Comprehensive heat transfer correlation for water/ethylene glycol-based graphene (nitrogen-doped graphene) nanofluids derived by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

Savari, Maryam; Moghaddam, Amin Hedayati; Amiri, Ahmad; Shanbedi, Mehdi; Ayub, Mohamad Nizam Bin

doi:10.1007/s00231-017-2047-y

Cited by 16 publications

(4 citation statements)

References 31 publications

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“…Therefore, an approach that adopts new algorithms can be an attractive alternative to classical methods since such an approach has a higher estimation power and includes a higher range of possibilities. Similar results have been reported by Khoshnevisan et al (2015), Savari, Moghaddam, Amiri, Shanbedi, and Ayub (2017) and Kaveh, Sharabiani, et al (2018) the comparison between the ANN and the ANFIS.…”

Section: Resultssupporting

confidence: 89%

Assessment of kinetics, effective moisture diffusivity, specific energy consumption, shrinkage, and color in the pistachio kernel drying process in microwave drying with ultrasonic pretreatment

Jahanbakhshi

Kaveh

Taghinezhad

et al. 2020

J Food Process Preserv

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The aim of this study is to determine the kinetics and quality properties of pistachios under different microwave powers (270, 450, and 630 W) and ultrasound (US) pretreatment at 0, 10, 20, 40, and 60 min. It is also aimed at comparing the estimations of the moisture ratio of pistachio by mathematical modeling, artificial neural networks (ANNs), and adaptive neuro‐fuzzy inference system (ANFIS) methods. The minimum and maximum values of effective moisture diffusivity (Deff) were 1.43 × 10–8 and 4.30 × 10–8 m2/s, respectively. Specific energy consumption (SECtotal) of the samples was varied between 58.097 and 122.21 kWh/kg. The lowest value of shrinkage (13.1%) and color change (19.97) were reported in a microwave power of 270 W and duration of US 0 min. The results showed that considering (R2 = .9997) and Mean Square Error (b∗ = 0.0004), the ANFIS model had a better performance in predicting the moisture ratio of pistachios compared to the mathematical models and ANNs. Practical applications Drying is defined as the process of removing moisture through the simultaneous transfer of heat and mass. Heat transfer from the surrounding environment to the foodstuff leads to the evaporation of the surface moisture. Moisture can also be transferred from inside the product to its surface and then evaporate there. The use of US waves has been suggested in combination drying methods especially in situations where preserving the appearance and nutritional value of the food are the main priority. The effective moisture diffusion coefficient is one of the important parameters in modeling, designing, and optimizing the drying process. Color is an important indicator of the quality of the food and represents the chemical, biochemical, and microbiological characteristics of the product. During the drying process, due to the evaporation of moisture from the foodstuff, the phenomenon of shrinkage occurs and this also affects the physical properties of the solids and the appearance of the final product.

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Section: Resultssupporting

confidence: 89%

Assessment of kinetics, effective moisture diffusivity, specific energy consumption, shrinkage, and color in the pistachio kernel drying process in microwave drying with ultrasonic pretreatment

Jahanbakhshi

Kaveh

Taghinezhad

et al. 2020

J Food Process Preserv

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“…Moreover, the Levenberg‐Marquardt (LM) and Bayesian Regularization (BR) algorithms were used. Three threshold functions namely sigmoid activation function (Logsig), linear activation function (Purelin), and hyperbolic tangent activation function (Tansig) were used to predict the proposed parameters (Jahanbakhshi & Salehi, ; Savari, Moghaddam, Amiri, Shanbedi, & Ayub, ).…”

