Estimation of coking indexes based on parental coal properties by variable importance measurement and boosted-support vector regression method. (March 2019)

• Record Type: Journal Article
• Authors: Hadavandi, E.; Chelgani, S. Chehreh
• Is Part Of: Measurement. Volume 135(2019)
• Page Start: 306
• Page End: 311
• Publication Date: 2019-03
• Subjects: Free swelling index -- Maximum fluidity -- Feature selection -- Mutual information -- Boosted-SVR; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.11.068
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Maximum fluidity (MF) and Free Swelling Index (FSI) are coking indexes. It is demanding to estimate MF and FSI via parental coal components. Mutual Information (MI) method as an intelligence tool can measure importance of variables. Boosted-SVR can used the most effective coal components for MF and FSI prediction. Boosted-SVR method could prediction MF and FSI quite satisfactory. Abstract: Coke as a fuel has a critical role for steel making industries. Since coke is a product of blended coals, it is essential to study relationships between parental coal components with quality of their coke products. Free swelling index (FSI) and maximum fluidity (MF) are standard coking indexes that widely used for blending coals and measuring quality of products. This study has been explored interdependencies between measured coal components by mutual information (MI) method and evaluated their importance in the prediction of coking indexes for a wide range of Illinois coal samples. MI results indicated that the set of moisture-organic sulfur and moisture-nitrogen-sulfate sulfur were the best variables for predictions of log(MF) and FSI, respectively. Adaptive Boosting method based on support vector regression (SVR), called Boosted-SVR, was used the selected variable sets for predictions of coking indexes. In testing stage of models, correlation of determination (R 2 ) between actual and predicted values for the log(MF) and FSI were 0.89 and 0.90, respectively. These results indicated that Boosted-SVR model could quite satisfactory predict coking indexes. In general, outcomes of this investigation demonstrated an appropriate potential of coking quality prediction with limited numbers of input variables and suggested that a combination of MI with Boosted-SVR model as a new powerful tool which can be used for the computation of other complex fuel and processing problems based on measurement of conventional properties.