Soft-measuring models of thermal state in iron ore sintering process

Huang, Xiaoxian; Fan, Xiaohui; Chen, Xuling; Gan, Min; Zhao, Xinze

doi:10.1016/j.measurement.2018.07.095

Cited by 10 publications

(4 citation statements)

References 22 publications

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“…Oppositely, the delay of BTP may lead to insufficient combustion and affect the sintering quality. In view of this situation, the accurate BTP intelligent prediction has received many researchers’ interests . Some representative works are as follows.…”

Section: Review Of Soft Sensing Methods In Ironmaking Processmentioning

confidence: 99%

“…In view of this situation, the accurate BTP intelligent prediction has received many researchers’ interests. 67 Some representative works are as follows. Liu et al 68 used the gradient boosting decision tree (GBDT) algorithm and decision rules to predict BTP considering process knowledge and data characteristics dynamically.…”

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: Review Of Soft Sensing Methods In Ironmaking Processmentioning

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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“…With regard to other state parameters, Huang et al [ 69 ] first used the fuzzy clustering algorithm to analyze the production data and then established a prediction model of the bed permeability state via SVM. On the basis of this work, a soft‐sensing model coupled with feature extraction and comprehensive evaluation was established to reveal the thermal state of sinter ore. [ 70 ]…”

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

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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“…In terms of classification, we tested five models, including: logistic regression model, SVM model, decision tree model, extra tree classification model and artificial neural network model. In the regression prediction, five models of linear regression, SVM model, decision tree, extra tree regression model and artificial neural network were adopted [23][24][25][26][27][28]. In classification and regression prediction, we trained each model using the optimal features extracted from the feature selection analysis.…”

Section: Modelling Selectionmentioning

confidence: 99%

Synthetically predicting the quality index of sinter using machine learning model

Song

Lyu

Liu

et al. 2019

Ironmaking & Steelmaking

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A comprehensive prediction model of sinter quality based on machine learning algorithm was proposed. First of all, the mass historical data of actual sintering production was collected, cleaned and integrated. On this basis, the drum index and screening index of sinter were analysed by cluster analysis, and the quality of sinter was evaluated synthetically by clustering results and iron grade of sinter. Then, the important characteristic variables related to sinter quality index were screened by Recursive feature elimination, stability selection and random forest selection. The comprehensive classification model of sinter quality and the regression model of sinter's total iron content were established by using various machine learning algorithms. The results show that the prediction accuracy of the classification model and regression model established by the extra tree is the best, and the application effect of the model is verified by using the testing set. The F1-score of the comprehensive quality index classification model is 0.92 and the R 2 of the total iron content regression model is 0.882. This model has good learning and generalization ability, and the accurate prediction of sinter quality index is realized.

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Soft-measuring models of thermal state in iron ore sintering process

Cited by 10 publications

References 22 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

Synthetically predicting the quality index of sinter using machine learning model

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