Section: Methodsmentioning

confidence: 99%

Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

Abbaspour‐Gilandeh

Jahanbakhshi

Kaveh

2019

Food Science & Nutrition

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This study aimed to predict the drying kinetics, energy utilization (E u ), energy utilization ratio (EUR), exergy loss, and exergy efficiency of quince slice in a hot air (HA) dryer using artificial neural networks and ANFIS. The experiments were performed at air temperatures of 50, 60, and 70°C and air velocities of 0.6, 1.2, and 1.8 m/s. The thermal parameters were determined using thermodynamic relations. Increasing air temperature and air velocity increased the effective moisture diffusivity (D eff ), E u , EUR, exergy efficiency, and exergy loss. The value of the D eff was varied from 4.19 × 10 -10 to 1.18 × 10 -9 m 2 /s. The highest value E u , EUR, and exergy loss and exergy efficiency were calculated 0.0694 kJ/s, 0.882, 0.044 kJ/s, and 0.879, respectively. Midilli et al. model, ANNs, and ANFIS model, with a determination coefficient (R 2 ) of .9992, .9993, and .9997, provided the best performance for predicting the moisture ratio of quince fruit. Also, the ANFIS model, in comparison with the artificial neural networks model, was better able to predict E u , EUR, exergy efficiency, and exergy loss, with R 2 of .9989, .9988, .9986, and .9978, respectively. K E Y W O R D S adaptive neuro-fuzzy inference system, artificial neural networks, drying, quince, thermodynamic parameters | 595 ABBASPOUR-GILANDEH Et AL.focusing on energy and exergy analysis is very important (Lingayat, Chandramohan, & Raju, 2018;Yogendrasasidhar & Setty, 2018).Şevik, Aktaş, Dolgun, Arslan, and Tuncer (2019) analyzed the exergy and energy in the process of drying mint and apple slices in a solar and solar-infrared. The results indicated that the loss of exergy and exergy efficiency increases by increasing the air temperature. Exergy efficiency for mint in solar dryer and solar-infrared was 69.35% and 59. 07%, respectively. Akpinar, Midilli, and Bicer (2006) analyzed the energy and exergy in pumpkin. They reported that the pumpkin dried within the time range of 5.66-12 hr with a loss of exergy from 0 to 1.165 kJ/s. The maximum exergy of the system input was 2.198 kJ/s. Also, with increased exergy loss, the energy used in the solar dryer increased. Karthikeyan and Murugavelh (2018) studied the energy and exergy required to dry turmeric in a mixed-mode forced convection solar tunnel dryer and concluded that the loss of exergy and energy utilization ratio and its efficiency was increased with increasing temperature.

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“…From the different working fluids, 1% Ag hybrid nanofluid (0.5% Al 2 O 3 and 0.5% Ag) allowed to obtain the highest values of thermal enhancement, effectiveness, and pumping power. Savari et al 105 developed a fundamental correlation for predicting the Nusselt number of the water/EG-based nanofluids, including graphene or nitrogen-doped graphene.…”

mentioning

confidence: 99%

Advances of nanofluids in heat exchangers—A review

Menni

Chamkha

Ameur³

2020

Heat Trans

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Recently, many researchers have focused on their studies on the analysis of nanofluid flows due to their participation in the enhancement of heat transfer rates in industrial processes. The ordinary fluids, such as water, mineral oils, and so on, are known for their low thermal conductivity in heat transfer processes. A significant enhancement in the thermal properties of ordinary fluid may be obtained by adding nanoparticles having a diameter of less than 100 nm or suspension of fibers. Better spreading, wetting, dispersion, and stability and with acceptable viscosity are the main advantageous properties of nanofluids on a solid surface. The nanofluids are encountered in various thermal engineering systems such as in heat exchangers, refrigeration, thermal management of fuel cells, cooling of nuclear reactors, microelectromechanical systems, and others. In particular, the thermal conversion is known as a great application of nanotechnology, and many studies have been achieved with such fluids in heat exchangers. Therefore, this paper aims to present a global insight into the different applications of nanofluids in various heat exchangers, that is, heat pipe and plate-fin heat exchangers. All research works have been summarized into three main parts: laminar, transition, and turbulent nanofluid flow regimes.

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Comprehensive heat transfer correlation for water/ethylene glycol-based graphene (nitrogen-doped graphene) nanofluids derived by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

Cited by 16 publications

References 31 publications

Assessment of kinetics, effective moisture diffusivity, specific energy consumption, shrinkage, and color in the pistachio kernel drying process in microwave drying with ultrasonic pretreatment

Assessment of kinetics, effective moisture diffusivity, specific energy consumption, shrinkage, and color in the pistachio kernel drying process in microwave drying with ultrasonic pretreatment

Prediction kinetic, energy and exergy of quince under hot air dryer using ANNs and ANFIS

Advances of nanofluids in heat exchangers—A review

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