Quantification of Selective Transport of Fructose and Glucose During Membrane Filtration of Pomegranate Juice

The intelligent prediction of membrane fouling during membrane filtration by mathematical models and artificial intelligence models

Recently, membrane separation technology has been widely utilized in filtration process intensification due to its efficient performance and unique advantages, but membrane fouling limits its development and application. Therefore, the research on membrane fouling prediction and control technology is crucial to effectively reduce membrane fouling and improve separation performance. This review first introduces the main factors (operating condition, material characteristics, and membrane structure properties) and the corresponding principles that affect membrane fouling. In addition, mathematical models (Hermia model and Tandem resistance model), artificial intelligence (AI) models (Artificial neural networks model and fuzzy control model), and AI optimization methods (genetic algorithm and particle swarm algorithm), which are widely used for the prediction of membrane fouling, are summarized and analyzed for comparison. The AI models are usually significantly better than the mathematical models in terms of prediction accuracy and applicability of membrane fouling and can monitor membrane fouling in real-time by working in concert with image processing technology, which is crucial for membrane fouling prediction and mechanism studies. Meanwhile, AI models for membrane fouling prediction in the separation process have shown good potential and are expected to be further applied in large-scale industrial applications for separation and filtration process intensification. This review will help researchers understand the challenges and future research directions in membrane fouling prediction, which is expected to provide an effective method to reduce or even solve the bottleneck problem of membrane fouling, and to promote the further application of AI modeling in environmental and food fields.

Membrane-based Operations for the Fractionation of Polyphenols and Polysaccharides From Winery Sludges

The present work investigated the impact of ultrafiltration (UF) and nanofiltration (NF) membranes on the recovery and fractionation of polyphenolic compounds and polysaccharides from Sangiovese and Cabernet Sauvignon wine lees. A laboratory-made flat-sheet membrane in cellulose acetate (CA400-38) was used in the UF treatment of Sangiovese wine lees; three laboratory-made flat-sheet membranes in cellulose acetate (CA316, CA316-70, CA400-22) and a polyamide commercial membrane (NF90) were used in the NF treatment of Cabernet Sauvignon wine lees. All membranes were characterized in terms of hydraulic permeability and rejection toward references solutes; the performances of the membranes were measured in terms of productivity, fouling index, cleaning efficiency and retention toward target compounds.

Experimental results indicated that all UF and NF membranes were effective in separating target compounds rejecting more than 92% of polysaccharides with polyphenols preferentially permeating through the membrane. The UF membrane rejected more than 40% of total polyphenols; rejections toward non-flavonoids and flavonoids were less than 25% and 12.5%, respectively.

The laboratory-made NF membranes exhibited higher permeate flux values (of the order of 11–12 L/m2h) in comparison with the commercial NF membrane, despite the observed differences in the retention of specific solutes. Among the prepared membranes the CA316 showed a total rejection toward most part of non-flavonoids and flavonoids.

The experimental results support the use of UF and NF processes in a sequential design to fractionate and refine phenolic compounds from winery sludge for the production of concentrated fractions with high antioxidant activities.