However, the extreme conditions inside the furnace and the limited number of temperature measurements make it difficult to continuously and accurately predict molten steel temperature. This paper proposes a mechanism constrained LSTM prediction model tailored to electric arc furnace processes. By modifying the loss function to incorporate the temperature variation range derived from the mechanism model into network training, it mitigates the issue of insufficient temperature labels. Concurrently, The parameter ranges in the mechanistic model were optimized using an improved particle swarm optimization algorithm to obtain new parameter ranges. The mechanistic model with these new parameters was then employed to guide the network training. Experimental results demonstrate that the proposed optimization algorithm enhances prediction accuracy.