Application of experimental design approach and artificial neural network (ANN) for the determination of potential micellar-enhanced ultrafiltration process

Rahmanian, Bashir; Pakizeh, Majid; Mansoori, Seyed Ali Akbar; Abedini, Reza

doi:10.1016/j.jhazmat.2010.11.135

Cited by 87 publications

(50 citation statements)

References 25 publications

(23 reference statements)

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“…values for the zeta potential and thermal conductivity ratio. Because the AARE values for both parameters are very small and close to zero, we can conclude that the ANN model is suitable for the prediction of this nanofluid system and that the prediction values are reliable (Rahmanian et al, 2011). …”

Section: Optimization Of the Operational Conditionsmentioning

confidence: 79%

“…Also, an ANN can be considered as a massively parallel distributed processor, which transfers the knowledge and rules existing beyond the experimental data into the network structure for further applications (Rahmanian et al, 2011;Shanbedi et al, 2013;Salehi et al, 2013;Shanbedi et al, 2014). In this study, a multilayer feed-forward neural network has been used to design the complex nonlinear relationships between input and output layers.…”

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

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

Kazemi‐Beydokhti

Namaghi

Asgarkhani

et al. 2015

Braz. J. Chem. Eng.

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-Central composite rotatable design (CCRD) and artificial neural networks (ANN) have been applied to optimize the performance of nanofluid systems. In this regard, the performance was evaluated by measuring the stability and thermal conductivity ratio based on the critical independent variables such as temperature, particle volume fraction and the pH of the solution. A total of 20 experiments were accomplished for the construction of second-order polynomial equations for both target outputs. All the influential factors, their mutual effects and their quadratic terms were statistically validated by analysis of variance (ANOVA). According to the results, the predicted values were in reasonable agreement with the experimental data as more than 96% and 95% of the variation could be predicted by the respective models for zeta potential and thermal conductivity ratio. Also, ANN proved to be a very promising method in comparison with CCD for the purpose of process simulation due to the complexity involved in generalization of the nanofluid system.

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Section: Optimization Of the Operational Conditionsmentioning

confidence: 79%

Section: Artificial Neural Network (Anns)mentioning

confidence: 99%

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

Kazemi‐Beydokhti

Namaghi

Asgarkhani

et al. 2015

Braz. J. Chem. Eng.

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“…This statistic is adjusted to the size of the model, more specifically, the number of factors. The addition of non significant terms to the model can usually decrease the R 2 Adj: value [15].…”

Section: Discussionmentioning

confidence: 99%

Acrylamide Formation during the Frying of Beef Burger: Effect of Temperature and Time

Ghasemian

Rezaei

Abedini

et al. 2011

Chem. Eng. Res. Bull.

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Acrylamide (CH 2 = CHCONH 2 ) is creating cancer chemical which produced in certain foods due to heating process at high temperature and low humidity conditions. Due to the hazardous effect of acrylamide, in this study, the effect of frying temperature (180 and 200• C) and frying time (4 and 6 minutes) on the acrylamide levels has been discussed in fried beef burger using full factorial design of experiment (DOE). Produced acrylamide in each sample was analyzed by GC-FID system in the presence of internal standard. Between investigated parameters, results from DOE reveal that frying temperature has the higher impact on the produced acrylamide.

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“…In this method, subset (fraction) runs of full factorial design carefully chosen for investigation the effect of experimental factors on results [19,20]. The selected variables and their levels are shown in Table 1.…”

Section: Synthesis Of Mn-doped Zns Quantum Dotsmentioning

confidence: 99%

Selective Determination of Atropine Using poly Dopamine-Coated Molecularly Imprinted Mn-Doped ZnS Quantum Dots

Abbasifar

Samadi‐Maybodi

2016

J Fluoresc

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In this study, a selective method for the determination of atropine in pharmaceutical formulations was proposed. L-cysteine capped Mn-doped ZnS quantum dots (QDs) were prepared in an in-situ method using sodium thiosulfate precursor and characterized by spectrofluorometer, Fourier transform infrared spectroscopy (FT-IR), transmission electron microscope (TEM) and X-ray diffractometer (XRD). Dopamine hydrochloride was used as a precursor for preparation of poly dopamine-coated molecularly imprinted Mn-doped ZnS quantum dots. Finally, these prepared molecularly imprinted Mn-doped ZnS quantum dots were used for determination of atropine in pharmaceutical formulations. The obtained linear range for determination of atropine was in the range of 2 × 10(-8) - 7 × 10(-6) M, with a correlation coefficient (R(2)) of 0.9889; and the detection limit (S/N = 3) was 3.2 nM.

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Application of experimental design approach and artificial neural network (ANN) for the determination of potential micellar-enhanced ultrafiltration process

Cited by 87 publications

References 25 publications

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

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

Acrylamide Formation during the Frying of Beef Burger: Effect of Temperature and Time

Selective Determination of Atropine Using poly Dopamine-Coated Molecularly Imprinted Mn-Doped ZnS Quantum Dots

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