Liquid density prediction of five different classes of refrigerant systems (HCFCs, HFCs, HFEs, PFAs and PFAAs) using the artificial neural network-group contribution method

Moosavi, Majid; Sedghamiz, Elaheh; Abareshi, Maryam

doi:10.1016/j.ijrefrig.2014.09.007

Cited by 18 publications

(6 citation statements)

References 64 publications

(52 reference statements)

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“…The input parameters of the BP neural network are molecular descriptors, and the output parameters are prediction properties. The proportions of the training, validation and test sets were 70%, 15%, and 15%, respectively [38]. The transfer functions of the hidden and output layers of the BP neural network are tansig and purelin.…”

Section: Model Establishment and Evaluationmentioning

confidence: 99%

Performance Prediction and Working Fluid Active Design of Organic Rankine Cycle Based on Molecular Structure

et al. 2022

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Working fluid selection is crucial for organic Rankine cycles (ORC). In this study, the relationship between molecular structure and ORC performance was established based on the quantitative structure–property relationship (QSPR) and working fluid parameterized model (WFPM), from which an ORC working fluid was actively designed. First, the QSPR model with four properties, namely, critical temperature (Tc), boiling point (Tb), critical pressure (pc), and isobaric heat capacity (cp0), was built. Second, the evaporation enthalpy (hvap), evaporation entropy (svap), and thermal efficiency (η) were estimated by WFPM, and the results were compared with those using REFPROP to verify the calculation accuracy of the “QSPR+WFPM” coupling model. The average absolute relative deviations of evaporation enthalpy and entropy are below 8.44%. The maximum relative error of thermal efficiency is 6%. Then, the thermodynamic performance limit of ORC and corresponding thermophysical properties of the ideal working fluid were calculated at typical geothermal source conditions. Finally, the active design of the working fluid was conducted with the ideal working fluid Tc and pc as the target. The research shows that C3H4F2 and C4H3F5 are optimal working fluids at 473.15 and 523.15 K heat sources, respectively.

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Section: Model Establishment and Evaluationmentioning

confidence: 99%

Performance Prediction and Working Fluid Active Design of Organic Rankine Cycle Based on Molecular Structure

et al. 2022

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“…Due to the serious global warming effect of HFCs, HFEs were introduced as a new generation of refrigerants by RITE 16 , 17 . Also, there are some compounds that have the potential of using refrigerant fluids such as perfluoroalkylalkanes (PFAAs) and perfluoroalkanes (PFAs) 18 . We do not have detailed studies that predict the thermodynamic properties of refrigerants by theoretical methods; so every author uses a special equation and method to forecast the thermodynamic properties of the refrigerant systems 19 , 20 .…”

Section: Introductionmentioning

confidence: 99%

Insights into the prediction of the liquid density of refrigerant systems by artificial intelligent approaches

Li,

Baghban

2024

Sci Rep

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This study presents a novel model for accurately estimating the densities of 48 refrigerant systems, categorized into five groups: Hydrofluoroethers (HFEs), Hydrochlorofluorocarbons (HCFCs), Perfluoroalkylalkanes (PFAAs), Hydrofluorocarbons (HFCs), and Perfluoroalkanes (PFAs). Input variables, including pressure, temperature, molecular weight, and structural groups, were systematically considered. The study explores the efficacy of both the multilayer perceptron artificial neural network (MLP-ANN) and adaptive neuro-fuzzy inference system (ANFIS) methodologies in constructing a precise model. Utilizing a comprehensive dataset of 3825 liquid density measurements and outlier analysis, the models achieved R2 and MSE values of 0.975 & 0.5575 and 0.967 & 0.7337 for MLP-ANN and ANFIS, respectively, highlighting their remarkable predictive performance. In conclusion, the ANFIS model is proposed as an effective tool for estimating refrigerant system densities, particularly advantageous in scenarios where experimental measurements are resource-intensive or sophisticated analysis is required.

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“…1,5−8 Moreover, some empirical and semiempirical methods such as corresponding-state principle and multiparameter correlation have also been used to predict the thermal conductivity. 9−18 Recently, many researchers have employed artificial neural networks (ANN) to correlate the thermal properties, such as surface tension, 19−24 density, 25,26 and in particular thermal conductivity. 3,4,27−32 Karabulut and Koyuncu 32 exploited the feed-forward ANN model to develop thermal conductivity correlations of propane for the first time.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many researchers have employed artificial neural networks (ANN) to correlate the thermal properties, such as surface tension, 19 − 24 density, 25 , 26 and in particular thermal conductivity. 3 , 4 , 27 − 32 Karabulut and Koyuncu 32 exploited the feed-forward ANN model to develop thermal conductivity correlations of propane for the first time.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, many researchers have employed artificial neural networks (ANN) to correlate the thermal properties, such as surface tension, − density, , and in particular thermal conductivity. ,,− Karabulut and Koyuncu exploited the feed-forward ANN model to develop thermal conductivity correlations of propane for the first time. Later, Eslamloueyan and Khademi developed an ANN model to reproduce and predict thermal conductivity at atmospheric pressure for various pure gases.…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Model for the Prediction of Thermal Conductivity of Saturated Liquid Refrigerants and n-Alkanes

2022

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In this paper, a feed-forward back-propagation artificial neural network (ANN) is proposed to correlate and predict the thermal conductivity from the triple point temperature up to 0.98 times critical temperature (T c) for 23 refrigerants and 11 n-alkanes. It requires the temperature (T) as well as the molecular mass (M), acentric factor (ω), critical temperature, and critical pressure (P c) as input variables. The optimal ANN model is obtained by a trial-and-error procedure and consists of the input layer and the output layer together with one hidden layer with seven neurons. This ANN model can not only correlate the thermal conductivity but also accurately predict the thermal conductivity of refrigerants and n-alkanes. The correlation coefficients (R) in the training and testing phases are 0.9994 and 0.9993, respectively. Furthermore, the average absolute deviation (AAD) values are less than 1% for 14 out of 34 fluids, less than 2% for 28 fluids, and less than 4.5% for all the considered fluids.

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Liquid density prediction of five different classes of refrigerant systems (HCFCs, HFCs, HFEs, PFAs and PFAAs) using the artificial neural network-group contribution method

Cited by 18 publications

References 64 publications

Performance Prediction and Working Fluid Active Design of Organic Rankine Cycle Based on Molecular Structure

Performance Prediction and Working Fluid Active Design of Organic Rankine Cycle Based on Molecular Structure

Insights into the prediction of the liquid density of refrigerant systems by artificial intelligent approaches

Artificial Neural Network Model for the Prediction of Thermal Conductivity of Saturated Liquid Refrigerants and n-Alkanes

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