Experimental investigation and mathematical modeling of nano‐composite membrane fabrication process: Focus on the role of solvent type

Abstract

In this study, the effect of fabrication parameters on the performance of nano-SiO 2 -embedded membranes fabricated by three prevalent solvents was investigated. Genetic programming was applied for modeling and estimating the best preparation conditions to obtain a proper membrane for oily wastewater microfiltration. Membranes were prepared via a combination of vapor-induced phase separation and nonsolvent-induced phase separation methods. Different types of solvents such as N-methyl-2-pyrrolidone, N,N-dimethyl formamide, and dimethyl sulfoxide were used in cooperating with SiO 2 nanoparticles to improve hydrophilicity properties of polyethersulfone. Thermodynamic behavior of solvents and their interaction with polymer and nonsolvent were considered as criteria for forming a suitable membrane with the maximum effectiveness of nanoparticles. The results showed that nanoparticles are more influential along with a solvent that has a weak affinity with a polymer, which subsequently can produce efficient membranes under conditions with a balance between exposure time and relative humidity. The optimum amounts of parameters were estimated by genetic algorithm to introduce a condition for having a membrane with the maximum permeate flux. Membranes fabricated with dimethyl sulfoxide represented the highest pore size and the most hydrophilic surface, which, in turn, led to the desired membrane (permeate flux 300 L/m 2 hr and oil rejection 97%).

Influence of Polymer Solvents on the Properties of Halloysite-Modified Polyethersulfone Membranes Prepared by Wet Phase Inversion

“…The studies on the influence of the polymer solvent on the properties of the mixedmatrix PES membranes modified with NPs are very limited. Ghandashtani et al [38] investigated the effect of three solvents, NMP, DMF, and DMSO, on the performance of SiO 2modified PES mixed matrix membranes. The highest permeate flux and oil rejection were observed in the case of the membrane fabricated using DMSO, due to the largest pore size and the most hydrophilic surface [38].…”

“…Ghandashtani et al [38] investigated the effect of three solvents, NMP, DMF, and DMSO, on the performance of SiO 2modified PES mixed matrix membranes. The highest permeate flux and oil rejection were observed in the case of the membrane fabricated using DMSO, due to the largest pore size and the most hydrophilic surface [38]. However, these results are in disagreement with the previously cited papers [32,34,35] on the influence of various solvents on the properties of neat PES membranes.…”

Statistical Analysis of Synthesis Parameters to Fabricate PVDF/PVP/TiO2 Membranes via Phase-Inversion with Enhanced Filtration Performance and Photocatalytic Properties

“…Many studies have investigated different conditions separately, varying one factor while keeping the others fixed [ 32 , 33 , 34 , 35 ]. Recently, a few researchers have started to evaluate the variable conditions simultaneously, for which statistical analysis is required [ 36 , 37 , 38 , 39 , 40 , 41 ]. As a result, the statistical approach for membrane fabrication using the response surface methodology became an important step.…”

“…Orooji et al [ 38 ] fabricated membranes via phase-inversion using different solvent ratios, evaporation times, and bath compositions to improve pure water flux and remove BSA from water. Ghandashtani et al [ 41 ] fabricated various microfiltration membranes to treat oil-in-water emulsions and evaluated the effect of three different solvents while varying the evaporation time, relative humidity, and concentration of SiO 2 additive. Vatanpour et al [ 42 ] investigated the different PVDF and TiO 2 contents of UF membranes while varying operational conditions of submerged photocatalytic membrane reactors to evaluate the photocatalytic decomposition of Rhodamine B dye.…”

A predictive model for tuning additives for the fabrication of porous polymeric membranes

“…In theory, ANN is composed of a group of simple functioning elements based on the structure and processing of biological neural networks which are ordered in parallel layers and internally connected [18]. Indeed, GA which is a bio-inspired optimizer alone [19] or in combination with ANN was introduced as a useful approach to find the best fitness function in PVDF [20], polyethersulfone (PES) and polysulfone (PS) membranes preparation [18]. We found few references to compare ANN and RSM modeling capabilities in membrane science and this makes it a little hard to judge about the pros and cons of each approaches [21,22].…”