Polymorphic Measurement Method of FeO Content of Sinter Based on Heterogeneous Features of Infrared Thermal Images

Jiang, Zhaohui; Guo, Yuhao; Pan, Dong; Gui, Weihua; Maldague, Xavier

doi:10.1109/jsen.2021.3065942

Cited by 16 publications

(8 citation statements)

References 9 publications

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“…The prediction results show that the extracted multisource features reach a good performance and meet the needs of practical engineering. 60,61 Apart from chemical compositions, TS, as a critical metallurgical and intrinsic property, is also essential to the wear resistance and anticollision performance of the sintered ore. 62 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. 63 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.…”

Section: Soft Sensing Modeling Methods Based On Traditional Machine L...mentioning

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: Soft Sensing Modeling Methods Based On Traditional Machine L...mentioning

confidence: 99%

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

Yan,

Kong,

et al. 2024

ACS Omega

View full text Add to dashboard Cite

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“…Afterwards, a novel deep learning-based method based on heat transfer mechanism and infrared thermal image was proposed to realize the online prediction of the FeO content. [54,55] Apart from FeO content, Liu et al [31] adopted an LSTM network to predict the chemical composition such as TFe, FeO, V 2 O 5 , and CaO/SiO 2 . Further, a CNN based on the visual geometry group network (VGG16) model was developed to predict the basicity of an ore phase image.…”

Section: Prediction Of Quality Parametersmentioning

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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“…Usantiaga proposed a temperature measurement system for the sinter cooling process based on infrared thermal imaging technology [ 10 ]. Jiang combined the mechanism with the image characteristics acquired via the infrared imager and proposed a method for measuring the polymorph of FeO content in sinter based on the heterogeneous characteristics of infrared thermal images [ 11 ]. However, this method uses the fuzzy classification labels obtained via mechanism analysis and the label accuracy required via regression analysis is insufficient.…”

Section: Introductionmentioning

confidence: 99%

A Soft Sensor Model of Sintering Process Quality Index Based on Multi-Source Data Fusion

Jiang

Shi

et al. 2023

Sensors

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In complex industrial processes such as sintering, key quality variables are difficult to measure online and it takes a long time to obtain quality variables through offline testing. Moreover, due to the limitations of testing frequency, quality variable data are too scarce. To solve this problem, this paper proposes a sintering quality prediction model based on multi-source data fusion and introduces video data collected by industrial cameras. Firstly, video information of the end of the sintering machine is obtained via the keyframe extraction method based on the feature height. Secondly, using the shallow layer feature construction method based on sinter stratification and the deep layer feature extraction method based on ResNet, the feature information of the image is extracted at multi-scale of the deep layer and the shallow layer. Then, combining industrial time series data, a sintering quality soft sensor model based on multi-source data fusion is proposed, which makes full use of multi-source data from various sources. The experimental results show that the method effectively improves the accuracy of the sinter quality prediction model.

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Polymorphic Measurement Method of FeO Content of Sinter Based on Heterogeneous Features of Infrared Thermal Images

Cited by 16 publications

References 9 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

A Soft Sensor Model of Sintering Process Quality Index Based on Multi-Source Data Fusion

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