Prediction of Temperature-Dependent Henry’s Law Constants by Matrix Completion

Methods for predicting Henry’s law constants Hij describing the solubility
of solutes i in solvents j as a
function of temperature
are essential in chemical engineering. While isothermal properties
of binary mixtures can conveniently be predicted with matrix completion
methods (MCMs) from machine learning, we advance their application
to the temperature-dependent prediction of Hij in the present work by combining them with
physical equations describing the temperature dependence. For training
the methods, experimental Hij data for 122 solutes and 399 solvents ranging from 173.15
to 573.15 K were taken from the Dortmund Data Bank. Two MCMs are proposed:
a data-driven MCM that relies solely on experimental data and a hybrid
MCM that incorporates predictions from the established Predictive
Soave-Redlich-Kwong (PSRK) equation of state (EoS), effectively combining
physical knowledge and machine learning. The performance of these
MCMs is assessed via leave-one-out analysis and compared to that of
the PSRK-EoS, demonstrating superior prediction accuracy.