Research on The Prediction and Optimization of Blast Furnace Molten Iron Temperature Based on LSTM Long and Short-Term Neural Memory Network
DOI:
https://doi.org/10.5281/zenodo.13147689ARK:
https://n2t.net/ark:/40704/JIEAS.v2n4a21Keywords:
Iron and Steel industry, Green, OLS Regression Model, LSTM Long Short-term Memory Neural Network, PSO Particle Swarm Optimization Algorithm, Blast Furnace Molten Iron Temperature Prediction, Fuel RatioAbstract
The modern steel industry pursues green, low-consumption and high-quality development, and the prediction of fuel ratio and molten iron temperature is the key. However, the traditional prediction methods are limited, and the information age requires technological updates. In this paper, OLS regression, LSTM and PSO algorithms are used to establish and optimize the blast furnace molten iron temperature prediction model. First, analyze the literature and preprocess the data. Secondly, reduce dimension processing and select key variables. Then, construct the LSTM model to predict the temperature and evaluate the effect. Next, optimize the model with PSO and evaluate again. Finally, summarize the model, the prediction accuracy is high, especially the optimized model is significantly improve the prediction performance, but still need to be combined with the actual production of real-time detection and adjustment.
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