Laser-induced plasmas of materials containing hydrocarbons present strong carbon molecular emission features. Using these emissions to build models relating changes in spectral features to a physical parameter of the system, such as hydrocarbon content, can be difficult because of the dynamic complexity of the spectral features and temperature disequilibrium between molecular species. This study presents machine learning models trained to quantify the mole fraction of hexane in hexane–air plasmas from CN Violet and C2 Swan spectral features. Ensemble regression methods provide the most accurate predictions with root mean squared error on the order 10−2. Artificial neural network regressions produce predictions with superlative sensitivity, exhibiting detection limits as low as 0.008. These foundational models can be further refined with more advanced data to quantify the presence of carbon species in complex plasma environments, such as high-speed reacting flows.

中文翻译：

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机器学习辅助测定激光诱导己烷-空气等离子体分子发射中的 C6H14 摩尔分数

含碳氢化合物材料的激光诱导等离子体呈现出强烈的碳分子发射特征。由于光谱特征的动态复杂性和分子种类之间的温度不平衡，使用这些排放来建立将光谱特征的变化与系统的物理参数（例如碳氢化合物含量）相关的模型可能很困难。这项研究提出了经过训练的机器学习模型，用于量化来自 CN Violet 和 C 的己烷-空气等离子体中己烷的摩尔分数2天鹅光谱特征。集成回归方法提供最准确的预测，均方根误差约为 10−2。人工神经网络回归产生具有最高灵敏度的预测，检测限低至 0.008。这些基础模型可以使用更先进的数据进一步完善，以量化复杂等离子体环境（例如高速反应流）中碳物质的存在。