Mechanistic insights into the membrane fouling mechanism during ultrafiltration of high-concentration proteins via in-situ electrical impedance spectroscopy (EIS)

Tanudjaja, Henry J.; Ng, Angie Qi Qi; Chew, Jia Wei

doi:10.1016/j.jiec.2021.11.019

Cited by 9 publications

(2 citation statements)

References 48 publications

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“…There are 14 protein types in the database, with 78% being bovine serum albumin (BSA), highlighting the prevalence of the usage of BSA in protein-fouling studies. The majority of reported protein concentrations is in the range of 100–1000 ppm, which shows the relative scarcity of higher protein concentrations that is commonly found in the biopharmaceutical and dairy industries . As for membrane characteristics, 33 membrane materials have been investigated, with the most popular material being poly(ether sulfone) (PES).…”

Section: Methodsmentioning

confidence: 99%

“…The majority of reported protein concentrations is in the range of 100−1000 ppm, which shows the relative scarcity of higher protein concentrations that is commonly found in the biopharmaceutical and dairy industries. 107 As for membrane characteristics, 33 membrane materials have been investigated, with the most popular material being poly(ether sulfone) (PES). More details for protein type and membrane materials are listed in Tables S1 and S2, respectively.…”

Section: Databasementioning

confidence: 99%

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Understanding Single-Protein Fouling in Micro- and Ultrafiltration Systems via Machine-Learning-Based Models

Tanudjaja

Chew

2023

Ind. Eng. Chem. Res.

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Protein fouling is a complex mechanism. To enhance understanding on protein fouling of membranes, the target of this study is twofold: (i) to determine the relative influences of parameters via the Random Forest (RF) model and (ii) to evaluate the predictive capability of the Neural Network (NN) model. Membrane pore size is the most dominant influence on fouling followed by transmembrane pressure (TMP), while membrane configuration (i.e., flat-sheet, hollow fiber, or tubular) is the least dominant. The NN model gives modest predictive capability despite inconsistencies and variabilities of the experimental setups and protocols, which invariably affects the important parameters in the database compiled from past publications. The database was divided into microfiltration (MF) and ultrafiltration (UF) subsets based on the membrane pore size values. It was found that the dominant parameters for permeate flux are different, with membrane pore size and protein concentration being dominant for MF and UF, respectively, while TMP is dominant for protein rejection for both cases. For permeate flux, membrane material is the most dominant parameter for the non-BSA database, while membrane pore size remains the most dominant parameter for protein rejection regardless of the protein used. Results show that such data-driven RF and NN models can enhance the understanding on the relative dominance of the parameters on different phenomena and provide adequate prediction of protein fouling, in the absence of any governing equations.

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Section: Methodsmentioning

confidence: 99%

Section: Databasementioning

confidence: 99%