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.

介质阻挡放电是等离子体能量转换的重要方法。通过耦合不同的电源，可以通过各种操作参数轻松控制等离子体参数，以优化等离子体非氧化甲烷转化和等离子体催化氨合成。由于等离子体中反应的复杂性，将试错实验方法应用于多参数问题会消耗大量的资源和时间。当已知响应变化的原因时，多参数回归和确定独立性的筛选和稀疏算子可以合理预测影响因素与实验结果之间的变化关系，同时给出表达式，应用预测不同上升时间、脉冲宽度、频率和电压下等离子体非氧化甲烷转化。然而，等离子体能量转换中通常会添加催化剂。催化剂的特性由多个宏观和微观特性决定。如果对每个特征都进行拟合分析，会带来数据爆炸的问题，这在实验中是不可行的。因此，由于缺乏明确的特征量来表征等离子体催化氨合成中的催化作用，因此采用人工神经网络来解释不同催化剂的N _{2比例和气体温度的影响。}应用于不同问题的不同机器学习方法将加速等离子体能量转换中的参数优化。