Quality of Coke used in Blast Furnace Process – Analysis of Selected Parameters

Kardas, Edyta; Pustějovská, Pavlína

doi:10.2478/cqpi-2019-0081

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The blast furnace charge consists of three main raw materials: fuel, iron ore (sinter, pellets), and additives. The efficiency of the blast furnace process is affected by both the quality of the blast furnace coke and the quality of the ferrous burden materials [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Coke Substitution with Anthracite in Sintering Production

Pustejovska,

Sikora,

Jursova

et al. 2024

Processes

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This article summarises the possibility of replacing the coke breeze sintering fuel with an economically and ecologically more suitable fuel, anthracite. The main focus is on the possibility of replacing coke breeze with anthracite, during which, the replacement process is accelerated and the other properties are also affected. The analyses performed showed that the replacement of coke breeze with different amounts of anthracite does not have a negative effect if the initial permeability of the sintering bed is the same.

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Section: Introductionmentioning

confidence: 99%

Coke Substitution with Anthracite in Sintering Production

Pustejovska,

Sikora,

Jursova

et al. 2024

Processes

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“…The quality of coke has a significant impact on the efficiency and cost of metallurgical processes. 1 It is widely known that coke possesses sufficient strength to support the burden within the blast furnace in a suitable timeframe. To assess the performance of produced coke in this context, coke-making plants utilise two important indices: coke strength after reaction (CSR) and coke reactivity index (CRI).…”

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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