A 3-D Convolution-Based Burn-Through Point Multistep Prediction Model for Sintering Process

Yan, Feng; Yang, Chunjie; Zhang, Xinmin; Gao, Liang

doi:10.1109/tie.2023.3279576

Cited by 9 publications

(2 citation statements)

References 37 publications

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“…Recently, Yan et al 102 have also proposed the CBMP model to capture the temporal features and spatial features simultaneously. In the CBMP model, a spatial–temporal recalibration block was developed to quantify the contributions of spatial–temporal features for fine-grained modeling.…”

Section: Review Of Soft Sensing Methods In Ironmaking Processmentioning

confidence: 99%

A Survey of Data-Driven Soft Sensing in Ironmaking System: Research Status and Opportunities

Yan,

Kong,

et al. 2024

ACS Omega

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Data-driven soft sensing modeling is becoming a powerful tool in the ironmaking process due to the rapid development of machine learning and data mining. Although various soft sensing techniques have been successfully used in both the sintering process and blast furnace, they have not been comprehensively reviewed. In this work, we provide an overview of recent advances on soft sensing in the ironmaking process, with a special focus on data-driven techniques. First, we present a general soft sensing development framework of the ironmaking process based on the mechanism analysis and process characteristics. Second, we provide a detailed taxonomy of current soft sensing methods categorized by their predictive tasks (i.e., quality indicators prediction, state parameters prediction, etc.). Finally, we outline several insightful and promising directions, such as self-supervised learning and digital twins in the ironmaking process, for future research.

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Section: Review Of Soft Sensing Methods In Ironmaking Processmentioning

confidence: 99%

A Survey of Data-Driven Soft Sensing in Ironmaking System: Research Status and Opportunities

Yan,

Kong,

et al. 2024

ACS Omega

Self Cite

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“…Based on the mechanistic analysis, researchers mitigated the effect of noise by adding a denoising gate to the GRU and then proposed a denoising spatial-temporal encoderdecoder multistep prediction model for prediction of the BTP in advance [112,113]. Yan et al [114] proposed a multistep prediction model for BTP using RNN combined with 3D convolution to simultaneously learn spatial-temporal features from low to high levels, which was also effective and accurate. Li et al [115] used long short-term memory and genetic algorithm-recurrent neural network (GA-RNN) to detect the chemical composition of sintered raw materials and established a GA-RNN based sinter quality prediction model to guide the sinter production process.…”

Section: Recurrent Neural Network Model Structure and Its Application...mentioning

confidence: 99%

Application of deep learning in iron ore sintering process: a review

Gong,

Wang,

et al. 2024

J. Iron Steel Res. Int.

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In the wake of the era of big data, the techniques of deep learning have become an essential research direction in the machine learning field and are beginning to be applied in the steel industry. The sintering process is an extremely complex industrial scene. As the main process of the blast furnace ironmaking industry, it has great economic value and environmental protection significance for iron and steel enterprises. It is also one of the fields where deep learning is still in the exploration stage. In order to explore the application prospects of deep learning techniques in iron ore sintering, a comprehensive summary and conclusion of deep learning models for intelligent sintering were presented after reviewing the sintering process and deep learning models in a large number of research literatures. Firstly, the mechanisms and characteristics of parameters in sintering processes were introduced and analysed in detail, and then, the development of iron ore sintering simulation techniques was introduced. Secondly, deep learning techniques were introduced, including commonly used models of deep learning and their applications. Thirdly, the current status of applications of various types of deep learning models in sintering processes was elaborated in detail from the aspects of prediction, controlling, and optimisation of key parameters. Generally speaking, deep learning models that could be more effectively implemented in more situations of the sintering and even steel industry chain will promote the intelligent development of the metallurgical industry.

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Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review

Du,

Ma,

Fan

et al. 2025

Computers in Industry

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A 3-D Convolution-Based Burn-Through Point Multistep Prediction Model for Sintering Process

Cited by 9 publications

References 37 publications

A Survey of Data-Driven Soft Sensing in Ironmaking System: Research Status and Opportunities

A Survey of Data-Driven Soft Sensing in Ironmaking System: Research Status and Opportunities

Application of deep learning in iron ore sintering process: a review

Intelligent prediction and soft-sensing of comprehensive production indicators for iron ore sintering: A review

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