Metallurgical cokes, as fuel for blast furnaces, have certain properties which are directly related to their blended parental coal characters. The maximum fluidity (MF) of coal as an energy index is typically used to estimate the coke properties. In this investigation, Support Vector Regression (SVR), as an intelligent method, has been applied to link characteristics and pyrolysis properties of coal samples with their representative MFs. SVR variable importance measurement (VIM) through a wide range of coal properties indicated that volatile matter (VM) and maximum vitrinite reflectance (Rmax) are the most effective parameters for the MF prediction. The results indicated that low rank coal samples (VM>45% and Rmax>0.7) have log(MF) higher than 14 and high rank ones (VM<35% and Rmax<0.6) have log(MF) less than 4. The evaluation of the SVR model trained with these two selected input variables showed that SVR can predict MF quite accurately where the coefficient of determination (R 2) between actual MF and SVR predicted was 0.86. According to these results, generation of SVR models which can predict and measure variable importance dependently, potentially may be applied for the scaling up of laboratory coal thermoplastic behavior to industrial levels, helping to sustainable development, and satisfactorily estimating coal consumption in the steel-making plants.
|Number of pages||12|
|Journal||International Journal of Coal Preparation and Utilization|
|State||Published - Nov 1 2020|
Bibliographical notePublisher Copyright:
© 2017 Taylor & Francis.
- variable importance
ASJC Scopus subject areas
- Chemical Engineering (all)
- Fuel Technology
- Geotechnical Engineering and Engineering Geology
- Energy Engineering and Power Technology
- Mechanical Engineering