Data-driven methods for estimating the effective thermal conductivity of nanofluids: A comprehensive review

Zendehboudi, Alireza; Saidur, R.; Mahbubul, I.M.; Hosseini, Seyyed Hossein

doi:10.1016/j.ijheatmasstransfer.2018.11.053

Cited by 49 publications

(16 citation statements)

References 97 publications

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“…Furthermore, by increasing the Reynolds number and thus the mass transfer coefficient of ions ( Figure 7 ), the polarization concentration becomes smaller. Therefore, a higher feed flow rate minimizes the effect of concentration polarization by sweeping the ions from the membrane surface [ 60 , 73 , 74 ]. Conversely, with decreasing the feed flow rate, the mass transfer coefficient decreases and the concentration polarization increases on the membrane surface.…”

Section: Resultsmentioning

confidence: 99%

“…The static error metrics, including the average absolute relative error, AARE, and the coefficient of determination, R 2 , are applied to evaluate the validity of the results. AARE and R 2 were determinate as follows [ 57 , 58 , 59 , 60 ]:

where R i exp , R i calc ,

, n are the rejection of the species “ i ” for the experimental data, the rejection predicted for the species “ i ”, the average value of the rejection and the number of total data, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Process Modelingmentioning

confidence: 99%

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Modeling of Nanofiltration Process Using DSPM-DE Model for Purification of Amine Solution

et al. 2021

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The formation of heat stable salts (HSS) during the natural gas sweetening process by amine solvent causes many problems such as corrosion, foaming, capacity reduction, and amine loss. A modeling study was carried out for the removal of HSS ions from amine solution using nanofiltration (NF) membrane process that ensures the reuse of amine solution for gas sweetening. This model studies the physics of the nanofiltration process by adjusting and investigating pore radius, the effects of membrane charge, and other membrane characteristics. In this paper, the performance of the ternary ions was investigated during the removal process from methyl di-ethanol amine solution by the nanofiltration membrane process. Correlation between feed concentration and permeate concentration, using experimental results with mathematical correlation as Ci,p = f (Ci,f) was used in modeling. The results showed that the calculated data from the model provided a good agreement with experimental results (R2 = 0.90–0.75). Also, the effect of operating conditions (including feed pressure and feed flow rate on ions rejection and recovery ratio across the flat-sheet membrane) was studied. The results showed that the recovery and rejection ratios of the NF membrane depend on the driving pressure across the membrane. While the driving pressure is affected by the feed flow conditions and feed pressure.

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Section: Resultsmentioning

confidence: 99%

where R i exp , R i calc ,

Section: Methodsmentioning

confidence: 99%

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Modeling of Nanofiltration Process Using DSPM-DE Model for Purification of Amine Solution

et al. 2021

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“…An ANN, which is a kind of intelligence model, is proficient at adapting to changes in the environment, learning from experience, and improving its performance [57][58][59]. An ANN is composed of neurons.…”

Section: Theory Of An Annmentioning

confidence: 99%

Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

et al. 2020

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This work investigated the capability of multilayer perceptron artificial neural network (MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network (RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature (Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids (ILs). To this end, a comprehensive database of literature reviews was searched. The results of the SGB model were more satisfactory than the other models. Furthermore, an analysis was done to determine the outlying bad data points. It showed that most of the experimental data points were located in a reliable zone for the development of the model. The mean squared error and R2 were 0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN, ANFIS, and RBF–ANN, respectively. According to this study, the ability of SGB for estimating the Cp of ionanofluids was shown to be greater than other models. By eliminating the need for conducting costly and time-consuming experiments, the SGB strategy showed its superiority compared with experimental measurements. Furthermore, the SGB displayed great generalizability because of the stochastic element. Therefore, it can be highly applicable to unseen conditions. Furthermore, it can help chemical engineers and chemists by providing a model with low parameters that yields satisfactory results for estimating the Cp of ionanofluids. Additionally, the sensitivity analysis showed that Cp is directly related to T, Mw, and Tc, and has an inverse relation with ω and x. Mw and Tc had the highest impact and ω had the lowest impact on Cp.

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“…The neural network based on Multilayer Perceptron (MLP) has been applied in many engineering applications to study the thermal conductivity of Nanofluids, and a comprehensive review is given by Bahiraei et al [15]. In another study, Zendehboudi et al [16] compiled and compared different neural network applied to estimate the thermal conductivity of Nanofluids. However, very few studies have been undertaken for determining the effective thermal conductivity of granular solids [12,17].…”

Section: Introductionmentioning

confidence: 99%

Soft and hard computation methods for estimation of the effective thermal conductivity of sands

et al. 2020

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Thermal properties of sand are of importance in numerous engineering and scientific applications ranging from energy storage and transportation infrastructures to underground construction. All these applications require knowledge of the effective thermal parameters for proper operation. The traditional approaches for determination of the effective thermal property, such as the thermal conductivity are based on very costly, tedious and time-consuming experiments. The recent developments in computer science have allowed the use of soft and hard computational methods to compute the effective thermal conductivity (ETC). Here, two computation methods are presented based on soft and hard computing approaches, namely, the deep neural network (DNN) and the thermal lattice element method (TLEM), respectively, to compute the ETC of sands with varying porosity and moisture content values. The developed models are verified and validated with a small data set reported in the literature. The computation results are compared with the experiments, and the numerical results are found to be within reasonable error bounds. The deep learning method offers fast and robust implementation and computation, even with a small data set due to its superior backpropagation algorithm. However, the TLEM based on micro and meso physical laws outperforms it at accuracy.

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Data-driven methods for estimating the effective thermal conductivity of nanofluids: A comprehensive review

Cited by 49 publications

References 97 publications

Modeling of Nanofiltration Process Using DSPM-DE Model for Purification of Amine Solution

Modeling of Nanofiltration Process Using DSPM-DE Model for Purification of Amine Solution

Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

Soft and hard computation methods for estimation of the effective thermal conductivity of sands

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