Novel method for temperature prediction in rotary kiln process through machine learning and CFD

Wang, Yaozu; Xu, Yue; Song, Xiaoran; Sun, Qingke; Zhang, Jianliang; Liu, Zhengjian

doi:10.1016/j.powtec.2024.119649

Powder Technology

2024

DOI: 10.1016/j.powtec.2024.119649

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Novel method for temperature prediction in rotary kiln process through machine learning and CFD

Yaozu Wang,

Yue Xu,

Xiaoran Song

et al.

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2024

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Cited by 3 publications

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Machine learning-based modeling and fogging prevention strategies for ice rink environments

Nisa,

Lee,

Kuan

2024

Building and Environment

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Machine learning-based modeling and fogging prevention strategies for ice rink environments

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Lee,

Kuan

2024

Building and Environment

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Computational and intelligence modeling analysis of pharmaceutical freeze drying for prediction of temperature in the process

Alqarni,

Alqarni

2024

Case Studies in Thermal Engineering

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Modeling and 3-D simulation of petroleum coke calcination process: investigation of the effect of main operating variables

Eghbal Ahmadi,

Tayyebi,

Mosayebi

2024

International Journal of Chemical Reactor Engineering

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Coke calcination is a process that involves heating green petroleum coke to purify it and eliminate volatile materials for subsequent processing. Due to the complexity of the rotary kiln used in this process, conducting experimental studies can be challenging and restricted. However, quantitative analyses based on developed models can provide a foundation for optimizing and controlling the process, which can significantly enhance the design efficiency. A three-dimensional simulation model of a rotary calcining kiln for petroleum coke was created using COMSOL Multiphysics in a steady-state mode. This model accounted for all relevant physical and chemical phenomena in the gas stream and coke bed flow, including heat transfer, combustion, and the evolution of volatile matter and coke dust. The mathematical modeling yielded distributions of temperature and mass fractions within the kiln, as well as the velocity field. The results revealed two distinct peak temperatures in the gas phase: 1,780 K near the primary air injection point and 1,605 K near the tertiary air injection points. The findings were analyzed, and the impact of key variables was explored. The simulation data indicated that for every decrease of 10–15 m/s in air flow, the gas peak temperature dropped by approximately 100°. Additionally, an increase in the input oxygen concentration led to enhanced combustion, resulting in higher peak concentrations of CO2. The developed simulation model has proven to be a valuable and promising tool for the design and optimization of petroleum coke rotary kilns.

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Novel method for temperature prediction in rotary kiln process through machine learning and CFD

Cited by 3 publications

References 32 publications

Machine learning-based modeling and fogging prevention strategies for ice rink environments

Machine learning-based modeling and fogging prevention strategies for ice rink environments

Computational and intelligence modeling analysis of pharmaceutical freeze drying for prediction of temperature in the process

Modeling and 3-D simulation of petroleum coke calcination process: investigation of the effect of main operating variables

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