Estimating the solubility of HFC/HFO in ionic liquids from molecular structure using machine learning method

Hydrofluorocarbon (HFC) + ionic liquid (IL) and hydrofluoroolefin (HFO) + IL are two new types of working pairs developed for absorption refrigeration system. In this paper, aiming to provide a guide for screening the optimal one from many candidates, a model based on group contribution (GC) method and artificial neuron network (ANN) is presented to estimating the solubility of HFC/HFO in ILs from molecular structure. The input variables of the ANN-GC model are temperature, pressure, and the number of various groups. A dataset containing 1693 solubility data for 18 HFC/HFO in ILs consisting of 10 cations and 17 anions at temperature from 273.13 K to 413.30 K and pressure from 0.99 kPa to 41,000 kPa were established to train the model. The ANN-GC model has great regression ability indicated by the average relative deviation of 8.8 % from experimental data. Besides, the case study on predicting [HMIM][BF4] - R134a working pairs solubility shows that our model has great prediction ability on new substances. The sensitivity analysis points out the groups influence on the solubility and give a guideline for designing high solubility working pairs. We also used Leverage approach to find the outlier data and high leverage data. (c) 2022 Institution of Chemical Engineers. Published by Elsevier Ltd. All rights reserved.