Machine learning for design of thin-film nanocomposite membranes

In this study, a novel machine learning approach is proposed for estimation of the permeate flux and foulant rejection in nanocomposite filtration membranes. Nine independent variables are fed to artificial neural networks (ANNs) including support, nanoparticles concentration, concentration of organic phase trimesoyl chloride (TMC) in-n-hexane (TMC in n-hexane), operation pressure, contact angle, thin layer thickness, location of the nanoparticles (NPs), post-treatment temperature and duration, and permeate flux and foulant rejection were derived as the outputs of the ANNs. The proposed method was evaluated on two datasets across training, validation and test datasets, and an unseen dataset. 2250 different initial weights and number of the neurons in the hidden layer for the proposed ANN models were considered and compared to find the optimized ANN models. The mean squared error (MSE) and coefficient of determination (R2) were employed to select the best 20 ANN models for further analysis. The proposed ANN models resulted in accurate estimates for both permeate flux and foulant rejection with R2 of 0.9958 and 0.9412 in all data included in the training, validation and test datasets and R2 of 0.9938 and 0.9811 in unseen dataset, respectively. In addition, results of sensitivity analysis revealed that post treatment temperature and contact angle were found the most important input variables for estimation of permeate flux and foulant rejection. The proposed method can provide valuable insights for formulating permeate flux and foulant rejection and considering the effects of each experimental condition on nanocomposite filtration membranes without doing real experiments, which is time-consuming and expensive

Machine Learning for Advanced Design of Nanocomposite Ultrafiltration Membranes

Although the incorporation of nanoparticles into ultrafiltration polymeric membranes has shown promising outcomes, their commercial implementation has yet to be fulfilled due to inconsistency in data, lack of a reliable recipe for the optimum filler content, and reluctance in disrupting the production line which requires significant time and resources. There is a growing demand among membrane communities for a design platform that can accelerate the discovery of new nanocomposite membranes. In this work, a feed-forward ANN (artificial neural network) model that has one hidden layer and the Bayesian regularization training algorithm were chosen for designing a graphical user interface platform to predict the ultrafiltration nanocomposite membrane performance, that is, solute rejection, flux recovery, and pure water flux, thereby saving time and resources used in membrane design. Experimental data (735 samples from 200 reports published between 2006 and 2020) were derived from the literature for training, validation, and testing of the ANN models. The results indicated that the best 30 ANN models produce the most accurate estimation of membrane performance using the seven input variables of polymer concentration, polymer type, filler concentration, average filler size, solvent concentration (in the dope solution), solvent type, and contact angle on the unseen data set. Furthermore, a sensitivity analysis was performed on the achieved models to identify the most effective input variables for each nanocomposite membrane performance. This work has the potential to be extended to other mixed matrix membrane types that are going to be used for microfiltration, nanofiltration, reverse osmosis, and so forth