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A new skeletal mechanism for simulating MILD combustion optimized using Artificial Neural Network

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  • Si, Jicang
  • Wang, Guochang
  • Li, Pengfei
  • Mi, Jianchun

Abstract

This work develops a new skeletal mechanism of methane MILD combustion by a joint method of Directed Relation Graph (DRG), Computational Singular Perturbation (CSP) and Artificial Neural Network (ANN) (abbreviated as DRG-CSP-ANN method), where DRG and CSP are used for mechanism reduction and ANN for optimization. The detailed mechanism GRI-3.0, containing 53 species and 325 elementary reactions, is simplified to a skeletal mechanism with only 13 species and 35 reactions, named as Reduced-ANN. In addition, the mechanism reduced by DRG-CSP without ANN optimization, called Reduced-Ori, is also considered for comparison. Subsequently, the Reduced-ANN is validated by comparing its performance with those of other skeletal mechanisms, against that of GRI-3.0, in the auto-ignition time, one-dimensional premixed flame propagation speed and different computational-fluid-dynamics (CFD) simulations (i.e., CH4/H2 jet flame in hot coflow, premixed and non-premixed in-furnace MILD combustion). Results show that Reduced-ANN performs significantly better than all the other skeletal mechanisms including Reduced-Ori. For instance, the use of Reduced-ANN lessens the errors of predicting autoignition time and flame propagation speed from 7-18 % to 1.4 % and 16 % to 4 %, respectively. Therefore, the DRG-CSP-ANN method is qualified as a very promising way for mechanism reduction. In addition, the unsatisfying performance of Reduced-Ori demonstrates the necessity of mechanism optimization in reduction work, so that better predictions of specific quantities can be made to match those by the detailed mechanism.

Suggested Citation

  • Si, Jicang & Wang, Guochang & Li, Pengfei & Mi, Jianchun, 2021. "A new skeletal mechanism for simulating MILD combustion optimized using Artificial Neural Network," Energy, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:energy:v:237:y:2021:i:c:s036054422101851x
    DOI: 10.1016/j.energy.2021.121603
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    References listed on IDEAS

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    1. Jozaalizadeh, Toomaj & Toghraie, Davood, 2019. "Numerical investigation behavior of reacting flow for flameless oxidation technology of MILD combustion: Effect of fluctuating temperature of inlet co-flow," Energy, Elsevier, vol. 178(C), pages 530-537.
    2. Hu, Fan & Li, Pengfei & Guo, Junjun & Liu, Zhaohui & Wang, Lin & Mi, Jianchun & Dally, Bassam & Zheng, Chuguang, 2018. "Global reaction mechanisms for MILD oxy-combustion of methane," Energy, Elsevier, vol. 147(C), pages 839-857.
    3. Aly, Hamed H.H., 2020. "A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting," Energy, Elsevier, vol. 213(C).
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    Cited by:

    1. Chen, Mingfei & Zhou, Kaile & Liu, Dong, 2024. "Machine learning based technique for outlier detection and result prediction in combustion diagnostics," Energy, Elsevier, vol. 290(C).

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