In this manuscript, we consider the accuracy of end-point carbon content prediction affected by oxygen injection in multiple stages of electric arc furnace (EAF) melting process. Such a prediction would help to further evaluate process control strategies and optimize overall operation of the electric arc furnace. Principal component analysis (PCA) was used to normalize the 13 input variables affecting the endpoint carbon content. log-sigmoid and tan-sigmoid functions were used to verify the same sample, and it was found that the Mean squared error(MSE) of the model under logsig + logsig function was smaller, indicating that the model was more stable. At the same time, different hidden layer nodes were tried, and finally the structure of the model was determined as 13 × 10 × 8 × 1, and the activation function was logsig + logsig. Using historical smelting data to train and test the neural network model, the correlation coefficient (R) of the verified model is 0.7632, the model prediction is in the range of ±0.03%, the hit rate of the model is 64.5%, and the hit rate of the model is 42% in the range of ±0.02%. Combining the verification basis of the model with the metallurgical reaction principle of the EAF steelmaking process, a pretreatment method of phased input of total oxygen is proposed. The oxygen is divided into three stages, which are the oxygen consumption volume of 0–5 min, 5–30 min and more than 30 min and other variables are kept unchanged. The same neural network is used to train and verify the same data. After verification, the R of the oxygen staged model is 0.8274. The model prediction is in the range of ±0.03%, the hit rate of the model is 78.5%, and the hit rate of the model is 58% in the range of ±0.02%. Finally, an on-line carbon content prediction system based on artificial neural network model is developed and applied to actual production. Running results illustrated that the hit rate of end-point carbon content is 96.67%, 93.33% and 86.67%, respectively when the prediction errors are within ±0.05%, ±0.03% and ±0.01%, the improved neural network model can effectively predict the end-point carbon content, which provides a good basis for the carbon content at the end point of EAF steelmaking process.
A novel direct-current electric arc furnace (DC-EAF) was designed and constructed in this study for experimentally investigating high-titanium slag smelting, with an emphasis on addressing the issues of incomplete separation of metal and slag as well as poor insulation effects. The mechanical components (crucible, electrode, furnace lining, etc.) were designed and developed, and an embedded crucible design was adopted to promote metal-slag separation. The lining and bottom thicknesses of the furnace were determined via calculation using the heat balance equations, which improved the thermal insulation. To monitor the DC-EAF electrical parameters, suitable software was developed. For evaluating the performance of the furnace, a series of tests were run to determine the optimal coke addition under the conditions of constant temperature (1607 °C) and melting time (90 min). The results demonstrated that for 12 kg of titanium-containing metallized pellets, 4% coke was the most effective for enrichment of TiO2 in the high-titanium slag, with the TiO2 content reaching 93.34%. Moreover, the DC-EAF met the design requirements pertaining to lining thickness and facilitated metal-slag separation, showing satisfactory performance during experiments.
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