Hybrid Method for Endpoint Prediction in a Basic Oxygen Furnace

Wang, Ruibin; Mohanty, Itishree; Srivastava, A.; Roy, Tapas Kumar; Gupta, Prakash; Chattopadhyay, Kinnor

doi:10.3390/met12050801

Cited by 14 publications

(5 citation statements)

References 30 publications

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“…The optimal split node of the training sample can be determined by calculating the gain through formula (14), and the node value of the tree model can be calculated from formula (12), and the decision tree is constructed by using the strategy of growing leaf nodes, so as to obtain the leaf node value f t (x i ) of the decision tree, after K decision trees are obtained through K iterations, the prediction result of LightGBM model is obtained by substituting into Equation (2).…”

Section: Point Prediction Modelmentioning

confidence: 99%

“…In the current related research, the prediction models of sintering process parameters are mainly divided into a mechanism model and data-driven model. The mechanism model is usually established based on the heat and mass balance in the process, which mainly depends on the stability of static operating parameters and raw material characteristics [2]. Jaroslav et al established a simulation model of the sintering process under laboratory conditions by using thermodynamic software, predicted the thermodynamic conditions of carbonaceous fuel combustion in the sintering layer, and verified the results of the model in the laboratory sintering pot [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

Xia

Liu

et al. 2023

Ironmaking & Steelmaking

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Owing to the uncertainty operation in the sintering process, it is easy to produce uncertain prediction errors in the single drum index prediction model, which makes the prediction results lack certain reliability. Accurate and reliable prediction of the drum index can help improve the drum index. In this paper, a prediction interval estimation method of drum index based on a light gradient boosting machine (LightGBM) and kernel density estimation (KDE) is proposed. LightGBM can obtain accurate points prediction of drum index, and then use the KDE method to obtain the estimated prediction interval of drum index. The comparison results of different methods show that LightGBM has high prediction performance, and KDE can well quantify the prediction error of drum index, which verifies the effectiveness of the prediction interval estimation method combined with LightGBM and KDE, and provides more reliable decision-making information for the optimisation of sintering process parameters.

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Section: Point Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

Xia

Liu

et al. 2023

Ironmaking & Steelmaking

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show abstract

“…Scholars have made a lot of achievements in the research of converter static model and dynamic model. Martin 10 studied the application accuracy of the static model. The model was more accurate in predicting the equilibrium P content, but the accuracy was not enough in predicting the C content and temperature.…”

Section: Introductionmentioning

confidence: 99%

Research on prediction model of converter temperature and carbon content based on spectral feature extraction

Zhao,

et al. 2023

Sci Rep

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The flame of converter mouth can well reflect the change of temperature and composition of molten steel in the furnace. The flame characteristics of converter mouth collected by device can well predict the smelting process of converter. Based on the flame spectrum data set of converter mouth, this paper uses the BEADS algorithm and rough set attribute reduction algorithm optimized by genetic algorithm to extract the features of 2048-dimensional wavelength data. Through the model, eight indexes that contribute greatly to temperature and carbon content are selected, which are f-507, f-520, f-839, f-1073, f-1371, f-1528, f-1727 and f-1826. The MIC coefficients of the eight indicators with temperature and carbon content are calculated, and the MIC coefficients of the variables is small, and the selected indicators are representative. There was a significant correlation between temperature and C content. In BP neural network of temperature prediction model, it is found that the prediction accuracy of the training set is 0.99, the prediction accuracy of the test set is 0.99, the prediction accuracy of the verification set is 0.99, and the prediction accuracy of the whole set is 0.99. Through statistics, it is found that the hit rate of the temperature model in the range of ± 5 K is 88.7%, and the hit rate in the range of ± 10 K is 98.4%. and the RMSE parameter analysis shows that the average prediction error is 3.85 K. In BP neural network of carbon content prediction model, it is found that the prediction accuracy of the training set is 0.99, the prediction accuracy of the test set is 0.99, the prediction accuracy of the verification set is 0.98, and the prediction accuracy of the whole set is 0.99. Through statistics, it is found that the hit rate of the carbon contents model in the range of ± 0.05% is 94.0%, and the hit rate in the range of ± 0.10% is 98.3%, and the RMSE parameter analysis shows that the average prediction error is 0.021%. Finally, the universality of the model is verified by MIV algorithm.

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“…Wang et al established a hybrid method for the end‐point prediction of BOF. [ 6 ] This model could not only predict the end‐point P content but also forecast the end‐point C content and temperature. After applying the hybrid algorithm to improve the theoretical model and artificial neural networks (ANN), the performances of the models were improved.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Prediction of BOF End‐Point Phosphorus Content Among Machine Learning Models and Metallurgical Mechanism Model

Zhang

Yang

et al. 2022

steel research int.

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As a harmful element, phosphorus will cause cold brittleness that decreases the strength of steel. In order to ensure the mechanical properties of steel products, the phosphorus is generally removed from molten steel in BOF (basic oxygen furnace) steelmaking process. Therefore, the prediction of end-point P content in BOF is of great significance for steelmaking production.Several methods are usually employed to predict the end-point P content. The first one is using simple empirical formulas based on the previous production data. Through this method, the steelmaking workers can quickly predict the end-point P content on site and then promptly adjust the process parameters of BOF. However, the accuracy of this method is pretty low, and the prediction can only be used as a reference. Another method is the end-point P content prediction with the metallurgical mechanism from the perspective of dephosphorization kinetics. The dephosphorization kinetic model is established based on the coupled reaction model or the first principle. [1] The advantage of this method is that one can obtain not only the end-point P content but also the P content during the process. Although the metallurgical mechanism model (MMM) is with high accuracy, it takes as long as 20 min to complete the calculation for one heat, which cannot satisfy the quick steelmaking process. [2] With the progresses of computer equipment and artificial intelligence algorithms, the machine learning shows its outstanding capabilities in various fields in classification, regression, data mining, image recognition, and so on. The machine learning has also been applied in BOF end-point P content prediction for many years. He et al. proposed a prediction model based on the principal component analysis (PCA) and back propagation neural network (BPNN). [3] The results showed that the PCA-BPNN model had the highest prediction accuracy compared with the multiple linear regression (MLR) model and the normal BPNN model. Phull et al. and Li et al. used twin support vector machines (TWSVM) to classify the degrees of phosphorus removal (such as "High" and 'Low"). [4,5] The models achieved a high accuracy of 99.9% in classification, and the running time was shorter than the other models such as logistic regression and SVM radial basis. Wang et al. established a hybrid method for the end-point prediction of BOF. [6] This model could not only predict the end-point P content but also forecast the end-point C content and temperature. After applying the hybrid algorithm to improve the theoretical model and artificial neural networks (ANN), the performances of the models were improved. Moreover, many researchers applied different models to predict the end-point P content, such as membrane algorithm evolving extreme learning machine, [7] group method of data handling polynomial neural network, [8] and monotone-constrained BP neural network. [9]

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Hybrid Method for Endpoint Prediction in a Basic Oxygen Furnace

Cited by 14 publications

References 30 publications

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

Research on prediction model of converter temperature and carbon content based on spectral feature extraction

Comparison of the Prediction of BOF End‐Point Phosphorus Content Among Machine Learning Models and Metallurgical Mechanism Model

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