A forecast model of the sinter tumble strength in iron ore fines sintering process

Gao, Qiuzhi; Wang, Hui; Pan, Xiangyang; Jiang, Xin; Zheng, Haiyan; Shen, Fengman

doi:10.1016/j.powtec.2021.05.063

Cited by 16 publications

(9 citation statements)

References 29 publications

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“…After the PDF is obtained, the error confidence intervals with confidence levels of 97.5%, 95%, 92.5% and 90% can be obtained according to formula (15), and then the prediction interval of drum index can be obtained from formula (18), and the evaluation metrics is used to evaluate the quality of the prediction interval. The evaluation metrics under different confidence levels are shown in Table 4.…”

Section: Prediction Interval Estimation Resultsmentioning

confidence: 99%

“…Li et al proposed an online sequential limit learning machine (OS-ELM) to predict the FeO content and drum index of sinter, optimised the sintering operation parameters, and reduced the fuel consumption of about 0.5 kg per ton of sinter [14]. Gao et al optimised the neural network model by introducing principal component analysis (PCA) and genetic algorithm (GA), and the prediction accuracy of drum index reached 95.1% [15]. Chen et al considered the characteristics of unbalanced data, the lack of marker samples and the coexistence of linear and non-linear components in the sintering process data, established a drum index prediction system under multiple working conditions and unbalanced data, and verified the effectiveness of the system through simulation [16].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Prediction Interval Estimation Resultsmentioning

confidence: 99%

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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“…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%

“…Further, a CNN based on the visual geometry group network (VGG16) model was developed to predict the basicity of an ore phase image. [56] For the prediction of sinter ore quality indicators, Gao et al [32] built an integrated model based on PCA and the genetic algorithm (GA) to predict the TS of sinter ore. Qiang et al [57] combined the artificial fish swarm algorithm and back propagation (BP) network to predict the TS. In the actual production process, the labelled samples are often not enough.…”

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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“…Sinter is a process of using low-grade iron ore to create high-quality manufactured iron ore, which is widely used in blast furnace ironmaking, and its quality directly determines the cost and efficiency of blast furnace production [1][2][3][4]. Its quality is closely related to microscopic mineral composition and mineral phase structure characteristics [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

A Quantitative and Optimization Model for Microstructure Uniformity of Sinter Based on Multiple Regression-NSGA2

Fang,

Li,

Liu

et al. 2024

Metals

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The degree of homogeneity of the sintered ore phase structure directly determines its quality index. A sinter ore quality evaluation method based on the quantification of the homogeneity of the mineral phase structure is proposed. First, the magnetite particle size characteristics in the ore phase structures with different degrees of homogeneity were summarized under a polarized light microscope, and a criterion for evaluating the uniformity of the sintered ore phase structure based on the magnetite content of different particle size grades was determined. Second, a multiple regression model was established for the raw material composition ratio of magnetite with varying particle size grades. Finally, the multiple regression model was optimized using the second-generation non-dominated sorting genetic algorithm (NSGA2). The results show that mineral phase structure analysis categorized the magnetite particle sizes into <30 μm, 30~60 μm, and >60 μm. The adjusted R2 of the multiple regression model of the chemical composition of raw materials and the proportion of magnetite of each particle size grade were all greater than 0.95, and the p values were all <0.05, indicating a high degree of model fitting. Using model analysis, the single factor and the interaction between the multiple factors that significantly influence the proportion of magnetite in the three particle size grades were determined. The multivariate regression model was optimized using the NSGA2 algorithm to determine the ratios of Al2O3 mass% = 1.82, MgO mass% = 1.50, and R(CaO mass%/SiO2 mass%) = 1.84 for the highest degree of uniformity of the sintered ores. Under this sintering condition, the micro-mineral phase structure became more homogeneous, confirming the model’s reliability.

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A forecast model of the sinter tumble strength in iron ore fines sintering process

Cited by 16 publications

References 29 publications

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

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

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

A Quantitative and Optimization Model for Microstructure Uniformity of Sinter Based on Multiple Regression-NSGA2

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