Determining the technological value of coal on the basis of coke-quality predictions

Stankevich, A. S.; Zolotukhin, Yu. A.

doi:10.3103/s1068364x1507008x

Cited by 14 publications

(3 citation statements)

References 12 publications

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“…Kumar et al (2008) reported that the vitrinite reflectance in the range V 9 -V 13 , a commonly used aggregate value, is an improved predictor of the measured Coke Strength after Reaction (CSR) than the mean maximum vitrinite reflectance. 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). However, none of these methods are adequate in capturing the modality of the distributions, shown in Fig.…”

Section: Vitrinite Reflectance and Coal Blending Decisionsmentioning

confidence: 99%

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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Section: Vitrinite Reflectance and Coal Blending Decisionsmentioning

confidence: 99%

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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show abstract

“…However, their performance is inadequate when the coal blend formula is changed or in different plants. [6][7][8] In addition to regression models, statistical analysis techniques are more advanced methods in application because they are capable of capturing complex correlations between coal properties and coke performance in blast furnace which includes CSR and CRI. The statistical analysis techniques include principal component analysis (PCA), partial least squares regression (PLS regression) and multi-variate adaptive regression splines (MARS).…”

Section: Introductionmentioning

confidence: 99%

“…However, their performance is inadequate when the coal blend formula is changed or in different plants. 6–8…”

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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Required Coke Quality Influence on a Coking Coal Mix Price Research. Linearization of a Coking Coal Procurement Optimization Model

Lipatnikov

Shnayder

2020

Lecture Notes in Electrical Engineering

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Determining the technological value of coal on the basis of coke-quality predictions

Cited by 14 publications

References 12 publications

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

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

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

Required Coke Quality Influence on a Coking Coal Mix Price Research. Linearization of a Coking Coal Procurement Optimization Model

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