IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v237y2021ics036054422101851x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422101851X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.121603?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Xuebin & Zhang, Jiaye & Xu, Xinwei & Mikulčić, Hrvoje & Li, Yan & Zhou, Yuegui & Tan, Houzhang, 2020. "Numerical study of biomass Co-firing under Oxy-MILD mode," Renewable Energy, Elsevier, vol. 146(C), pages 2566-2576.
    2. 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.
    3. Mohtaram, Soheil & Sun, HongGuang & Lin, Ji & Chen, Wen & Sun, Yonghui, 2020. "Multi-Objective Evolutionary Optimization & 4E analysis of a bulky combined cycle power plant by CO2/ CO/ NOx reduction and cost controlling targets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    4. Li, Dan & Jiang, Fuxin & Chen, Min & Qian, Tao, 2022. "Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks," Energy, Elsevier, vol. 238(PC).
    5. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    6. Hao Zhen & Dongxiao Niu & Min Yu & Keke Wang & Yi Liang & Xiaomin Xu, 2020. "A Hybrid Deep Learning Model and Comparison for Wind Power Forecasting Considering Temporal-Spatial Feature Extraction," Sustainability, MDPI, vol. 12(22), pages 1-24, November.
    7. Bao, Yu & Yu, Qingbo & Xie, Huaqing & Qin, Qin & Zhao, Yu, 2023. "Effect of H2 and CO in syngas on oxy-MILD combustion," Applied Energy, Elsevier, vol. 352(C).
    8. Aminmahalati, Alireza & Fazlali, Alireza & Safikhani, Hamed, 2021. "Multi-objective optimization of CO boiler combustion chamber in the RFCC unit using NSGA II algorithm," Energy, Elsevier, vol. 221(C).
    9. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Yuan, Ziting & Li, Chen & Shao, Shuai & Zhang, Jian, 2021. "New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory," Renewable Energy, Elsevier, vol. 179(C), pages 2174-2186.
    10. Hu, Fan & Xiong, Biao & Liu, Xuhui & Huang, Xiaohong & Li, Yu & Liu, Zhaohui, 2023. "Optimized TGA-based experimental method for studying intrinsic kinetics of coal char oxidation under moderate or intense low-oxygen dilution oxy-fuel conditions," Energy, Elsevier, vol. 265(C).
    11. Wang, Shuai & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2021. "A novel combined model for wind speed prediction – Combination of linear model, shallow neural networks, and deep learning approaches," Energy, Elsevier, vol. 234(C).
    12. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    13. Chengcheng Gu & Hua Li, 2022. "Review on Deep Learning Research and Applications in Wind and Wave Energy," Energies, MDPI, vol. 15(4), pages 1-19, February.
    14. Fan, Gaofeng & Chen, Meijing & Wang, Chang’an & Feng, Qinqin & Sun, Yunlei & Xu, Jie & Du, Yongbo & Che, Defu, 2024. "Numerical study on oxy-fuel combustion characteristics of industrial furnace firing coking dry gas," Energy, Elsevier, vol. 286(C).
    15. Rahbari, Alireza & Homayoonfar, Sajad & Valizadeh, Esmaeil & Aligoodarz, Mohammad Reza & Toghraie, Davood, 2021. "Effects of micro-combustor geometry and size on the heat transfer and combustion characteristics of premixed hydrogen/air flames," Energy, Elsevier, vol. 215(PA).
    16. Tian, Ye & Zhou, Xiong & Ji, Xuanyu & Bai, Jisong & Yuan, Liang, 2019. "Applying moderate or intense low-oxygen dilution combustion to a co-axial-jet I-shaped recuperative radiant tube for further performance enhancement," Energy, Elsevier, vol. 171(C), pages 149-160.
    17. Aly, Hamed H.H., 2022. "A Hybrid Optimized Model of Adaptive Neuro-Fuzzy Inference System, Recurrent Kalman Filter and Neuro-Wavelet for Wind Power Forecasting Driven by DFIG," Energy, Elsevier, vol. 239(PE).
    18. Tsao, Hao-Han & Leu, Yih-Guang & Chou, Li-Fen, 2021. "A center-of-concentrated-based prediction interval for wind power forecasting," Energy, Elsevier, vol. 237(C).
    19. Zhao, Xinyu & Bai, Mingliang & Yang, Xusheng & Liu, Jinfu & Yu, Daren & Chang, Juntao, 2021. "Short-term probabilistic predictions of wind multi-parameter based on one-dimensional convolutional neural network with attention mechanism and multivariate copula distribution estimation," Energy, Elsevier, vol. 234(C).
    20. Huang, Xiaohong & Hu, Fan & Liu, Xuhui & Liu, Zhaohui, 2022. "Structure and reactivity of chars prepared from low-volatile coal under O2/N2 and O2/CO2 conditions in a flat-flame assisted entrained flow reactor," Energy, Elsevier, vol. 261(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:237:y:2021:i:c:s036054422101851x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.