The roles of artificial intelligence techniques for increasing the prediction performance of important parameters and their optimization in membrane processes: A systematic review

Membrane-based separation processes has been recently of significant global interest compared to other conventional separation approaches due to possessing undeniable advantages like superior performance, environmentally-benign nature and simplicity of application. Computational simulation of fluids has shown its undeniable role in modeling and simulation of numerous physical/chemical phenomena including chemical engineering, chemical reaction, aerodynamics, drug delivery and plasma physics. Definition of fluids can be occurred using the Navier-Stokes equations, but solving the equations remains an important challenge. In membrane-based separation processes, true perception of fluid's manner through disparate membrane modules is an important concern, which has been significantly limited applying numerical/computational procedures such s computational fluid dynamics (CFD). Despite this noteworthy advantage, the optimization of membrane processes using CFD is time-consuming and expensive. Therefore, combination of artificial intelligence (AI) and CFD can result in the creation of a promising hybrid model to accurately predict the model results and appropriately optimize membrane processes and phase separation. This paper aims to provide a comprehensive overview about the advantages of commonly-employed ML-based techniques in combination with the CFD to intelligently increase the optimization accuracy and predict mass transfer and the unfavorable events (i.e., fouling) in various membrane processes. To reach this objective, four principal strategies of AI including SL, USL, SSL and ANN were explained and their advantages/disadvantages were discussed. Then after, prevalent ML-based algorithm for membrane-based separation processes. Finally, the application potential of AI techniques in different membrane processes (i.e., fouling control, desalination and wastewater treatment) were presented.

Design and development of multilayer cotton masks via machine learning

With the ongoing COVID-19 pandemic, reusable high-performance cloth masks are recommended for the public to minimize virus spread and alleviate the demand for disposable surgical masks. However, the approach to design a high-performance cotton mask is still unclear. In this study, we aimed to find out the relationship between fabric properties and mask performance via experimental design and machine learning. Our work is the first reported work of employing machine learning to develop protective face masks. Here, we analyzed the characteristics of Egyptian cotton (EC) fabrics with different thread counts and measured the efficacy of triple-layered masks with different layer combinations and stacking orders. The filtration efficiencies of the triple-layered masks were related to the cotton properties and the layer combination. Stacking EC fabrics in the order of thread count 100-300-100 provides the best particle filtration efficiency (45.4%) and bacterial filtration efficiency (98.1%). Furthermore, these key performance metrics were correctly predicted using machine-learning models based on the physical characteristics of the constituent EC layers using Lasso and XGBoost machine-learning models. Our work showed that the machine learning-based prediction approach can be generalized to other material design problems to improve the efficiency of product development.

A low-cost and high-performance thin-film composite forward osmosis membrane based on an SPSU/PVC substrate

A low-cost sulfonated polysulfone (SPSU)/poly(vinyl chloride) (PVC) substrate based high-performance thin-film composite (TFC) forward osmosis (FO) membrane was fabricated in this work. The results showed that the morphologies of the substrates were looser and more porous, and the porosity, pure water permeability, surface hydrophilicity, and average pore size of the substrates significantly improved after the SPSU was introduced into the PVC substrates. Furthermore, the SPSU/PVC-based TFC membranes exhibited rougher, looser and less crosslinked polyamide active layers than the neat PVC-based TFC membrane. The water permeability obviously increased, and the structure parameter dramatically declined. Moreover, the FO performance significantly improved (e.g. the water flux of TFC2.5 reached 25.53/48.37 LMH under FO/PRO mode by using 1.0 M NaCl/DI water as the draw/feed solution, while the specific salt flux exhibited a low value of 0.10/0.09 g/L). According to the results, it can be concluded that 2.5% of SPSU was the optimal blend ratio, which exhibited the lowest sulfonated material blend ratio compared to the data reported in the literature. Hence, this is a feasible and low-cost fabrication approach for high-performance FO membrane by using the cheap PVC and low blend-ratio SPSU as the membrane materials.

High-performance thin-film composite forward osmosis membrane fabricated on low-cost PVB/PVC substrate

The incorporation of the PVB significantly improved the performance of the PVB/PVC substrates based thin-film composite forward osmosis membrane.