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Dielectric Barrier Discharge Plasma-Enabled Energy Conversion Under Multiple Operating Parameters: Machine Learning Optimization

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Abstract

Dielectric barrier discharge is an important method in plasma-enabled energy conversion. By coupling different power sources, plasma parameters can be easily controlled by a variety of operating parameters to optimize plasma-enabled non-oxidative methane conversion and plasma-catalytic ammonia synthesis. Due to the complexity of the reactions in the plasma, the application of the trial-and-error experiment method to multi-parameter problems will consume a lot of resources and time. When the cause of the change in response can be known, multi-parameter regression and sure independence screening and sparsifying operator can reasonably predict the changing relationship between the influencing factors and the experimental results, and at the same time give the expression, which is applied to the prediction of plasma-enabled non-oxidative methane conversion under different rising times, pulse widths, frequencies, and voltages. However, catalysts are usually added in plasma energy conversion. The characteristics of catalysts are determined by multiple macro- and micro-characteristics. If fitting analysis is carried out for each feature, the problem of data explosion will be brought about, and this is not feasible in the experiment. Therefore, the artificial neural network is used to explain the influence of the N2 ratio and gas temperature of different catalysts due to the lack of clear characteristic quantity to characterize the catalytic action in plasma-catalytic ammonia synthesis. Different machine learning methods applied to different problems will accelerate the parameter optimization in plasma-enabled energy conversion.

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Acknowledgements

This work was supported in part by the National Science Fund for Distinguished Young Scholars (Grant No. 51925703), the National Natural Science Foundation of China (Grant Nos. 52077205, 52007178, and 52177164), and the Youth Innovation Promotion Association of CAS (Grant No. 2022136).

Funding

Tao Shao: National Science Fund for Distinguished Young Scholars (Grant No. 51925703). Shuai Zhang:  National Natural Science Foundation of China (Grant No. 52077205) and Youth Innovation Promotion Association of CAS (Grant No. 2022136). Xiucui Hu:  National Natural Science Foundation of China (Grant Nos. 52007178 and 52177164).

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Authors and Affiliations

  1. Beijing International S&T Cooperation Base for Plasma Science and Energy Conversion, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing, 100190, China

    Xin Zeng, Shuai Zhang, Xiucui Hu & Tao Shao

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Xin Zeng & Tao Shao

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  1. Xin Zeng
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XZ and SZ performed the investigation, and data analysis, and wrote the main manuscript. XH designed and set up the plasma reactor systems. TS performed result analysis and discussion. All authors reviewed the manuscript.

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Correspondence to Tao Shao.

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Zeng, X., Zhang, S., Hu, X. et al. Dielectric Barrier Discharge Plasma-Enabled Energy Conversion Under Multiple Operating Parameters: Machine Learning Optimization. Plasma Chem Plasma Process 44, 667–685 (2024). https://doi.org/10.1007/s11090-023-10434-8

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