Neural network-based correlations for the thermal conductivity of propane

Karabulut, Elife; Koyuncu, Mustafa

doi:10.1016/j.fluid.2007.04.024

Cited by 2 publications

(2 citation statements)

References 26 publications

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“…Mohammadi and Richon [23] used ANN to predict the enthalpy of vaporization of hydrocarbons, especially heavy hydrocarbons and petroleum fractions from the specific gravity and normal boiling temperatures. Karabulut and Koyuncu [24] developed neural network models to establish correlations of thermal conductivity with temperature and density for propane. Giordani et al [25] used ANN for correlating a wider range of properties, principally mechanical properties of modified natural rubber.…”

Section: Methodological Choices For Exploring Property Correlationsmentioning

confidence: 99%

Exploring Correlations Between Properties Using Artificial Neural Networks

Zhang

Evans

Yang

2019

Metall Mater Trans A

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The traditional aim of materials science is to establish the causal relationships between composition, processing, structure, and properties with the intention that, eventually, these relationships will make it possible to design materials to meet specifications. This paper explores another approach. If properties are related to structure at different scales, there may be relationships between properties that can be discerned and used to make predictions so that knowledge of some properties in a compositional field can be used to predict others. We use the physical properties of the elements as a dataset because it is expected to be both extensive and reliable and we explore this method by showing how it can be applied to predict the polarizability of the elements from other properties.

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Section: Methodological Choices For Exploring Property Correlationsmentioning

confidence: 99%

Exploring Correlations Between Properties Using Artificial Neural Networks

Zhang

Evans

Yang

2019

Metall Mater Trans A

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“…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%

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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Neural network-based correlations for the thermal conductivity of propane

Cited by 2 publications

References 26 publications

Exploring Correlations Between Properties Using Artificial Neural Networks

Exploring Correlations Between Properties Using Artificial Neural Networks

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

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