The increasing application of new ionic liquids (IL) creates the need of liquid-liquid equilibria data for both miscible and quasi-immiscible systems. In this study, equilibrium concentrations at different temperatures for ionic liquid+water two-phase systems were modeled using a Quantitative-Structure-Property Relationship (QSPR) method. Data on equilibrium concentrations were taken from the ILThermo Ionic Liquids database, curated and used to make models that predict the weight fraction of water in ionic liquid rich phase and ionic liquid in the aqueous phase as two separate properties. The major modeling challenge stems from the fact that each single IL is characterized by several data points, since equilibrium concentrations are temperature dependent. Thus, new approaches for the detection of potential data point outliers, testing set selection, and quality prediction have been developed. Training set comprised equilibrium concentration data for 67 and 68 ILs in case of water in IL and IL in water modeling, respectively. SiRMS, MOLMAPS, Rcdk and Chemaxon descriptors were used to build Random Forest models for both properties. Models were subjected to the Y-scrambling test for robustness assessment. The best models have also been validated using an external test set that is not part of the ILThermo database. A two-phase equilibrium diagram for one of the external test set IL is presented for better visualization of the results and potential derivation of tie lines.
机构:
Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou
State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, HangzhouInstitute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou
Qian J.
Li W.
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Institute of Process Equipment, College of Energy Engineering, Zhejiang University, HangzhouInstitute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou
Li W.
Li X.
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Institute of Process Equipment, College of Energy Engineering, Zhejiang University, HangzhouInstitute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou
Li X.
Ma Z.
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Institute of Process Equipment, College of Energy Engineering, Zhejiang University, HangzhouInstitute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou
Ma Z.
Jin Z.
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Institute of Process Equipment, College of Energy Engineering, Zhejiang University, HangzhouInstitute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou
Jin Z.
Gao Xiao Hua Xue Gong Cheng Xue Bao/Journal of Chemical Engineering of Chinese Universities,
2023,
37
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: 350
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