Abstract
The accurate prediction of coke quality is important for the selection and valuation of metallurgical coals. Whilst many prediction models exist, they tend to perform poorly for coals beyond which the model was developed. Further, these models general fail to directly account for physical interactions occurring between the blend components, through the assumption that the aggregate properties of the blend are suitably representative of the overall behavior of the blend. To study this assumption, a parameter termed the vitrinite distribution category was introduced to directly account for the distribution of one of these commonly aggregated parameters, the vitrinite reflectance. The introduction of this parameter in a regression model for coke quality prediction improved the model fit. The vitrinite distribution category was demonstrated to provide new information about coal blending decisions, and was found to be capable of providing insight into the behavior of different blending structures. Residual analysis was applied to explore the behavior of the coke quality prediction model, with the vitrinite distribution category found to explain more than just the presence or absence of coals within a blend. This work provides the foundation of future studies in examining coal blending decisions, with the proposed parameter having the potential to be applied as part of a coke quality prediction model to optimize coal blending decisions.
Highlights
1.1 Coal, coke and the prediction of their propertiesMetallurgical coke, derived from the pyrolysis of selected coals, plays several critical roles in the ironmaking blast furnace (Babich and Senk 2013; Bertling 1999; Biswas1981)
Residual analysis was applied to explore the behavior of the coke quality prediction model, with the vitrinite distribution category found to explain more than just the presence or absence of coals within a blend
The following section firstly discusses the results of grouping the vitrinite reflectance distributions using the self-organizing map (SOM) algorithm, to produce the vitrinite distribution category (VDC), and the implications on improving regression quality
Summary
Metallurgical coke, derived from the pyrolysis of selected coals, plays several critical roles in the ironmaking blast furnace Many prediction models have been developed to estimate the strength of coke produced from blends of coals (Dıez et al 2002; North et al 2018a, b). The standard deviation or petrographic non-uniformity of the distribution is another attribute used within models, that relates to V-groups and blending decisions (Bulanov et al 2009; Stankevich and Bazegskiy 2013; Stankevich et al 2008; Stankevich and Zolotukhin 2015). In the case of a distinctly bimodal distribution, neither the Rv,max, subset sum, or standard deviation approaches are able to suitably
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal of Coal Science & Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.