Prediction of Gray-King coke type from radical concentration and basic properties of coal blends

Xiang, Chong; Liu, Qingya; Shi, Lei; Zhou, Bin; Liu, Zhenyu

doi:10.1016/j.fuproc.2020.106584

Cited by 11 publications

(6 citation statements)

References 50 publications

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“…A good sample of coal for coking coal should have a high amount of carbon with a low amount of hydrogen, nitrogen, sulphur, and oxygen [13]. Low oxygen will increase the value of coke resistance or have a good CSR value [14]. Hydrogen will affect the volatile matter content, the degree of coal maturity, and affect the calorific value [13].…”

Section: 12mentioning

confidence: 99%

Characteristics and potential of coal for coking coal in Meliat Formation, Tarakan Basin, Nunukan, North Kalimantan, Indonesia

Kurniadi,

Rahmat,

Amijaya

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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Coking coal is coal that melts, expands, then reconsolidates during carbonization and can produce coke as an iron refining agent in blast furnaces. Characterization of the potential of coking coal in Indonesia is still being developed. The research was conducted to find out how the potential of coal in the Meliat Formation for coking coal. The purpose of this study was to determine the characteristics and potential of coking coal from coal has been mined at PT. Duta Tambang Rekayasa and it was included in that formation. The samples analyzed were 4 units and the sample was a blend of coal has been mined at seams E, F, and G. The result, all coal has high ash (> 10% adb), volatile matter (> 31% dmmf), sulphur (> 1% daf), H/C (0.88 - 1)(%daf), O/C (0.11 - 0.19)(%daf) and a low vitrinite reflectance (Rmax < 0.5), which indicates that the coal parameter does not match the requirements for coking coal. the low CSN test values (1 - 1.5) and GKCT (B and C type) are the keys to coal in this research including non-coking coal. This coal will produce poor quality coke because many substances will evaporate and do not have plastic properties.

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Section: 12mentioning

confidence: 99%

Characteristics and potential of coal for coking coal in Meliat Formation, Tarakan Basin, Nunukan, North Kalimantan, Indonesia

Kurniadi,

Rahmat,

Amijaya

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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“…To enhance robustness, this research utilised machine-learning algorithms to identify and classify coal samples into different types based on their characteristics. [17][18][19] This classification step helped in capturing the inherent differences between coal blends and improved the overall prediction performance. To improve accuracy, a combination of multi-output regression models and polynomial features has been employed.…”

Section: Introductionmentioning

confidence: 99%

DT-MNLR: a novel hybrid machine learning framework for precise coke strength and reactivity prediction

Wang,

Yuan,

Zhao

et al. 2024

Ironmaking & Steelmaking: Processes, Products and Applicati

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Accurately predicting coke strength after reaction (CSR) and coke reactivity index (CRI) is important for optimising coke quality in metallurgical industry, thereby minimising production costs and maximising resource utilisation. This study introduces a novel machine-learning model, the decision tree multi-output non-linear regression (DT-MNLR) model, for accurately predicting both CSR and CRI. The DT-MNLR model leverages the strengths of multiple algorithms: decision trees for efficient coal blend classification, multi-output regression for handling the interrelated nature of CSR and CRI, and a backpropagation neural network for capturing complex non-linear relationships within the data. Recognising the intricate interactions among coal properties that significantly impact coke quality, the model incorporates high-level polynomial features and additional coal property variables, enhancing its predictive accuracy. Rigorous validation using diverse testing samples demonstrates the DT-MNLR model's superior performance across a wide range of CSR and CRI values. Comparative analysis against other machine-learning methods showcases the DT-MNLR model's advantages, including lower prediction errors, improved generalisation to unseen data and enhanced robustness in handling outliers. This research significantly advances the field of coke quality prediction by establishing the DT-MNLR model as a powerful tool for coal blend analysis and quality control. The model's effectiveness paves the way for significant advancements in intelligent systems for industrial applications, promoting optimal resource utilisation and process efficiency.

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“…In general, the Gray–King coke method is used for the determination of the caking power . This indicator characterizes the caking power of coal by the type and characteristics of the nonvolatile residue (ISO 502:2015) .…”

Section: Introductionmentioning

confidence: 99%

“…15,16 In general, the Gray−King coke method is used for the determination of the caking power. 22 This indicator characterizes the caking power of coal by the type and characteristics of the nonvolatile residue (ISO 502:2015). 23 In addition, the free-swelling index (FSI) is also used, which is a measure of the increase in the coal volume when heated under certain conditions (ASTM D-720; ISO 335).…”

Section: Introductionmentioning

confidence: 99%

Technology of Producing Petroleum Coking Additives to Replace Coking Coal

et al. 2021

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Coke chemical companies often have a deficit of coals of particularly valuable grades, the coking coals. This work studies the opportunity of producing petroleum coking additives using delayed coking during heavy petroleum residue processing. Experiments for the production of a carbon material were conducted using three kinds of heavy petroleum residues of the oil refinery plant Ltd Kinef: the vacuum residue from crude atmospheric and vacuum distillation units (VR1), the vacuum residue from the vacuum distillation hydrocracking unit (VR2), and the visbreaker residue from the visbreaking unit (VR3). For the produced carbon material, the quality indicators were determined, and X-ray diffraction, thermogravimetric, and differential thermal analyses were conducted. The petroleum coking additive produced instead of the typical petroleum coke under a milder temperature regime had the required quality indicators, particularly, the volatile-matter yield within the range from 15 to 25 wt %, to be used in metallurgical production for partial replacement of coking coals in the charge to produce metallurgical coke.

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Prediction of Gray-King coke type from radical concentration and basic properties of coal blends

Cited by 11 publications

References 50 publications

Characteristics and potential of coal for coking coal in Meliat Formation, Tarakan Basin, Nunukan, North Kalimantan, Indonesia

Characteristics and potential of coal for coking coal in Meliat Formation, Tarakan Basin, Nunukan, North Kalimantan, Indonesia

DT-MNLR: a novel hybrid machine learning framework for precise coke strength and reactivity prediction

Technology of Producing Petroleum Coking Additives to Replace Coking Coal

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