Experimental study of micro-particle fouling under forced convective heat transfer

Peyghambarzadeh, S.M.; Vatani, Ali; Jamialahmadi, M.

doi:10.1590/s0104-66322012000400004

Cited by 19 publications

(3 citation statements)

References 32 publications

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“…K/kW for wt.%=0.05, 0.1 and 0.15, respectively. Figure 9a represents a comparison chart between the resistance values extracted from the literature and those calculated from the experiments [58]. As perceived, the calculated data are in a reasonable agreement with those of data extracted from the literature within the deviation of ±20%.…”

Section: Nanoparticle Foulingsupporting

confidence: 66%

Flow Boiling Heat Transfer Characteristics of Titanium Oxide/Water Nanofluid (Tio2/Di Water) in an Annular Heat Exchanger

Nakhjavani

Zadeh

2020

Journal of Thermal Engineering

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A range of experiments was conducted to measure the heat transfer characteristics of titanium oxide/deionized water nanofluid (NF) inside a steel-made Pyrex annular system. A set of experiments was designed and performed at inlet temperature (IT) of the NF (333 K-363 K), the applied heat flux (AHF) (4.98 kW/m 2 to 112 kW/m 2 ), 1988 < Re < 13,588 and dispersion concentration of wt.%=0.05 to wt.%=0.15) on the average heat transfer coefficient (HTC) and boiling section's average pressure drop (PD). It was demonstrated that the increase in the volume flow and the AHF can increase the HTC while increasing the weight concentration of the NF, initially increased the HTC such that the maximum enhancement in the HTC was 35.7% at wt.%=0.15 and Re=13500, however, over the time, the HTC of the NF decreased. The reduction in HTC was attributed to the formation of continual sedimentation on the boiling surface after 1000 minutes of the operation. The IT of the NF slightly increased the HTC, which was due to the enhancement in the thermal and physical properties such as thermal conductivity. The maximum enhancement in HTC due to increase of the IT from 333 K to 363 K was 4.2% at wt.%=0.15 and Re=13500. The bubble formation was also found to be a strong function of the applied HF such that with increasing the HF, the rate of the bubble formation increased, which was also the reason behind the augmentation in the HTC at larger AHFs. Also, the PD was augmented due to the increase in the velocity and flow and also weight concentration of NF. The highest value measured for PD was 9 kPa recorded at a weight fraction of 0.15 and Re=13500, which was 28% larger than that of measured for the base fluid. It was also found that a continual fouling layer of nanoparticles (NPs) was formed on the boiling surface, which induced a thermal resistance against the boiling heat transfer. The fouling formation reduced the HTC of the NF such that the maximum reduction in the HTC was 21.6% after 1000 minutes of the operation of the heater.

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Section: Nanoparticle Foulingsupporting

confidence: 66%

Flow Boiling Heat Transfer Characteristics of Titanium Oxide/Water Nanofluid (Tio2/Di Water) in an Annular Heat Exchanger

Nakhjavani

Zadeh

2020

Journal of Thermal Engineering

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“…(Grandgeorge et al 1998) performed an experimental study on the liquid-phase particulate fouling using deionized water containing TiO 2 particles as a foulant fluid in stainless steel plate heat exchangers. (Peyghambarzadeh et al 2012) studied the deposition of micron-sized α-alumina particles which are introduced into a hydrocarbon base fluid (n-heptane).The deposition during forced convection heat transfer is measured using an accurate thermal approach. Furthermore, new theoretical model has been developed to predict the asymptotic fouling resistance.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

Benyekhlef

Mohammedi

Hassani

et al. 2021

Water Science and Technology

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In this work an artificial neural network model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. This data points contains 6 inputs, including time, volumetric concentration, heat flux, mass flow rate, inlet temperature, thermal conductivity and fouling resistance as an output. The experimental data are used for training, testing and validation the ANN using multiple layer perceptron (MLP). The comparison of statistical criteria of different networks shows that the optimal structure for predicting the fouling resistance of the nanofluid is the MLP network with 20 hidden neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The accuracy of the model was assessed based on three known statistical metrics including mean square error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The obtained model was found with the performance of {MSE = 6.5377 × 10−4, MAPE = 2.40% and R2 = 0.99756} for the training stage, {MSE = 3.9629 × 10−4, MAPE = 1.8922% and R2 = 0.99835} for the test stage and {MSE = 5.8303 × 10−4, MAPE = 2.57% and R2 = 0.99812} for the validation stage. In order to control the fouling procedure, and after conducting a sensitivity analysis, it found that all input variables have strong effect on the estimation of the fouling resistance.

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“…Fouling is a natural and costly phenomenon that affects a wide range of industrial sectors such as cooling water towers, heat exchange systems, drinking water systems (wells, filtration membranes, distribution systems, storage tanks, plumbing systems in buildings), wastewater treatment and seawater desalination [ 1 , 2 , 3 , 4 ]. It is defined as the deposition of unwanted materials on the surfaces [ 5 ]. In membrane based technologies related to water treatment, four different types of fouling have been described: (1) mineral precipitation on the membrane surfaces (typically with low solubility salts such as magnesium, calcium and barium salts), (2) colloidal or particulate matter fouling (i.e., clay, silt and humic particles) (3) organic fouling (i.e., proteins, polysaccharides, lipids, oil and humic acids) and (4) biofouling by microorganisms as extracellular polymeric substances (EPS) produced by bacteria [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Antibacterial Properties of Polysulfone Membranes Blended with Arabic Gum

et al. 2019

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Polysulfone (PS) membranes blended with different loadings of arabic gum (AG) were synthesized using phase inversion method and the antibacterial properties of the synthesized membranes were tested using a number Gram-negative (Escherichia coli, Klebsiella pneumonia and Pseudomonas aeruginosa) and Gram-positive (Staphylococcus aureus) bacterial species. It was shown that AG addition to the dope polymer solutions essentially changed porous structure, hydrophilicity and zeta potential of the cast PS/AG membranes. These changes were due to the amphiphilic properties of AG macromolecules that contained negatively charged hydrophilic residues. A pronounced decrease in bacterial attachment was seen in the field emission scanning electron microscopy (FESEM) images for PS/AG membrane samples compared to both commercial (Microdyn-Nadir) and bare PS (without AG) membranes. AG loading dependent trend was observed where the prevention of bacterial colonization on the membrane surface was strongest at the highest (7 wt. %) AG loading in the casting solution. Possible mechanisms for the prevention of bacterial colonization were discussed. Significantly, the inhibition of bacterial attachment and growth on PS/AG membranes was observed for both Gram-positive and Gram-negative bacterial models, rendering these novel membranes with strong biofouling resistance attractive for water treatment applications.

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Experimental study of micro-particle fouling under forced convective heat transfer

Cited by 19 publications

References 32 publications

Flow Boiling Heat Transfer Characteristics of Titanium Oxide/Water Nanofluid (Tio2/Di Water) in an Annular Heat Exchanger

Flow Boiling Heat Transfer Characteristics of Titanium Oxide/Water Nanofluid (Tio2/Di Water) in an Annular Heat Exchanger

Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

Antibacterial Properties of Polysulfone Membranes Blended with Arabic Gum

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