In the present study, a backpropagation neural network combined with genetic algorithm (GA-BP) model was constructed for prediction the solubility of refrigerants in linear chained precursors of POE lubricants (PECs). A total of 2248 experimental solubility data of refrigerants in PECs reported in literature were collected with temperatures from 243.15 K to 363.15 K and pressures up to 10 MPa. The input variables of the model were optimized using non-dominated sorting genetic algorithm with elite strategy (NSGA-II). The optimized inputs include temperature, pressure, molecular weight, critical temperature, and acentric factor. Results indicate that the GA-BP model using the optimized inputs can correlate the solubility data with good accuracy, the average absolute relative deviation between calculated results from the model and the literature is 0.98 %. Moreover, in order to validate the predictive ability of the established GA-BP model, the solubility of R1243zf in PEC4 and PEC5 was measured at the temperature range from 278.15 K to 343.15 K. The calculated values from the GA-BP model were compared with the experimental data, and the average absolute relative deviation is 8.85 %. Finally, sensitivity analysis was performed by Partial Derivatives (PaD) method to assess the contribution of input variables. Leverage approach was used for outlier detection to ensure the robustness of the model.

中文翻译：

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基于反向传播神经网络结合遗传算法的PEC润滑油中制冷剂溶解度预测


在本研究中，构建了反向传播神经网络与遗传算法（GA-BP）相结合的模型，用于预测制冷剂在 POE 润滑剂（PEC）的线性链前体中的溶解度。共收集了文献报道的 2248 个制冷剂在 PEC 中的实验溶解度数据，温度范围为 243.15 K 至 363.15 K，压力高达 10 MPa。使用精英策略的非支配排序遗传算法（NSGA-II）对模型的输入变量进行优化。优化的输入包括温度、压力、分子量、临界温度和偏心因子。结果表明，使用优化输入的 GA-BP 模型可以以良好的精度关联溶解度数据，模型计算结果与文献的平均绝对相对偏差为 0.98%。此外，为了验证所建​​立的GA-BP模型的预测能力，在278.15 K至343.15 K的温度范围内测量了R1243zf在PEC4和PEC5中的溶解度。将GA-BP模型的计算值与实验数据，平均绝对相对偏差为 8.85%。最后，通过偏导数（PaD）方法进行敏感性分析，以评估输入变量的贡献。采用杠杆方法进行异常值检测，以保证模型的稳健性。