Prediction of coke AMS through data mining: a practical approach

Kumar, Deepak; Saxena, V. K.; Tiwari, H. P.; Aleti, B. T.; Tiwary, Vijay Kumar

doi:10.1080/19392699.2020.1845662

International Journal of Coal Preparation and Utilization

2020

DOI: 10.1080/19392699.2020.1845662

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Prediction of coke AMS through data mining: a practical approach

Deepak Kumar

V. K. Saxena

H. P. Tiwari

et al.

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Cited by 2 publications

References 11 publications

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Optimization of Blast Furnace Ironmaking Using Machine Learning and Genetic Algorithms

Parihar,

Nistala,

Kumar

et al. 2024

steel research int.

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Blast furnace is a multiphase counter‐current packed bed reactor that converts iron‐bearing materials such as lumps, sinter, and pellets into hot metal using metallurgical coke and pulverized coal. The quality of input materials has a significant impact on furnace performance, hot metal quality and steel plant economics. It is difficult for operators to identify the optimal settings required for efficient and safe operation based on their experience alone, given the large number of furnace parameters. A multiobjective optimization problem for maximizing furnace productivity (PROD) and minimizing fuel rate (FR) with constraints on hot metal silicon (HMSi) and temperature (HMT) is formulated and solved using a genetic algorithm. Machine learning (ML) models are developed for PROD, FR, HMSi, and HMT and tested with data from an industrial blast furnace. Pareto‐optimal solutions along with optimal settings for key manipulated variables are obtained. It is demonstrated that PROD and FR can be improved by ≈3–5% at steady state. The overall ML model‐based optimization framework can be used as part of a blast furnace digital twin system to operate the furnace efficiently in real‐time for the given quality of raw materials.

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Optimization of Blast Furnace Ironmaking Using Machine Learning and Genetic Algorithms

Parihar,

Nistala,

Kumar

et al. 2024

steel research int.

View full text Add to dashboard Cite

show abstract

Post reactive strength and reactivity of metallurgical coke: infrared gas analyzer

Kumar

Saxena

Tiwari

et al. 2021

International Journal of Coal Preparation and Utilization

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Prediction of coke AMS through data mining: a practical approach

Cited by 2 publications

References 11 publications

Optimization of Blast Furnace Ironmaking Using Machine Learning and Genetic Algorithms

Optimization of Blast Furnace Ironmaking Using Machine Learning and Genetic Algorithms

Post reactive strength and reactivity of metallurgical coke: infrared gas analyzer

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