Abstract
The inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. The large time delay of the blast furnace iron making process was analyzed. Moreover, Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Aiming at the problem of low prediction accuracy of the ordinary neural network model, genetic algorithm was used to optimize parameters on the BP neural network model to improve the convergence speed of the model to achieve global optimization. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. The model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production.
Highlights
In the steel production process, the blast furnace provides high-quality hot metal for steelmaking through complicated processes such as discrete addition, continuous smelting, and discrete output. e control parameters of the smelting process have more than 100 and have the characteristics of high nonlinearity, randomness, and large time lag, so controlling the stable state of the furnace temperature is one of the keys to ensure the smooth progress of blast furnace ironmaking [1,2,3,4]
Production practice shows that the hot metal silicon content has a strong correlation with the temperature of the blast furnace, and it can be used to indirectly reflect the temperature change in the furnace [5,6,7,8]
Huang et al improved the accuracy of prediction of hot metal silicon content by combining principal component analysis with extreme learning machine and optimizing the weight and threshold using particle swarm optimization algorithm
Summary
Received 12 July 2021; Revised 28 July 2021; Accepted 2 August 2021; Published 18 August 2021. E inconsistency of the detection period of blast furnace data and the large time delay of key parameters make the prediction of the hot metal silicon content face huge challenges. Aiming at the problem that the hot metal silicon content is not consistent with the detection period of time series of multiple control parameters, the cubic spline interpolation fitting model was used to realize the data integration of multiple detection periods. Spearman analysis was combined with the weighted moving average method to optimize the data set of silicon content prediction. Combined with the autocorrelation analysis of the hot metal silicon content, a modified model for the prediction of hot metal silicon content based on error analysis was proposed to further improve the accuracy of the prediction. E model comprehensively considers problems such as data detection inconsistency, large time delay, and inaccuracy of prediction results. Its average absolute error is 0.05009, which can be used in actual production
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