Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM

Gu, Mao-qiang; Xu, Anjun; Wang, Hongbing; Wang, Zhitong

doi:10.3390/pr9111987

Cited by 19 publications

(8 citation statements)

References 15 publications

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“…Recently, there has been an increasing interest in incorporating novel deep learning (DL) methodologies such as transfer learning, [51] graph neural networks (GNNs), [52] CNNs, [46] auto‐encoder Bayesian network, [24] and reinforcement learning [36] . The application of recurrent neural networks (RNNs), specifically long short‐term memory (LSTM), has been explored for handling time‐series data [53] . A deep learning framework based on fully connected networks (FCN) and CNN has been developed for regression tasks, taking into account both static and multivariate time series information [28] …”

Section: Data‐driven Modeling Workflow For Bof Processmentioning

confidence: 99%

Toward learning steelmaking—A review on machine learning for basic oxygen furnace process

Ghalati,

Zhang,

El‐Fallah

et al. 2023

Materials Genome Engineering Advances

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Basic oxygen furnace (BOF) steelmaking is the most widely used process in global steel production today, accounting for around 70% of the industry's output. Due to the physical, mechanical, and chemical complexities involved in the process, conventional monitoring and control methods are often pushed to their limits. The increasing global competition has created a demand for new methods to monitor and control the BOF steelmaking process. Over the past decade, Machine Learning (ML) techniques have garnered substantial attention, offering a promising pathway to enhance efficiency and suitability of steel production. This paper presents the first comprehensive review of ML applications in the BOF steelmaking process. We provide an introduction to both fields: an overview of the BOF steelmaking process and ML. We analyze the existing work on ML applications in BOF steelmaking and synthesize common concepts into categories, supporting the identification of common use cases and approaches. This analysis concludes with the elaboration of challenges, potential solutions, and a future outlook for further research directions.

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Section: Data‐driven Modeling Workflow For Bof Processmentioning

confidence: 99%

Toward learning steelmaking—A review on machine learning for basic oxygen furnace process

Ghalati,

Zhang,

El‐Fallah

et al. 2023

Materials Genome Engineering Advances

View full text Add to dashboard Cite

show abstract

“…To quantify the performance of the models in predicting end-point P content, the mean absolute relative error (MARE) and root mean square error (RMSE) are implemented, which can be calculated by Equation ( 23) and (24).…”

Section: Model Accuracymentioning

confidence: 99%

“…About the second blowing time, the purpose of second blowing is to control the end-point C content and temperature. [24] The increase of second blowing time may result in the rephosphorization phenomenon due to the higher temperature, which is unfavorable for dephosphorization from the thermodynamic point of view. [25] As for the lime weight, the main component of lime is calcium oxide (CaO).…”

Section: Variable Importancementioning

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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“…Currently, new technologies in the field of artificial intelligence are being developed rapidly. [10] With progress in information electrification technology, the converter smelting manufacturing execution system (MES) formed by a variety of new sensors and monitoring systems can obtain the parameters of the converter smelting process in real time. [8,11] Thus, intelligent algorithms, such as machine learning and artificial neural networks, can be widely used in the data processing and parameter prediction of complex industrial systems.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Converter Tapping Weight Based on Principal Component Analysis–Whale Optimization Algorithm–Backpropagation Algorithm Neural Network Model

Xue,

Xu,

Bao

et al. 2024

steel research int.

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Tapping weight is an important parameter in converter blowing process, wherein precisely predicting the quantity of steel required in an alloy baking converter can effectively guide the requirement of alloy ingredients. In practical production, the main approach is empirical estimation, despite its low accuracy. Employing a general neural network model for prediction requires to gather the converter blowing parameters and endpoint temperature measurement sampling parameters as the model inputs. However, this data cannot be obtained until the blowing process reaches its endpoint, rendering it impractical for alloy batching that requires advance preparation for baking. In this study, a principal component analysis–whale optimization algorithm–backpropagation algorithm (PCA–WOA–BP) neural network tapping prediction model is developed using raw material parameters available before alloy baking as input. This model is integrated into the intelligent alloy reduction model of a factory. The model achieves a regression coefficient R2 of 0.823, with 98.53% of furnaces having a prediction error of less than 2 t. The tapping weight of 20 consecutive heats in actual production is predicted; the error range is less than 80% within 1 t, and the converter tapping weight is predicted accurately.

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Real-Time Dynamic Carbon Content Prediction Model for Second Blowing Stage in BOF Based on CBR and LSTM

Cited by 19 publications

References 15 publications

Toward learning steelmaking—A review on machine learning for basic oxygen furnace process

Toward learning steelmaking—A review on machine learning for basic oxygen furnace process

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

Prediction of Converter Tapping Weight Based on Principal Component Analysis–Whale Optimization Algorithm–Backpropagation Algorithm Neural Network Model

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