Prediction of viscosity of water-based Al2O3, TiO2, SiO2, and CuO nanofluids using a reliable approach

Meybodi, Mahdi Kalantari; Naseri, Saeed; Shokrollahi, Amin; Daryasafar, Amin

doi:10.1016/j.chemolab.2015.10.001

Cited by 61 publications

(23 citation statements)

References 85 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The deviation between the target data and model prediction is defined as the residual value. Moreover, the value of H can be obtained by the following relationship :

H = X {(X^{t} X)}^{- 1} X^{t}

where X is a matrix with m rows and n columns, and superscript t denotes for the matrix transpose operation. In addition, m is the number of datapoints, and n indicates the number of parameters.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Rigorous silica solubility estimation in superheated steam: Smart modeling and comparative study

Rostami

Shokrollahi

Esmaeili‐Jaghdan

et al. 2018

Env Prog and Sustain Energy

Self Cite

View full text Add to dashboard Cite

One of the main issues of wastewater treatment is the silica deposition in steam turbines. Evaporation of silica with the steam in adequate concentration is one of the main sources of scale formation in steam turbines. In this study, the authors introduce the utilization of a genetic‐based approach—gene expression programming (GEP)—for solubility prognostication of the silica in superheated steam of boilers with respect to water silica content and pressure. The result of GEP mathematical approach is a new algebraic formula to achieve our goals. Developed model predicts the silica solubility in the range of 0.8–22.1 MPa and 1–500 mg/kg for pressure and boiler water silica content, respectively. The results show that the constructed model outperforms pre‐existing method and provides acceptable predictions with average absolute relative deviation (AARD%) of 9.46% and determination coefficient (R2) of 0.98. Outcomes of the performed sensitivity analysis reveal that the effect of pressure is more pronounced than the impact of the boiler water silica content on the estimation of the silica solubility. Moreover, an outlier analysis based on Leverage approach was performed confirming the validity of the used database in this study. Finally, it can be concluded that the recommended tool in this study can be a suitable candidate for prognosticating the silica scaling in industries dealing with steam. © 2018 American Institute of Chemical Engineers Environ Prog, 38:e13089, 2019

show abstract

“…The deviation between the target data and model prediction is defined as the residual value. Moreover, the value of H can be obtained by the following relationship :

H = X {(X^{t} X)}^{- 1} X^{t}

Section: Resultsmentioning

confidence: 99%

“…The deviation between the target data and model prediction is defined as the residual value. Moreover, the value of H can be obtained by the following relationship [48]:…”

Section: Evaluation Of the Developed Modelmentioning

confidence: 99%

Rigorous silica solubility estimation in superheated steam: Smart modeling and comparative study

Rostami

Shokrollahi

Esmaeili‐Jaghdan

et al. 2018

Env Prog and Sustain Energy

Self Cite

View full text Add to dashboard Cite

show abstract

“…The nanofluids are a dispersion of nanosized elements into a base fluid (water or oils). Some of the advantages of the nanofluids include the improvement of thermal properties by removing the excess heat generated by a system, and tribological properties by reducing wear and COF [46,47].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the good tribological properties (anti wear and friction reduction), silica nanoparticles have been investigated as additives to lubricants. These nanoparticles have a good dispersion within commercial lubricants, tunable properties and composition in a very predictable manner to meet the needs of particular applications, providing the best viscosity according to experimental and mathematical model studies [45,46]. The SiO 2 nanoparticles have a good performance on the physical, chemical and electrical field.…”

Section: Introductionmentioning

confidence: 99%

Tribological and Thermal Transport Performance of SiO2-Based Natural Lubricants

2019

View full text Add to dashboard Cite

Fluids and lubricants are critical for the mechanical manufacturing processing of metals, due to a high amount of friction generated, also reflected as heat, could wear and damage tooling and machine components. The proper application of lubricants increases machinery lifetime, decreases long-term costs, and energy and time consumption due to the maintenance or components exchange/repairs. Besides being non-renewable, mineral oils bring consequences to the environment due to their low biodegradability and could affect the user with respiratory and skin diseases. Recently, due to an increase in environmental awareness, the search of biocompatible and efficient lubricants has become a technology goal. The vegetable oil-based lubricants are slowly emerging as ecofriendly and high-performance alternatives to petroleum-based lubricants. This study evaluates soybean, sunflower, corn and paraffinic oils reinforced with SiO2 nanoparticles. The thermal and tribological evaluations were performed varying the temperature and nanofiller concentrations. The thermal conductivity improvements were observed for all nanolubricants as the temperature and filler fraction increased. The highest thermal conductivities were observed at 323 K with 0.25 wt % SiO2. The soybean and corn oils unveiled a maximum enhancement of ~11%. The tribological evaluations showed that SiO2 addition, even in small concentration, resulted into a significant improvement on a load-carrying capacity. For instance, at 0.25 wt % enhancements of 45% and 60% were observed for soybean and sunflower oils, respectively. The coefficient of friction performance also showed enhancements between 10% and 26%.

show abstract

“…They obtained a successful model according to the results of 0.28 MSE and 0.910 MAPE error analyses [14]. Meybodi [15]. Karimi et al, used an accurate and effective artificial neural network (ANN) based on the genetic algorithm (GA) predicted 8 different nanofluids viscosity.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Viscosity Values of Nanofluids at Different pH Values by Alternating Decision Tree and Multilayer Perceptron Methods

Demirpolat

Daş

2019

Applied Sciences

View full text Add to dashboard Cite

Due to the poor thermal properties of conventional thermal fluids such as water, oil and ethylene glycol, small solid particles are added to these fluids to enhance heat transfer. Since the viscosity change determines the rheological behavior of a liquid, it is very important to examine the parameters affecting the viscosity. Since the experimental viscosity measurement is expensive and time-consuming, it is more practical to estimate this parameter. In this study, CuO (copper oxide) nanoparticles were produced and then Scanning Electron Microscope (SEM) images analyses of the produced particles were made. Nanofluids were obtained by using pure water, ethanol and ethylene glycol materials together with the produced nanoparticles and the viscosity values were calculated by experimental setups at different density and temperatures. For the viscosity values of nanofluids, predictive models were created by using different computational intelligence methods. Mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE) error analyses were used to determine the accuracy of the predictive models. The multilayer perceptron method, which has the least error value in computational methods, was chosen as the best predicting method. The multilayer perceptron method, with an average accuracy of 51%, performed better than the alternating decision tree method. As a result, the viscosity increased with the increase in the pH of the nanofluids produced by adding CuO nanoparticles and decreased with the increase in the temperature of the nanofluids. The importance of this study is to create a predictive model using computational intelligence methods for viscosity values calculated with different pH values.

show abstract

Prediction of viscosity of water-based Al2O3, TiO2, SiO2, and CuO nanofluids using a reliable approach

Cited by 61 publications

References 85 publications

Rigorous silica solubility estimation in superheated steam: Smart modeling and comparative study

Rigorous silica solubility estimation in superheated steam: Smart modeling and comparative study

Tribological and Thermal Transport Performance of SiO2-Based Natural Lubricants

Prediction of Viscosity Values of Nanofluids at Different pH Values by Alternating Decision Tree and Multilayer Perceptron Methods

Contact Info

Product

Resources

About