Synthetically predicting the quality index of sinter using machine learning model

Song, Lixin; Lyu, Qing; Liu, Xiaojie; Yan-qin, Sun

doi:10.1080/03019233.2019.1617573

Cited by 21 publications

(10 citation statements)

References 23 publications

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“…[ 60 ] Fan et al [ 28 ] also constructed a BP neural network model to predict four technical parameters: the mixture water, fuel ratio, sintering speed, and drum strength. Some other recent studies are Song et al [ 61 ] Chen et al [ 62 ] and Kumar et al [ 63 ]…”

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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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 accurate quality evaluation of wire rod will directly affect the quality continuous improvement. Among them, the comprehensive quality evaluation is the most comprehensive grasp of the overall product quality level [19]. Comprehensive Evaluation (CE), also called a multi-index comprehensive evaluation method, refers to a method that uses a more systematic and standardised method to simultaneously evaluate multiple indicators and multiple units.…”

Section: Comprehensive Quality Evaluationmentioning

confidence: 99%

Uniformity and comprehensive quality evaluation of steel product via process capability index and TOPSIS

Wang

Zhang

2021

Ironmaking & Steelmaking

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The overall quality of the steel wire is composed of the mechanical properties, the key process parameters, the geometrical parameters, surface quality, etc. A uniformity and comprehensive quality evaluation method of steel product based on the improved process capability index and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is proposed. First, the uniformity of the process parameters is described by the volume ratio between the process data area and the specification area which is calculated via kernel density estimation. The relative closeness of the newly added batches is obtained by TOPSIS, which is used as a comprehensive quality evaluation index. A time dimension is added to the radar chart to visualize high-dimensional data, which is intuitive to compare the differences between batches. The results show that the evaluation method can accurately obtain the uniformity and comprehensive quality of steel products.

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“…Iron ore sintering is a key process in the ironmaking and steelmaking industry, and it is an important raw material for blast furnace production. When the drum index of the sinter is low, it is easy to crush into small-size sinter ore, and the small size sinter ore will seriously affect the blast furnace output [1]. Therefore, the drum index is an important physical indicator of the quality of sinter.…”

Section: Introductionmentioning

confidence: 99%

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

Xia

Liu

et al. 2023

Ironmaking & Steelmaking

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Owing to the uncertainty operation in the sintering process, it is easy to produce uncertain prediction errors in the single drum index prediction model, which makes the prediction results lack certain reliability. Accurate and reliable prediction of the drum index can help improve the drum index. In this paper, a prediction interval estimation method of drum index based on a light gradient boosting machine (LightGBM) and kernel density estimation (KDE) is proposed. LightGBM can obtain accurate points prediction of drum index, and then use the KDE method to obtain the estimated prediction interval of drum index. The comparison results of different methods show that LightGBM has high prediction performance, and KDE can well quantify the prediction error of drum index, which verifies the effectiveness of the prediction interval estimation method combined with LightGBM and KDE, and provides more reliable decision-making information for the optimisation of sintering process parameters.

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Synthetically predicting the quality index of sinter using machine learning model

Cited by 21 publications

References 23 publications

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

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

Uniformity and comprehensive quality evaluation of steel product via process capability index and TOPSIS

Prediction interval estimation of sinter drum index based on light gradient boosting machine and kernel density estimation

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