Multichannel profile-based monitoring method and its application in the basic oxygen furnace steelmaking process

Qian, Qingting; Fang, Xiaolei; Xu, Jun; Li, Min

doi:10.1016/j.jmsy.2021.09.010

Cited by 10 publications

(4 citation statements)

References 33 publications

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“…Gui et al [60] Pyrometallurgy • Deng et al [61] • D. Liu et al [28] • J. Liu et al [62] • Savic et al [63] • Ghea Puspita et al [64] • Cardoso et al [65] • Qian et al [66] • Cardoso et al [67] • Wang et al [68] • Yang et al [69] • Zhao et al [70] • RF: Random forest, EXS: expert system, FL: fuzzy logic, ANN: artificial neural network, CNN: convolutional neural network, MPC: model predictive control. • Olivier et al [39] • Estrada et al [29] •…”

Section: Application Of Soft Computing In Mineral Extraction and Proc...mentioning

confidence: 99%

“…Haar wavelet decomposition, PCA, and Mahalanobis distance with functional support vector data description (SVDD) are used by Qian et al [66] to predict the silicon content of hot metal. The results show that the proposed method outperforms other methods in the literature for silicon content prediction.…”

Section: Applications Of Soft Computing In the Pyrometallurgy Stagesmentioning

confidence: 99%

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Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal

Herrera,

Sinche Gonzalez,

Okkonen

et al. 2023

Minerals

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In the past two decades, the mining sector has increasingly embraced simulation and modelling techniques for decision-making processes. This adoption has facilitated enhanced process control and optimisation, enabling access to valuable data such as precise granulometry measurements, improved recovery rates, and the ability to forecast outcomes. Soft computing techniques, such as artificial neural networks and fuzzy algorithms, have emerged as viable alternatives to traditional statistical approaches, where the complex and non-linear nature of the mineral processing stages requires careful selection. This research examines the up-to-date use of soft computing techniques within the mining sector, with a specific emphasis on comminution, flotation, and pyrometallurgical and hydrometallurgical processes, and the selection of soft computing techniques and strategies for identifying key variables. From this, a soft computing approach is presented to enhance the monitoring and prediction accuracy for mineral waste disposal, specifically focusing on tailings and spent heap leaching spoils database treatment. However, the accessibility and quality of data are crucial for the long-term application of soft computing technology in the mining industry. Further research is needed to explore the full potential of soft computing techniques and to address specific challenges in mining and mineral processing.

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Section: Application Of Soft Computing In Mineral Extraction and Proc...mentioning

confidence: 99%

Section: Applications Of Soft Computing In the Pyrometallurgy Stagesmentioning

confidence: 99%

Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal

Herrera,

Sinche Gonzalez,

Okkonen

et al. 2023

Minerals

View full text Add to dashboard Cite

show abstract

“…Figure 4 reveals that static features have been predominantly employed for BOF data‐driven modeling. Despite the significant role of process time‐series features, their availability has been limited to a few BOF datasets [26–28] . In Brämming et al., [29] a comprehensive blend of all available features—process parameters, image, off‐gas data, and vessel vibration audiometry measurements—was utilized to estimate foam height and endpoint phosphorus prediction.…”

Section: Data‐driven Modeling Workflow For Bof Processmentioning

confidence: 99%

“…Addressing this gap, Qian et al. made significant strides in detecting the splashing phenomenon within the BOF process [27] . The authors proposed a novel method capable of converting irregularly gathered observations into smoothly differentiated functions, effectively highlighting the traits of splashing anomalies.…”

Section: Analysis Of ML Use Cases In Bofmentioning

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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Monitoring and diagnosis of complex production process based on free energy of Gaussian–Bernoulli restricted Boltzmann machine

Dong

Qian

et al. 2022

J. Iron Steel Res. Int.

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Multichannel profile-based monitoring method and its application in the basic oxygen furnace steelmaking process

Cited by 10 publications

References 33 publications

Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal

Soft Computing Application in Mining, Mineral Processing and Metallurgy with an Approach to Using It in Mineral Waste Disposal

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

Monitoring and diagnosis of complex production process based on free energy of Gaussian–Bernoulli restricted Boltzmann machine

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