Tumble Strength Prediction for Sintering: Data-driven Modeling and Scheme Design

Ye, Jiale; Ding, Xuda; Chen, Cailian; Guan, Xinping; Xianchang, Cao

doi:10.1109/cac51589.2020.9326800

Cited by 8 publications

(16 citation statements)

References 14 publications

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“…Jiang et al 61 Polymorphic measurement method of FeO content of sinter based on heterogeneous features of infrared thermal images. Ye et al 64 TS model combined with a local thermal nonequilibrium (LTNE) model for tumble strength prediction. Chen et al 65 Semisupervised just-in-time learning framework using a Gaussian mixture model (GMM).…”

Section: Soft Sensing Methods Of State Parametersmentioning

confidence: 99%

“…In general, high strength sinter ore helps to reduce the industry dust output dosage and improve efficiency of the blast furnace, while too low strength can affect the permeability of material surface . Under this background, Ye et al developed a TS estimation method based on LSSVM and local thermal nonequilibrium (LTNE) model, and the proposed scheme was verified in the sinter pot tests . In addition, the testing time of TS takes several hours, making the labeled samples scarce.…”

Section: Review Of Soft Sensing Methods In Ironmaking Processmentioning

confidence: 99%

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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: Soft Sensing Methods Of State Parametersmentioning

confidence: 99%

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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“…In order to solve this problem, a semi‐supervised prediction system was devised for the prediction of the TS. [ 58 ] Besides, to address the time‐tag matching problem, Ye et al [ 33 ] proposed a TS model combined with a local thermal non‐equilibrium (LTNE) model, considering the unequal distribution in the sintering bed and the delay in the measuring process.…”

Section: Review Of Data‐driven Methods In the Sintering Processmentioning

confidence: 99%

“…Li et al [30] Dynamic time feature expanding and extracting framework for FeO content prediction Liu et al [31] LSTM network for the chemical composition prediction Gao et al [32] Integrated model combining PCA with GA for tumble strength prediction Ye et al [33] TS model combined with a local thermal non-equilibrium (LTNE) model for tumble strength prediction Du et al [34] Fuzzy time series model for BTP prediction Yan et al [27] Denoising spatial-temporal encoder-decoder network for BTP prediction Chen et al [35] Hybrid just-in-time learning soft sensor (HJITL-SS) for CCR prediction Hu et al [36] Customized kernel-based Fuzzy C-Means (CKFCM) clustering method for CCR prediction Control Du et al [37] A fuzzy controller using the Mann-Kendal for BTP control Wang and Wu [38] A two-level hierarchical intelligent control system for BTP control Chen et al [39] Takagi-Sugeno (T-S) fuzzy model for BTP control Ying et al [40] Proportional-integral-derivative (PID) neural network for ignition temperature control Cao et al [41] An expert control system for ignition temperature control Optimization Zhou et al [42] Multi-objective and multi-time-scale optimization model for CCR Huang et al [43] A low-carbon and low-cost blending scheme for reducing the energy consumption Hu et al [44] An online optimization model for CCR Wu et al [45] An intelligent integrated optimization system (IIOS) for proportioning Wang et al [46] Cascade multi-objective optimization model (CMOM) for proportioning Abbreviations: BTP, burn-through point; CCR, comprehensive carbon ratio; GA, genetic algorithm; LSTM, long short-term memory; PCA, principal component analysis.…”

Section: Predictionmentioning

confidence: 99%

Data‐driven modelling methods in sintering process: Current research status and perspectives

et al. 2022

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The sintering process, as a primary modus of the blast furnace ironmaking industry, has enormous economic value and environmental protection significance for the iron and steel enterprises. Recently, with the emergence of artificial intelligence and big data, data‐driven modelling methods in the sintering process have increasingly received the researchers' attention. But now, there is still no systematic review of the data‐driven modelling approaches in the sintering process. Therefore, in this article, we conduct a comprehensive overview and prospects on the data‐driven models for the purpose of intelligent sintering. First, the mechanism and characteristics of the sintering process are introduced and analyzed elaborately. Second, the detailed research status of the sintering process is illustrated from four aspects: key parameters prediction, control, optimization, and others. Finally, several challenges and promising modelling methods such as deep learning in the sintering process are outlined and discussed for future research.

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“…A fuzzy time series modelling method based on fuzzy c-mean clustering was proposed to accurately predict BTP Ye et al[65] A data-driven prediction of tumble strength based on local thermal non-equilibrium (LTNE) model is proposed to solve problem of uncertainty in thermochemical reaction equations of sintered beds…”

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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Tumble Strength Prediction for Sintering: Data-driven Modeling and Scheme Design

Cited by 8 publications

References 14 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

Data‐driven modelling methods in sintering process: Current research status and perspectives

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

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