Prediction model of end-point phosphorus content for BOF based on monotone-constrained BP neural network

Zhou, Kaixiao; Lin, Wen-Hui; Sun, Jiankun; Zhang, Jiangshan; Zhang, Dezheng; Feng, Xiaoming; Liu, Qing

doi:10.1007/s42243-021-00655-6

Cited by 31 publications

(15 citation statements)

References 12 publications

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“…The reactions that occurred in the converter are very complex, and end-point phosphorus content is affected by numerous influential factors. Therefore, the 10 variables as shown in Table 4 were selected as inputs of the model by incorporating scholarly research [7,34] and SPSS data analysis. From the scatter plot in Figure 5, the proposed ESSA-DELM is performed closer to the ideal line (y=x) between the actual and predicted values, and with fewer points outside of the error range from -0.003% to 0.003%.…”

Section: Prediction Model Of End-point Phosphorus Content Based On Essa-delmmentioning

confidence: 99%

“…He et al [6] set up a principal component analysis (PCA)-BP model for end-point phosphorus content prediction. Zhu et al [7] established a Prediction model of end-point phosphorus content for BOF based on monotone-constrained BPNN. The above hybrid BPNN prediction model improves the prediction accuracy to some extent compared with traditional BPNN, but it is still insufficient.…”

Section: Introductionmentioning

confidence: 99%

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Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF

Quan¹,

Li²,

Cui³

et al. 2021

Preprint

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：An effective technology for predicting the end-point phosphorous content of basic oxygen furnace (BOF) can provide theoretical instruction to improve the quality of steel via controlling the hardness and toughness. Given the slightly inadequate prediction accuracy in the existing prediction model, a novel hybrid method was suggested to more accurately predict the end-point phosphorus content by integrating an enhanced sparrow search algorithm (ESSA) and a multi-strategy with a deep extreme learning machine (DELM) as ESSA-DELM in this study. To begin with, the input weights and hidden biases of DELM were randomly selected, resulting in that DELM inevitably had a set of non-optimal or unnecessary weights and biases. Therefore, the ESSA was used to optimize the DELM in this work. For the ESSA, the Trigonometric substitution mechanism and Cauchy mutation were introduced to avoid trapping in local optima and improve the global exploration capacity in SSA. Finally, to evaluate the prediction efficiency of ESSSA-DELM, the proposed model was tested on process data of the converter from the Baogang steel plant. The efficacy of ESSA-DELM was more superior to that of other DELM-based hybrid prediction models and conventional models. The result demonstrated that the hit rate of end-point phosphorus content within ±0.003%, ±0.002%, and ±0.001% was 91.67%, 83.33%, and 63.55%, respectively. The proposed ESSA-DELM model could possess better prediction accuracy compared with other models, which could guide field operations.

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Section: Prediction Model Of End-point Phosphorus Content Based On Essa-delmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF

Quan¹,

Li²,

Cui³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…[18] Since the artificial neural network can approach any continuous nonlinear mapping, with strong self-learning ability, the artificial neural network technology is applied to converter steelmaking end point prediction, as long as there are enough training samples, a good fitting effect can be achieved. [19][20][21] The model has greatly improved the processing ability of the complex nonlinear relationship among the large amount of data acquired by sensors in the process of steelmaking. He et al proposed a deep learning model based on principal component analysis and back propogation (BP) neural network to predict the phosphorus content of the basic oxygen furnace end point.…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network‐Based Method for Predicting Oxygen Content at the End Point of Converter

Wang

Bao

2022

steel research int.

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The main factor affecting the cleanliness of steel is inclusions, most of which are deoxidation products. The greater the oxygen content fluctuation at the converter's end point, the more unstable the inclusion control. A large amount of charging, operation, and sampling data is collected during the converter smelting, but it is not fully utilized, and the deoxidizing alloy addition still relies on manual experience. Herein, the representative convolutional neural network (CNN) of deep learning is adopted, and the number of convolutional layers, convolutional kernel size, and the number of convolutional kernels are optimized to establish the best prediction model of the converter end point. Its root mean square error, mean absolute error, and mean absolute percentage error are 35.29%, 25.59%, and 7.30%, respectively, which are superior to the back propagation neural network. This CNN prediction model uses 1300 sets of preprocessed data containing 23 indicator variables, of which 1200 sets of data are used for training and 100 sets for model validation. Within the ±50 ppm error scope, the model prediction hit rate can reach 87%, and when within the ±70 ppm error scope, the hit rate is 93%.

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“…One of the important links of converter steelmaking is to realize the accurate control of blowing end-point. Most converter steelmaking production mainly depends on manual experience or sub-lance detection to achieve end-point control in China [4,5]. Depending on the experience of production personnel to control composition and temperature, the accuracy of end-point hit is low.…”

Section: Introductionmentioning

confidence: 99%

Continuous Prediction Model of Carbon Content in 120 t Converter Blowing Process

Wang

Gao

Xing

et al. 2022

Metals

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A continuous prediction model of carbon content of 120 t BOF is established in this paper. Based on the three-stage decarburization theory and combined with the production process of 120 t converter, the effects of oxygen lance height and top blowing oxygen flow rate are also considered in the model. The explicit finite difference method is used to realize continuous prediction of carbon content in the converter blowing process. The model parameters such as ultimate carbon content in molten pool are calculated according to the actual data of 120 t BOF, which improves the hit rate of the model. Process verification and end-point verification for the continuous prediction model have been carried out, and the results of process verification indicate that the continuous prediction model established in the paper basically accords with the actual behavior of decarburization. Moreover, the hit ratio of the continuous prediction model reached 85% for the prediction of end-point carbon content within a tolerance of ±0.02%.

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Prediction model of end-point phosphorus content for BOF based on monotone-constrained BP neural network

Cited by 31 publications

References 12 publications

Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF

Using Enhanced Sparrow Search Algorithm-Deep Extreme Learning Machine Model to Forecast End-Point Phosphorus Content of BOF

Convolutional Neural Network‐Based Method for Predicting Oxygen Content at the End Point of Converter

Continuous Prediction Model of Carbon Content in 120 t Converter Blowing Process

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