A prior prediction of oxides of nitrogen, i.e., NO_X (sum of NO and NO₂), in diesel exhaust while treating with electric discharge-based nonthermal plasma (NTP) technique, would assist the researchers in planning the resources required for the treatment. In this present study, the performance of different regression-based models, i.e., linear, support vector regression and Gaussian process regression (GPR), has been analyzed for predicting the NO_X concentrations based on the values of five dominating parameters of the NTP treatment. Experiments have been conducted and collected a dataset of 4032 number of input–output pairs to be used for training and testing of the regression models. The performances of these models have been assessed while testing them for the unseen set of data. A comparison of root-mean-square error (RMSE) has been made, where Matern 3/2 type of GPR model has been found to be the best among all the considered models with an RMSE of 1.86 ppm for a test data of 1210 sets. The model is shown to perform consistently well even when the test data are increased to 50% of total data. Regression analysis shows that the NO_X can be predicted with very good accuracy using the Matern 3/2 type of GPR model.

中文翻译：

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基于回归的基于放电的处理预测柴油机排气中氮氧化物的模型

使用基于放电的非热等离子体（NTP）技术进行处理时，对柴油机排气中的氮氧化物（即NO _X（NO和NO _2的总和）进行预测）将有助于研究人员规划处理所需的资源。在本研究中，已经分析了不同的基于回归模型的性能，即线性，支持向量回归和高斯过程回归（GPR），以预测NO _X浓度基于NTP处理的五个主要参数的值。已经进行了实验，并收集了4032个输入-输出对的数据集，用于训练和测试回归模型。这些模型的性能已在测试它们的数据时得到了评估。进行了均方根误差（RMSE）的比较，发现在所有考虑的模型中，Matern 3/2类型的GPR模型是最好的，对于1210套测试数据，RMSE为1.86 ppm 。即使测试数据增加到总数据的50％，该模型也表现出一致的良好性能。回归分析表明，使用Matern 3/2型GPR模型可以非常准确地预测NO _X。