Methods of coke quality prediction: A review

North, Lauren; Blackmore, Karen; Nesbitt, Keith; Mahoney, Merrick R.

doi:10.1016/j.fuel.2018.01.090

Cited by 43 publications

(16 citation statements)

References 83 publications

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“…Generally, in terms of coal properties, coke quality is primarily influenced by coal rank, coal type (reactive and inert macerals), and its inherent ability to soften, become plastic, and re-solidify into a coherent mass when heated [ 25 ]. As a general comment on model replication, they cannot be used beyond the conditions under which they were derived [ 27 ]. It is more acceptable to conclude the relations between coke structure and the properties.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Characterization Methods for Metallurgical Coke Structures

Zheng

Zhang

et al. 2021

Materials

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The structure of coke affects its reactivity and strength, which directly influences its performance in the blast furnace. This review divides coke structures into chemical structure, physical structure, and optical texture according to their relevant characteristics. The focuses of this review are the current characterization methods and research status of the coke structures. The chemical structures (element composition and functional group) can be characterized by elemental analysis, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy (Raman), X-ray photoelectron spectroscopy (XPS), and nuclear magnetic resonance imaging technology (13C NMR). The physical structures (pore structure and micro-crystallite structure) can be characterized by image method, X-ray CT imaging technique, mercury intrusion method, nitrogen gas adsorption method, X-ray diffraction method (XRD), and high-resolution transmission electron microscopy (HRTEM). The optical textures are usually divided and counted by a polarizing microscope. In the end, this review provides an idea of the construction of a coke molecular structural model, based on the above characterization. With the coke model, the evolution principles of the coke can be calculated and simulated. Hence, the coke performance can be predicted and optimized.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Review of Characterization Methods for Metallurgical Coke Structures

Zheng

Zhang

et al. 2021

Materials

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“…As a single statistical measure, such as mean, does not adequately capture the shape of vitrinite distributions, a novel parameter that allowed robust classification of this behavior was developed (North et al 2017). This parameter, the vitrinite distribution category (VDC), was derived using a data-mining algorithm known as a self-organizing map (SOM; Kohonen 1982).…”

Section: Background To Vitrinite Reflectance Distribution Analysismentioning

confidence: 99%

“…This method is shown schematically in Fig. 2 [for full details, see (North et al 2017)] and in brief, comprises the following steps:…”

Section: Background To Vitrinite Reflectance Distribution Analysismentioning

confidence: 99%

“…To generate the categories contained within the VDC parameter, only blends were considered. As a modification to the approach described by North et al (2017), additional new data was added to extend the training set. Data that had previously been filtered due to low measured CSR was also reintroduced.…”

Section: Application and Modifications Of Previously Described Approachmentioning

confidence: 99%

“…The regression terms defined in the previous work (North et al 2017) were also modified. The rank term that was previously used, R v,max , was replaced with the blend volatile matter on a dry, ash free basis (VM daf ).…”

Section: Application and Modifications Of Previously Described Approachmentioning

confidence: 99%

See 2 more Smart Citations

Understanding the impact of coal blending decisions on the prediction of coke quality: a data mining approach

North

Blackmore

Nesbitt

et al. 2018

Int J Coal Sci Technol

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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.

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Coke Making: Most Efficient Technologies for Greenhouse Emissions Abatement

Cavaliere

2019

Clean Ironmaking and Steelmaking Processes

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Methods of coke quality prediction: A review

Cited by 43 publications

References 83 publications

A Comprehensive Review of Characterization Methods for Metallurgical Coke Structures

A Comprehensive Review of Characterization Methods for Metallurgical Coke Structures

Understanding the impact of coal blending decisions on the prediction of coke quality: a data mining approach

Coke Making: Most Efficient Technologies for Greenhouse Emissions Abatement

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