Alkhatib Ismail I I, Albà Carlos G, Darwish Ahmad S, Llovell Fèlix, Vega Lourdes F
Research and Innovation Center on CO2 and Hydrogen (RICH), Khalifa University, PO Box 127788 Abu Dhabi, United Arab Emirates.
Chemical Engineering Department, Khalifa University, PO Box 127788 Abu Dhabi, United Arab Emirates.
Ind Eng Chem Res. 2022 Jun 1;61(21):7414-7429. doi: 10.1021/acs.iecr.2c00719. Epub 2022 May 18.
We present here a novel integrated approach employing machine learning algorithms for predicting thermophysical properties of fluids. The approach allows obtaining molecular parameters to be used in the polar soft-statistical associating fluid theory (SAFT) equation of state using molecular descriptors obtained from the conductor-like screening model for real solvents (COSMO-RS). The procedure is used for modeling 18 refrigerants including hydrofluorocarbons, hydrofluoroolefins, and hydrochlorofluoroolefins. The training dataset included six inputs obtained from COSMO-RS and five outputs from polar soft-SAFT parameters, with the accurate algorithm training ensured by its high statistical accuracy. The predicted molecular parameters were used in polar soft-SAFT for evaluating the thermophysical properties of the refrigerants such as density, vapor pressure, heat capacity, enthalpy of vaporization, and speed of sound. Predictions provided a good level of accuracy (AADs = 1.3-10.5%) compared to experimental data, and within a similar level of accuracy using parameters obtained from standard fitting procedures. Moreover, the predicted parameters provided a comparable level of predictive accuracy to parameters obtained from standard procedure when extended to modeling selected binary mixtures. The proposed approach enables bridging the gap in the data of thermodynamic properties of low global warming potential refrigerants, which hinders their technical evaluation and hence their final application.
我们在此展示一种新颖的综合方法，该方法采用机器学习算法来预测流体的热物理性质。这种方法能够利用从真实溶剂的导体类筛选模型（COSMO-RS）获得的分子描述符，获取用于极性软统计缔合流体理论（SAFT）状态方程的分子参数。该程序用于对18种制冷剂进行建模，包括氢氟烃、氢氟烯烃和氢氯氟烯烃。训练数据集包括从COSMO-RS获得的六个输入以及极性软SAFT参数的五个输出，其高统计精度确保了精确的算法训练。预测得到的分子参数被用于极性软SAFT中，以评估制冷剂的热物理性质，如密度、蒸气压、热容、汽化焓和声速。与实验数据相比，预测结果具有较高的准确度（平均绝对偏差 = 1.3 - 10.5%），并且与使用标准拟合程序获得的参数相比，准确度处于相似水平。此外，当扩展到对选定的二元混合物进行建模时，预测参数的预测准确度与从标准程序获得的参数相当。所提出的方法能够弥合低全球变暖潜能值制冷剂热力学性质数据方面的差距，而这一差距阻碍了它们的技术评估以及最终应用。
