Data mining – new perspectives on predicting coke quality in recovery stamp charged coke making process

Tiwari, H. P.; Haldar, S. K.; Roy, Abhijit Guha; Bhattacharjee, Arunabh; Mishra, Prakhar

doi:10.1051/metal/2015040

Cited by 13 publications

(4 citation statements)

References 4 publications

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“…Ash in coke can be guaranteed within the required range only if the ash in mixed coal is controlled, and the ash in coke is generally 1.3-1.4 times that in mixed coal. So the ash of mixed coal in general is controlled between 9% and 10% [1].…”

Section: Ash (A D ) Ash In Coal Remains In Coke After Cokingmentioning

confidence: 99%

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Application Study of Sigmoid Regularization Method in Coke Quality Prediction

Zhao

Liu

et al. 2020

Complexity

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Coke is an indispensable and vital flue for blast furnace smelting, during which it plays a key role as a reducing agent, heat source, and support skeleton. Models of prediction of coke quality based on ANN are established to map the functional relationship between quality parameters Mt, Ad, Vdaf, St,d, and caking property (X, Y, and G) of mixed coal and quality parameters Ad, St,d, coke reactivity index (CRI), and coke strength after reaction (CSR) of coke. A regularized network training method based on Sigmoid function is designed considering that redundancy of network structure may lead to the learning of undesired noise, in which weights having little impact on performance and leading to overfitting are removed in terms of computational complexity and training errors. The cascade forward neural network with validation is found to be the most suitable one for coke quality prediction, with errors around 5%, followed by feedforward neural network structure and radial basis neural networks. The cascade forward neural network may play a guiding role during the coke production.

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Section: Ash (A D ) Ash In Coal Remains In Coke After Cokingmentioning

confidence: 99%

“…With the growing trend of large-scale blast furnace, smelting effect of blast furnace and its economic and technical indicators are more deeply influenced by the quality and performance of coke [1]. Unfortunately, various types and proportions of mixed coal can lead to different coke quality [2].…”

Section: Introductionmentioning

confidence: 99%

Application Study of Sigmoid Regularization Method in Coke Quality Prediction

Zhao

Liu

et al. 2020

Complexity

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“…Metallurgical coke plays a key role in the blast furnace ironmaking process -it must maintain the permeability of the furnace so liquid iron may be drained from the furnace and combustion air injected into the base of the furnace (Riley 2007;Tiwari et al 2015). To maintain permeability the coke must maintain a good size distribution and shape while being subjected to mechanical loads, high temperatures, attack by oxidising gases and dissolution in the blast furnace liquids.…”

Section: Introductionmentioning

confidence: 99%

Classifying coke using CT scans and landmark multidimensional scaling

Nesbitt

Aziz

Mahoney

et al. 2023

Int J Coal Sci Technol

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One factor that limits development of fundamental research on the influence of coke microstructure on its strength is the difficulty in quantifying the way that microstructure is both classified and distributed in three dimensions. To support such fundamental studies, this study evaluated a novel volumetric approach for classifying small (approx. 450 μm3) blocks of coke microstructure from 3D computed tomography scans. An automated process for classifying microstructure blocks was described. It is based on Landmark Multi-Dimensional Scaling and uses the Bhattacharyya metric and k-means clustering. The approach was evaluated using 27 coke samples across a range of coke with different properties and reliably identified 6 ordered class of coke microstructure based on the distribution of voxel intensities associated with structural density. The lower class (1–2) subblocks tend to be dominated by pores and thin walls. Typically, there is an increase in wall thickness and reduced pore sizes in the higher classes. Inert features are also likely to be seen in higher classes (5–6). In general, this approach provides an efficient automated means for identifying the 3D spatial distribution of microstructure in CT scans of coke.

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“…This increase in prime hard coking coal is not an appropriate action for stamp charge coke oven batteries because it increases oven wall pressure and coal blend cost. Hence, many researchers have tried to efficiently use non-coking coal to reduce the coal blend cost without compromising the coke quality. − Several factors like the intrinsic properties of coal and weathering phenomena also affect coke quality . The literature reported that the poor quality of coke affects the ironmaking process mainly in two ways, viz., (i) the blast furnace operations team increases coke rate to meet the process requirements if the coke quality deteriorates significantly and (ii) the coke plant process team increases the PHCC coal composition in the coal blend, resulting in high blend cost to improve the coke quality.…”

Section: Introductionmentioning

confidence: 99%

Variability in Metallurgical Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR): An Experimental Study

et al. 2022

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The coke reactivity index and coke strength after reaction are critical parameters for the efficient operation of a blast furnace. Therefore, maintaining desired qualities of the produced coke as per coal blend chemistry and coke oven battery parameters is essential. However, the coke reactivity index (CRI) and coke strength after reaction (CSR) vary from laboratory to laboratory even though they have the same determination methodology. In the present investigation, a unique laboratory sample holder for reactivity test has been developed. The test method by Nippon Steel Corporation (NSC) has been compared with the newly developed sample holder method. The correlation between coke CRI and CSR has been studied with samples with a wide range of reactivity in the repeatability test. Results confirmed that the reactivity of coke highly depends on the reaction of individual coke pieces participating in the test. Despite undergoing strict process monitoring of the testing procedure of hot strength of coke, the present study confirmed that a variation of ±2 points in coke CSR and CRI does not affect coke quality in a single reading. The study also includes the influence of the number of coke pieces in the test sample to optimize the coke bed height. This paper described in detail the methodologies adopted, addressing the factors resulting in differences in CRI/CSR values within the same coke.

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Data mining – new perspectives on predicting coke quality in recovery stamp charged coke making process

Cited by 13 publications

References 4 publications

Application Study of Sigmoid Regularization Method in Coke Quality Prediction

Application Study of Sigmoid Regularization Method in Coke Quality Prediction

Classifying coke using CT scans and landmark multidimensional scaling

Variability in Metallurgical Coke Reactivity Index (CRI) and Coke Strength after Reaction (CSR): An Experimental Study

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