A robust machine learning algorithm known as the least-squares support vector machine (LSSVM) model was used to predict the liquid densities of 48 different refrigerant systems. Hence, a massive dataset was gathered using the reports published previously. The proposed model was evaluated via various analyses. Based on the statistical analysis results, the actual values predicted by this model have high accuracy, and the calculated values of RMSE, MRE, STD, and R² were 0.0116, 0.158, 0.1070, and 0.999, respectively. Moreover, sensitivity analysis was done on the efficient input parameters, and it was found that CF₂H₂ has the most positive effect on the output parameter (with a relevancy factor of +50.19). Furthermore, for checking the real data accuracy, the technique of leverage was considered, the results of which revealed that most of the considered data are reliable. The power and accuracy of this simple model in predicting liquid densities of different refrigerant systems are high; therefore, it is an appropriate alternative for laboratory data.

中文翻译：

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使用最小二乘支持向量机方法预测制冷剂系统液体密度的综合建模

一种称为最小二乘支持向量机 (LSSVM) 模型的稳健机器学习算法用于预测 48 种不同制冷剂系统的液体密度。因此，使用之前发布的报告收集了大量数据集。通过各种分析评估了所提出的模型。根据统计分析结果，该模型预测的实际值具有较高的精度，计算得到的RMSE、MRE、STD和R ²值分别为0.0116、0.158、0.1070和0.999。此外，对有效输入参数进行敏感性分析，发现CF ₂ H ₂对输出参数有最积极的影响（相关因子为 +50.19）。此外，为了检查真实数据的准确性，考虑了杠杆技术，其结果表明，大多数考虑的数据都是可靠的。这种简单模型在预测不同制冷剂系统的液体密度方面的能力和准确性很高；因此，它是实验室数据的合适替代方案。