Research on Sinter Quality Prediction System Based on Granger Causality Analysis and Stacking Integration Algorithm

Sinter ore quality directly affects the stability of blast furnace production. In terms of both physical and chemical properties, the main indicators of sinter quality are the TFe content, alkalinity, and drum index. By analyzing the massive historical data on the sinter production of a steel compan...

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Vydané v:Metals (Basel ) Ročník 13; číslo 2; s. 419
Hlavní autori: Li, Xin, Liu, Xiaojie, Li, Hongyang, Liu, Ran, Zhang, Zhifeng, Li, Hongwei, Lyu, Qing, Wen, Liangyixin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.02.2023
Predmet:
Algorithms
Alkalinity
Blast furnace chemistry
Blast furnace gas
Causality
Chemical properties
Coal gas
Correlation analysis
Data processing
Energy consumption
Gas pressure
Goodness of fit
Granger causality analysis
Ignition temperature
Indicators
Mathematical models
Missing data
Neural networks
Parameters
Prediction models
Predictions
Sintering
sintering quality
Stacking
stacking integration algorithm
China
ISSN:2075-4701, 2075-4701
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Shrnutí:Sinter ore quality directly affects the stability of blast furnace production. In terms of both physical and chemical properties, the main indicators of sinter quality are the TFe content, alkalinity, and drum index. By analyzing the massive historical data on the sinter production of a steel company, this study proposes a sinter quality prediction system based on Granger causality analysis and a stacking integration algorithm. First, based on real historical data of sintering production in steel enterprises (including coal gas pressure, ignition temperature, combustion air pressure, etc.), data preprocessing of raw data was carried out using a combination of feature engineering and the sintering process. Second, Pearson correlation analysis, Spearman correlation analysis, and Granger causality analysis were used to screen out the characteristic parameters with a strong influence on the target variable of sinter quality (drum Index, TFe, alkalinity). Third, a prediction model for sinter quality parameters was developed using a stacking integration algorithm pair for training. Finally, a program development tool was used to realize the establishment and online operation of a sinter ore quality prediction system. The test results showed that the predicted goodness of fit of the model for the TFe content, alkalinity (R), and drum index were 0.942, 0.958, and 0.987, respectively, and the model calculation time met the actual production requirements. By establishing a suitable model and running the program online, the real-time prediction of the main indicators of sinter quality was realized to guide production promptly.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2075-4701
2075-4701
DOI:10.3390/met13020419