Analysis of an ultrafiltration model: Influence of operational conditions

Vincent‐Vela, María‐Cinta; Cuartas-Uribe, B.; Álvarez-Blanco, Silvia; Lora-García, Jaime

doi:10.1016/j.desal.2011.08.030

Cited by 17 publications

(14 citation statements)

References 56 publications

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“…(21) For an ultrafiltration unit based on a tubular ceramic membrane and for superficial velocities of retentate inside the tube over 2 m/s, the following values of parameters in eq. 21were reported [49][50][51][52]: R 0 =2-1210 13 m -1 and r g / c Xg =3-8´10 9 s -1 .…”

Section: Total Ultrafiltration Permeate Fluxmentioning

confidence: 99%

Modelling of Ethanol Fermentation Coupled with Product Recovery by Pervaporation

Trică¹,

Pârvulescu²,

Dobre³

et al. 2017

Rev. Chim.

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Bioethanol is the most important biofuel produced by fermentation of sugars from various biomass types. The main disadvantages associated to this process consist in the negative effect of high ethanol concentration on the cell growth and in the separation cost of ethanol-water system resulted in the fermentation process. Sugar fermentation using Saccharomyces cerevisiae yeast coupled with bioethanol recovery by pervaporation has been modeled and simulated in this paper. In order to avoid the clogging of pervaporation membrane, the yeast cells were previously retained into an ultrafiltration unit. Three operating modes were analyzed and compared, i.e., classical batch fermentation (BF), batch fermentation coupled with external ultrafiltration and pervaporation (BFPV), and fed batch fermentation coupled with external ultrafiltration and pervaporation (FBFPV). Surface areas of ultrafiltration and pervaporation units were selected as process control variables.

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Section: Total Ultrafiltration Permeate Fluxmentioning

confidence: 99%

Modelling of Ethanol Fermentation Coupled with Product Recovery by Pervaporation

Trică¹,

Pârvulescu²,

Dobre³

et al. 2017

Rev. Chim.

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“…One of the main advantages of the models developed by Hermia is the physical meaning of their phenomenological coefficients, as they allow a deeper comprehension of the fouling mechanisms taking place onto the membrane surface and/or inside its pores. The main hypotheses of each fouling mechanism are well described in the literature [22,25] and can be resumed as follows: if the solute molecules have a much smaller size than the membrane pores, they can enter in the pores, attach to their walls and diminish the internal diameter of such pores (standard blocking); when solute molecules are approximately of the same size as membrane pores, these molecules are able to seal the pore and accumulate one on each other (intermediate blocking) or they form a monolayer (complete blocking); if the solute molecules cannot pass through the membrane pores as the former ones are much bigger than the latter, solute molecules can form a cake on the membrane surface (cake/gel layer formation).…”

Section: ( )mentioning

confidence: 99%

“…In addition, the steady-state permeate flux obtained is greater at 3 m/s than that achieved at 1 m/s. This pattern can be explained by the greater the shear stress that high crossflow velocity causes on the proximity of the membrane surface and thus, the solute molecule deposited as a cake layer on the membrane surface diminishes [25,36]. In addition, concentration polarization has been reported to be a significant foulant phenomenon to take into account [37][38][39][40].…”

Section: Network Architecturementioning

confidence: 99%

Comparison between artificial neural networks and Hermia’s models to assess ultrafiltration performance

Corbatón-Báguena

Vincent‐Vela

Gozálvez-Zafrilla

et al. 2016

Separation and Purification Technology

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In this work, flux decline during crossflow ultrafiltration of macromolecules with ceramic membranes has been modeled using artificial neural networks. The artificial neural network tested was the multilayer perceptron. Operating parameters (transmembrane pressure, crossflow velocity and time) and dynamic fouling were used as inputs to predict the permeate flux. Several pretreatments of the experimental data and the optimal selection of the parameters of the neural networks were studied to improve the fitting accuracy. The fitting accuracy obtained with artificial neural networks was compared with Hermia pore blocking models adapted to crossflow ultrafiltration. The artificial neural networks generate simulations whose performance was comparable to that of Hermia's models adapted to crossflow ultrafiltration. Considering the computational speed, high accuracy and the ease of the artificial neural networks methodology, they are a competitive, powerful and fast alternative for dynamic crossflow ultrafiltration modeling.

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“…The resistance in a series model based on Darcy's law was investigated; it was based on the dependence of the flux on the pressure gradient and viscosity (μ) . A general membrane‐fouling model that accounted for the internal clogging, partial and total clogging, cake deposition, and cake deposition with retro flux for an ultrafiltration system was developed by Ghaffour .…”

Section: Introductionmentioning

confidence: 99%

Experiment and simulation of the simultaneous removal of organic and inorganic contaminants by micellar enhanced ultrafiltration with mixed micelles

Vibhandik

Pawar

Marathe

2014

J of Applied Polymer Sci

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Lead, chromium, and chlorobenzene (CB) were removed simultaneously by micellar enhanced ultrafiltration with a mixture of anionic and nonionic surfactants. The process parameters, including the molar ratio of the nonionic surfactant to the ionic surfactant (α), surfactant to metal ion (S/M) molar ratio, applied pressure, and inlet flow rate, were investigated. As α was varied from 0 to 1.5, the rejection of the metal ions increased. The optimum α value was 0.5. The permeate flux decreased by 28% with increasing α from 0 to 1.5. The S/M ratio was optimized at 10 in the presence of CB. The addition of CB increased the rejection of surfactants and decreased the metal‐ion rejection. The effect of the applied pressure and inlet flow were studied and found to be optimum at 1 bar and 150 mL/min, respectively. The optimized parameters were applied to the steady‐state process. The multiple‐solute model was applied to the steady‐state data. The relative error for solute rejection was varied from 5 to 9%, and the relative error for flux was 5%. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 41435.

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Analysis of an ultrafiltration model: Influence of operational conditions

Cited by 17 publications

References 56 publications

Modelling of Ethanol Fermentation Coupled with Product Recovery by Pervaporation

Modelling of Ethanol Fermentation Coupled with Product Recovery by Pervaporation

Comparison between artificial neural networks and Hermia’s models to assess ultrafiltration performance

Experiment and simulation of the simultaneous removal of organic and inorganic contaminants by micellar enhanced ultrafiltration with mixed micelles

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