scholarly journals A new Correlation for Predicting the Filtration Flux Development in a Membrane Tube

2020 ◽  
Vol 24 (2) ◽  
pp. 38-41
Author(s):  
Djamel Sahel ◽  
Mohamed Bousmaha
Keyword(s):  
New Correlation ◽  
Membrane Tube ◽  
Filtration Flux

On Scott's new correlation of the Texas Midway

1926 ◽  
Vol s5-12 (71) ◽  
pp. 453-455
Author(s):  
J. A. Gardner
Keyword(s):  
New Correlation

Development of a New Correlation and Post Processing of Heat Transfer Coefficient and Pressure Drop of Functionalized COOH MWCNT Nanofluid by Artificial Neural Network

2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Regression Coefficient ◽  
New Correlation ◽  
The Neural Network

Background: Because of nanofluids applications in improvement of heat transfer rate in heating and cooling systems, many researchers have conducted various experiments to investigate nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat transfer are examples of these characteristics. Method: This paper presents a modeling and validation method of heat transfer coefficient and pressure drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and proposing a new correlation. In the current experiment, the ANN input data has included the volume fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN outputs. Results: Comparing modeling results with proposed correlation proves that the empirical correlation is not able to accurately predict the experimental output results, and this is performed with a lot more accuracy by the neural network. The regression coefficient of neural network outputs was equal to 99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure drop. The regression coefficient for the provided equation was also equal to 97.02% and 77.90%, respectively, for these two parameters, which indicates this equation operates much less precisely than the neural network. Conclusion: So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity and viscosity executed by different scholars via neural networks.

Ultrafiltration of silica sols

1989 ◽  
Vol 54 (1) ◽  
pp. 91-101 ◽  
Author(s):  
Milan Stakić ◽  
Slobodan Milonjić ◽  
Vladeta Pavasović ◽  
Zoja Ilić
Keyword(s):  
Particle Size ◽  
Silica Particle ◽  
Specific Resistance ◽  
Membrane Surface ◽  
Membrane Resistance ◽  
Experimental Conditions ◽  
Surface Unit ◽  
Silica Sols ◽  
Filtration Flux ◽  
Batch Cell

Ultrafiltration of three laboratory made silica and two commercial silica sols was studied using Amicon YC membrane in a 200 ml capacity batch-cell. The effect of silica particle size, stirring conditions, pressure, pH and silica contents on ultrafiltration was investigated. The results obtained indicate that the smaller particles have, disregarding the stirring conditions, lower filtration flux. The differences observed in filtration flux are more pronounced in the conditions without stirring. The obtained value of the membrane resistance is independent of the conditions investigated (stirring, pressure, pH, silica contents and particle size). The values of the resistance of polarized solids, specific resistance, and the mass of gel per membrane surface unit were calculated for all experimental conditions.

scholarly journals Potential Pitfalls in Membrane Fouling Evaluation: Merits of Data Representation as Resistance Instead of Flux Decline in Membrane Filtration

Membranes ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 460
Author(s):  
Bastiaan Blankert ◽  
Bart Van der Bruggen ◽  
Amy E. Childress ◽  
Noreddine Ghaffour ◽  
Johannes S. Vrouwenvelder
Keyword(s):  
Membrane Fouling ◽  
Membrane Filtration ◽  
Data Representation ◽  
Strong Impact ◽  
Experimental Conditions ◽  
Permeable Membrane ◽  
Dead End ◽  
Flux Decline ◽  
The Difference ◽  
Filtration Flux

The manner in which membrane-fouling experiments are conducted and how fouling performance data are represented have a strong impact on both how the data are interpreted and on the conclusions that may be drawn. We provide a couple of examples to prove that it is possible to obtain misleading conclusions from commonly used representations of fouling data. Although the illustrative example revolves around dead-end ultrafiltration, the underlying principles are applicable to a wider range of membrane processes. When choosing the experimental conditions and how to represent fouling data, there are three main factors that should be considered: (I) the foulant mass is principally related to the filtered volume; (II) the filtration flux can exacerbate fouling effects (e.g., concentration polarization and cake compression); and (III) the practice of normalization, as in dividing by an initial value, disregards the difference in driving force and divides the fouling effect by different numbers. Thus, a bias may occur that favors the experimental condition with the lower filtration flux and the less-permeable membrane. It is recommended to: (I) avoid relative fouling performance indicators, such as relative flux decline (J/J0); (II) use resistance vs. specific volume; and (III) use flux-controlled experiments for fouling performance evaluation.

Molecular modeling of membrane tube pearling and the effect of nanoparticle adsorption

2014 ◽  
Vol 16 (22) ◽  
pp. 10799-10809 ◽  
Author(s):  
Tongtao Yue ◽  
Xianren Zhang ◽  
Fang Huang
Keyword(s):  
Molecular Modeling ◽  
Water Pressure ◽  
Membrane Tube

DPD simulations suggest that the membrane tube pearling can be regulated by the inner water pressure and NP adsorption.

Experimental study on repair of large segmental bone defects of goat femur by nano calcium-deficient hydroxyapatite-multi (amino acid) copolymer membrane tubes

2021 ◽  
pp. 088532822110002
Author(s):  
Yan Xiong ◽  
Hong Duan ◽  
Bin Zhang ◽  
Cheng Ren ◽  
Zeping Yu ◽  
...  
Keyword(s):  
Amino Acid ◽  
Bone Defects ◽  
Torsional Stiffness ◽  
Control Group ◽  
Allogenic Bone ◽  
Acid Copolymer ◽  
Membrane Tube ◽  
Segmental Bone ◽  
Experimental Group ◽  
Segmental Bone Defects

Objective The purpose of this study was to observe feasibility of nano calcium-deficient hydroxyapatite-multi (amino acid) copolymer (n-CDHA-MAC) membrane tubes in repairing goat femurs’ large defects. Methods Twelve goats were divided into two groups, whose femurs were created 30 mm segmental bone defects and then implants were performed. In experimental group, the bone defect of right femur was reconstructed by n-CDHA-MAC membrane tube, while left side was reconstructed by allogenic bone tube in control group. Every three goats were sacrificed at 4, 8, 16, 24 weeks after operation respectively. General observation, X-ray analysis, histology, Scanning electron microscope (SEM) examination and protein level comparison of BMP-2 were conducted to evaluate the effects of repairing segmental bone defects. Results All goats recovered well from anesthesia and surgical interventions. The radiographic evaluations showed that periosteal reaction outside of the membrane tubes and allogenic bone tubes were observed 4 weeks after surgery. At 16 weeks, callus was continuously increased in experimental group, which was more obvious than control group. At 24 weeks, callus outside of the membrane tubes connected together. Histologic evaluation showed fibro-cartilage callus was evolved into bony callus in experimental group, which was more obvious than control group at 8 and 16 weeks. The protein expression level of BMP-2 increased at 4, 8 weeks and peaked at 16 weeks in experimental groups. There were statistical differences at 8 and 16 weeks ( P < 0.05). At each time point in 8, 16, 24 weeks after surgery, the bending stiffness, torsional stiffness and compressive strength of the two groups were similar, and there was no significant difference ( P > 0.05). Conclusions This novel surface degradation n-CDHA-MAC membrane tube has good ability to maintain enough membrane space, which can provide long-term and stable biomechanical support for large bone defects and integrate well with the surrounding bone.

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