Recent development in machine learning of polymer membranes for liquid separation

Abstract

We summarize the recent development in machine learning studies of polymer membranes for liquid separation and suggest directions for future exploration.

Progressing thin-film membrane designs for post-combustion CO2capture: performance or practicality?

“…In fact, ML has already found numerous preliminary applications in membrane gas separation, such as predicting the performance of MOF-based MMMs or that of a number of different polymer building blocks, which are enabled through the large database available for these materials. [204][205][206] The many material-selection and design criteria discussed as well as the various potential research areas suggested in this entire section are summarized in Fig. 9.…”

Design of Modified Polymer Membranes Using Machine Learning

“…26 When validated with experimental data, the applied machine-learning models often showed excellent accuracy and predictive capability. 17,27 In this study, we applied machine learning methods to data sets containing performance parameters of electron beam modified membranes. This method can be used to graft organic molecules on membranes to improve their performance.…”

“…One possibility to tackle the complexity of similar issues is the application of machine learning. , Machine learning is a type of artificial intelligence that enables computers to learn patterns from data and subsequently make decisions based on these patterns . To do this, machine learning approaches usually use large data sets.…”

Machine Learning-Assisted Design of Thin-Film Composite Membranes for Solvent Recovery

“…As a transformative technology, ML has been increasingly applied to the discovery of new materials. In the field of membranes, it has been increasingly used for gas separation, water treatment, biofuel purification, , and other liquid separation . Nevertheless, only a few ML studies were reported on OSN, specifically, to predict solvent permeances in a polydimethylsiloxane membrane and solute rejections in PuraMem S600 and PuraMem 280 membranes, to evaluate the OSN performance of commercial and polyimide membranes, to predict the rejections of more than 400 solutes in three polyamide membranes, to optimize TFC membranes for OSN, to examine solvent impact on solute rejection, and to predict the separation of complex organic mixtures in linear polymer membranes .…”

“…In the field of membranes, it has been increasingly used for gas separation, 12 water treatment, 13 biofuel purification, 14,15 and other liquid separation. 16 Nevertheless, only a few ML studies were reported on OSN, specifically, to predict solvent permeances in a polydimethylsiloxane membrane 17 and solute rejections in PuraMem S600 and PuraMem 280 membranes, 18 to evaluate the OSN performance of commercial and polyimide membranes, 19 to predict the rejections of more than 400 solutes in three polyamide membranes, 20 to optimize TFC membranes for OSN, 21 to examine solvent impact on solute rejection, 22 and to predict the separation of complex organic mixtures in linear polymer membranes. 23 Most of these ML studies primarily investigated the effects of operating conditions as well as solvent and solute properties on OSN performance in a handful of membranes, and they could not be applied to design new membranes or predict OSN performance of membranes with new structures because the chemical structures of membranes were not considered.…”