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

Wang, Zhongliang; Bao, Yanping; Gu, Changzhi

doi:10.1002/srin.202370011

Cited by 5 publications

(4 citation statements)

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“…Wang et al. provided a detailed account of their use of a one‐dimensional convolutional neural network (CNN) and its network structure [46] …”

Section: Data‐driven Modeling Workflow For Bof Processmentioning

confidence: 99%

“…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] .…”

Section: Data‐driven Modeling Workflow For Bof Processmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Wang et al. provided a detailed account of their use of a one‐dimensional convolutional neural network (CNN) and its network structure [46] …”

Section: Data‐driven Modeling Workflow For Bof Processmentioning

confidence: 99%

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

“…[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. [12][13][14][15] The predicted values of the unknown parameters are obtained by inputting real-time data into a trained artificial neural network. One of the most widely used models is the feedforward neural network model trained using the backpropagation algorithm (BP).…”

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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“…Presently, most end-point prediction models are based on static data and employ various machine-learning approaches, including support vector regression, [9] case-based reasoning (CBR), [10] multilayer perceptron, [11] and ensemble models. [12] These models primarily aim to predict the carbon content and temperature at the converter's end point, [13] with phosphorus content prediction being secondary [14] ; research on predicting other elements, such as sulfur content, [15] is comparatively less developed. To improve prediction accuracy, some researchers employ principal component analysis, [16] metallurgical mechanism models, and other feature optimization techniques.…”

mentioning

confidence: 99%

End‐Point Prediction of Converter Steelmaking Based on Main Process Data

Kang,

Zhao,

et al. 2024

steel research int.

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In this article, main process data, notably time–series data such as lance position patterns, are analyzed during converter steelmaking, and methodologies in data processing and transforming are proposed. In this study, utilizing both the transformed key time–series and primary static process data, the influence of various process parameters on the end‐point parameters of converter steelmaking is analyzed. Furthermore, it establishes predictive models for the end‐point content of carbon (C) and phosphorus (P), as well as the end‐point temperature. In the findings, it is indicated that the end‐point carbon content and temperature are primarily influenced by the oxygen flow pattern, lime addition pattern, and key smelting parameters. The end‐point phosphorus content is mainly affected by the oxygen flow pattern, limestone addition pattern, and dolomite addition pattern. Regarding the prediction of end‐point carbon and phosphorus content, and end‐point temperature, compared to seven sub‐models, the hybrid model demonstrates an average accuracy improvement of 37.88%, 25.03%, and 31.51%, respectively, and the end‐point hit rate improves by 18.77%, 19.59%, and 20.41%, respectively.

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

Cited by 5 publications

References 0 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

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

End‐Point Prediction of Converter Steelmaking Based on Main Process Data

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