Electrical efficiency of the photovoltaic/thermal collectors cooled by nanofluids: Machine learning simulation and optimization by evolutionary algorithm

Cao, Yan; Kamrani, Elham; Mirzaei, Saeid; Khandakar, Amith; Vaferi, Behzad

doi:10.1016/j.egyr.2021.11.252

Cited by 90 publications

(19 citation statements)

References 72 publications

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“…After determining the general form of the proposed correlation, it is now necessary to adjust its coefficients using an appropriate method. The differential evolution (DE) optimization algorithm 69 , 70 is employed to adjust these unknown coefficients through a non-linear regression process. The absolute average relative deviation (AARD%) between the model predictions and actual measurements is an objective function for the optimization stage.…”

Section: Resultsmentioning

confidence: 99%

Developing an accurate empirical correlation for predicting anti-cancer drugs’ dissolution in supercritical carbon dioxide

Faress

Yari

Kouchi

et al. 2022

Sci Rep

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This study introduces a universal correlation based on the modified version of the Arrhenius equation to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide (CO2). A combination of an Arrhenius-shape term and a departure function was proposed to estimate the solubility of anti-cancer drugs in supercritical CO2. This modified Arrhenius correlation predicts the solubility of anti-cancer drugs in supercritical CO2 from pressure, temperature, and carbon dioxide density. The pre-exponential of the Arrhenius linearly relates to the temperature and carbon dioxide density, and its exponential term is an inverse function of pressure. Moreover, the departure function linearly correlates with the natural logarithm of the ratio of carbon dioxide density to the temperature. The reliability of the proposed correlation is validated using all literature data for solubility of anti-cancer drugs in supercritical CO2. Furthermore, the predictive performance of the modified Arrhenius correlation is compared with ten available empirical correlations in the literature. Our developed correlation presents the absolute average relative deviation (AARD) of 9.54% for predicting 316 experimental measurements. On the other hand, the most accurate correlation in the literature presents the AARD = 14.90% over the same database. Indeed, 56.2% accuracy improvement in the solubility prediction of the anti-cancer drugs in supercritical CO2 is the primary outcome of the current study.

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Section: Resultsmentioning

confidence: 99%

Developing an accurate empirical correlation for predicting anti-cancer drugs’ dissolution in supercritical carbon dioxide

Faress

Yari

Kouchi

et al. 2022

Sci Rep

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“…The fundamentals subject to use of ANN model can be assessed in refs. [60][61][62]. In this regard, the hybrid meshing is used to get the numerical values of drag coefficient.…”

Section: Results Analysismentioning

confidence: 99%

Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles

2022

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For various obstacles in the path of a flowing liquid stream, an artificial neural networking (ANN) model is constructed to study the hydrodynamic force depending on the object. The multilayer perceptron (MLP), back propagation (BP), and feed-forward (FF) network models were employed to create the ANN model, which has a high prediction accuracy and a strong structure. To be more specific, circular-, octagon-, hexagon-, square-, and triangular-shaped cylinders are installed in a rectangular channel. The fluid is flowing from the left wall of the channel by following two velocity profiles explicitly linear velocity and parabolic velocity. The no-slip condition is maintained on the channel upper and bottom walls. The Neumann condition is applied to the outlet. The entire physical design is mathematically regulated using flow equations. The result is presented using the finite element approach, with the LBB-stable finite element pair and a hybrid meshing scheme. The drag coefficient values are calculated by doing line integration around installed obstructions for both linear and parabolic profiles. The values of the drag coefficient are predicted with high accuracy by developing an ANN model toward various obstacles.

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“…In these conditions, the oil samples of 23 and 71 not only have appropriate viscoelastic behavior, but they also experience 97.2% and 96.3% reductions in their viscosity, respectively. These days, crude oil-derived fuels have undeniable roles in providing domestic and industrial activities with their energy demand 1 . Increasing demand for fossil fuels leads to conducting so many studies on different aspects of the petroleum reservoirs, such as drilling 2 , fluid extraction 3,4 , pressure maintenance 5 and analysis [6][7][8] , formation characterization 9 , enhanced oil recovery [10][11][12] .…”

Section: Experimental Analyzing the Effect Of N-heptane Concentration...mentioning

confidence: 99%

Experimental analyzing the effect of n-heptane concentration and angular frequency on the viscoelastic behavior of crude oil containing asphaltene

Fazeli

Escrochi

Hosseini

et al. 2022

Sci Rep

Self Cite

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Asphaltene often produces problems in upstream and downstream sections of crude oil transportation and processing equipment. These issues are directly related to the asphaltene precipitation in transportation pipelines, separation columns, heat exchangers, and storage tanks. This research investigates the impact of angular frequency and n-heptane concentration on asphaltene precipitation and rheological behavior of two oil samples from the Mansouri oil field in Iran, i.e., 23 and 71. The viscosity tests revealed that these oil samples and their mixtures with n-heptane exhibit Newtonian behavior. Moreover, increasing the n-heptane concentration increases the asphaltene precipitation and dramatically decreases crude oil viscosity. The frequency tests revealed that the presence of n-heptane has an unfavorable effect on crude oil’s viscoelastic behavior. Therefore, it is necessary to find the optimum range of angular frequency and n-heptane concentration to minimize the asphaltene content of crude oil and provide them with appropriate viscoelastic behavior. Increasing the angular frequency continuously increases all oil samples’ loss modulus and strengthens their liquid-like manner. The experimental results confirmed that the angular frequency higher than 33.6 rad/s and 75% volume concentration of n-heptane is the best condition for the oil sample of 23. On the other hand, the angular frequency higher than 23.4 rad/s and 75% volume concentration of n-heptane is the best condition for the oil sample of 71. In these conditions, the oil samples of 23 and 71 not only have appropriate viscoelastic behavior, but they also experience 97.2% and 96.3% reductions in their viscosity, respectively.

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Electrical efficiency of the photovoltaic/thermal collectors cooled by nanofluids: Machine learning simulation and optimization by evolutionary algorithm

Cited by 90 publications

References 72 publications

Developing an accurate empirical correlation for predicting anti-cancer drugs’ dissolution in supercritical carbon dioxide

Developing an accurate empirical correlation for predicting anti-cancer drugs’ dissolution in supercritical carbon dioxide

Artificial Neural Networking (ANN) Model for Drag Coefficient Optimization for Various Obstacles

Experimental analyzing the effect of n-heptane concentration and angular frequency on the viscoelastic behavior of crude oil containing asphaltene

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