PREDICTION OF STABILITY AND THERMAL CONDUCTIVITY OF SnO2NANOFLUID VIA STATISTICAL METHOD AND AN ARTIFICIAL NEURAL NETWORK

Kazemi‐Beydokhti, Amin; Namaghi, Hamed Azizi; Asgarkhani, M. A. Haj; Heris, Saeed Zeinali

doi:10.1590/0104-6632.20150324s00003518

Cited by 35 publications

(8 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As can be observed, the Df and sum of square of lack of t is zero. The lack of t contain the insigni cant parameters [31]. Therefore, it can be concluded that the proposed models in Table 1 and Table 2 involve only the signi cant factors.…”

Section: The Analysis Of Variance Studymentioning

confidence: 94%

“…The graphical approach can show the relationship between factors and response. In this method, the residuals are converted to the studentized residuals and are plotted versus main factors, run number or on normal probability [31,32]. The rst assumption of the ANOVA is the normality of error that can be veri ed using normal probability plot.…”

Section: The Analysis Of Variance Studymentioning

confidence: 99%

See 1 more Smart Citation

Improvement of photocatalytic decomposition of methyl orange by modified MWCNTs, prediction of degradation rate using statistical models

Abbasi

2021

J Mater Sci: Mater Electron

View full text Add to dashboard Cite

Multi-walled carbon nanotubes (MWCNTs) have been successfully modi ed with TiO2 nanoparticles via a two-step hydrolysis technique. Firstly, the pristine MWCNTs are functionalized in nitric acid (HNO3) to creation of oxygen containing groups. Secondly, TiO2 nanoparticles are synthesized on the surface of functionalized MWCNTs through hydrolysis method. The synthesized samples have been used as photocatalyst for decomposition of methyl orange (MO) as dye organic pollutant. The characterization of samples using x-ray diffraction pattern (XRD) con rm the presence of TiO2 nanoparticles with mixture of anatase and rutile phases. Regarding the photocatalytic performance of TiO2 nanoparticles and modi ed MWCNTs with TiO2 nanoparticles, it observed that the degradation rate of MO increases by increasing the irradiation time from 5 min to 35 min. The variation of degradation rate upon pH of suspension reveals that the maximum and minimum degradation rate are in acidic (pH=3) and neutral condition (pH=7). Meanwhile, the results show that the presence of MWCNTs leads to the enhancement of degradation rate. The analysis of variance (ANOVA) results con rm that both of main factors (irradiation time and pH) and their interaction have a signi cant in uence on the degradation rate of MO. However, the effect of irradiation time is more than that of pH and their interaction. The graphical methods verify the quality and adequacy of the statistical models for prediction of the degradation rate.

show abstract

Section: The Analysis Of Variance Studymentioning

confidence: 94%

Section: The Analysis Of Variance Studymentioning

confidence: 99%

Improvement of photocatalytic decomposition of methyl orange by modified MWCNTs, prediction of degradation rate using statistical models

Abbasi

2021

J Mater Sci: Mater Electron

View full text Add to dashboard Cite

show abstract

“…The samples' re ectances were measured using visible spectrophotometry, under D65 illuminant at 10° (Konica-Minolta CM 3600d) (Kazemi-Beydokhti et al, 2015).…”

Section: Dyeing Process Designmentioning

confidence: 99%

Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

Rosa

Guerhardt

Júnior

et al. 2021

Preprint

View full text Add to dashboard Cite

This work explores the modeling and optimization of the conditions to obtain a set of blue pigments for dyeing reactive cotton, by means of an approach that combines the techniques response surface methodology (RSM) and artificial neural network (ANN). By means of RSM technique the interactions and the effects of the main process variables (factors) on the behavior of coloristic intensity (K.S-1) were investigated. For this, a 26 central composite rotational design (CCRD) was carried out considering the factors temperature, NaCl, Na2CO3, NaOH, processing time and RB5 concentration. The results obtained show that all investigated factors have considerable effect on the behavior of K.S-1. The data produced in the dyeing experiments were used to build and train a Multilayer Perceptron ANN (MLP-ANN) to predict K.S-1, being the input layer of the MLP-ANN designed according to the results achieved by the RSM. The non-linear behavior of dyeing with RB5 was successfully modeled by a three-layer MLP-ANN comprising 6 input neurons, 15 hidden neuros, and 1 output neuron to indicate the value of K.S-1. The results achieved in the performed simulations confirmed the ANN effectiveness to predict K.S-1 values in RB5 the dyeing process, with high coefficient of determination (R2=0.942). The developed approach allowed the composition of a table containing optimized conditions to obtain a set of colors of the blue palette using RB5 dye, varying from sky blue to oxford blue, which will facilitate the assembly of the dyes. Finally, the experiments conducted in this work allowed the development of a computational tool to support the dyeing process, saving chemical inputs and time in cotton dyeing with specific dyestuff.

show abstract

“…Soft computing methods provide several tools to show nonlinear input-output mapping and solve a variety of nonlinear and complex problems. Among these methods, the clustering algorithm is applied for classifying elements into categories or clusters on the basis of their similarity and can be also used for anomaly detection in industrial applications (Rodriguez et al, 2014;Elhamifar et al, 2013;Birant et al, 2007;Assidjo et al, 2008;Kazemi-Beydokhti et al, 2015). Besides, the combination of clustering algorithm and CFD method is rarely researched.…”

Section: Brazilian Journal Of Chemical Engineeringmentioning

confidence: 99%

Modeling of Multiphase Flow in an Air-Cooling System Using the CFD-Fsca Approach

Jin

Chen

Ren

et al. 2018

Braz. J. Chem. Eng.

View full text Add to dashboard Cite

The deposition of crystallized ammonium salt particles presents serious damage to the hydrogenation reactor effluent air cooler (REAC) in petrochemical engineering. The correlation technology process was analyzed for predicting the particle deposition situation in air coolers. Moreover, the particle crystallization and deposition were revealed, and a mathematical model for flow field simulation was established by computational fluid dynamics (CFD). In addition, the fast search clustering algorithm (FSCA) realized in Matlab software was applied to detect tube anomaly. The relatively greater risk of deposition or blockage in the tube area was detected, and this finding is consistent with the distribution of the surface temperature field in air coolers. Meanwhile, good qualitative agreement was found between the distribution rule of particle deposition rate as simulated by CFD and the situation of opened header box on the spot. Therefore, the combined method of CFD simulation and FSCA detection can be utilized for predicting the condition of particle deposition in the air-cooling systems.

show abstract

PREDICTION OF STABILITY AND THERMAL CONDUCTIVITY OF SnO2NANOFLUID VIA STATISTICAL METHOD AND AN ARTIFICIAL NEURAL NETWORK

Cited by 35 publications

References 30 publications

Improvement of photocatalytic decomposition of methyl orange by modified MWCNTs, prediction of degradation rate using statistical models

Improvement of photocatalytic decomposition of methyl orange by modified MWCNTs, prediction of degradation rate using statistical models

Modeling and Optimization of Reactive Cotton Dyeing Using Response Surface Methodology Combined with Artificial Neural Network

Modeling of Multiphase Flow in an Air-Cooling System Using the CFD-Fsca Approach

Contact Info

Product

Resources

About