Abstract Accurate prediction of gas component viscosity is crucial in gas processing, heat and mass transfer and flow calculations, as well as gas reserves estimation. Many models have been proposed to predict the viscosity of gas components, but they have limited temperature and pressure ranges. Furthermore, machine learning-based methods have not been widely used, and it remains unclear if there are benefits from these approaches. In this study, we explore gas component viscosity prediction versus molecular weight, critical properties, acentric factor, normal boiling point, dipole moment, and temperature using 38 supervised machine learning algorithms. The algorithms are tested by using 4673 data sets for 1602 organic and inorganic gas components collected from the literature. In addition, we compare the outputs of the best predictive model with the viscosity models provided in the literature. The results show that the best performing algorithm is a hidden layer neural network model containing eight neurons with a Bayesian regularization algorithm as a training algorithm and tan-sigmoid and linear transfer functions. Examining the model with an experimental dataset, which has not been used in the model development process, shows that the performance of the proposed model has an absolute mean relative error of 7.2% from experimental measurements.

中文翻译：

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有监督机器学习技术在气体组分粘度多元建模中的应用：比较研究

摘要 气体组分粘度的准确预测在气体加工、传热传质和流量计算以及天然气储量估算中至关重要。已经提出了许多模型来预测气体组分的粘度，但它们的温度和压力范围有限。此外，基于机器学习的方法尚未得到广泛使用，目前尚不清楚这些方法是否有好处。在这项研究中，我们使用 38 种监督机器学习算法探索了气体组分粘度预测与分子量、临界特性、偏心因子、正常沸点、偶极矩和温度的关系。通过使用从文献中收集的 1602 种有机和无机气体成分的 4673 个数据集对算法进行了测试。此外，我们将最佳预测模型的输出与文献中提供的粘度模型进行比较。结果表明，性能最好的算法是一个包含 8 个神经元的隐藏层神经网络模型，以贝叶斯正则化算法作为训练算法，以及 tan-sigmoid 和线性传递函数。使用尚未在模型开发过程中使用的实验数据集检查模型，表明所提出模型的性能与实验测量的绝对平均相对误差为 7.2%。