Artificial neural network (ANN) modeling of thermal conductivity of supercritical ethane

ABSTRACT

In this present contribution, thermal conductivity ofethene (TCE) above the critical temperature has been studied. The present data cover the temperature range from 283.46 to 425.00 K and the pressure range from 0.1 to 100 MPa. In the present investigation, various network-based strategies, named as artificial neural network (ANN) optimized with two evolutionary algorithms, including genetic algorithm (GA) and differential evolution (DE), were developed for assessing tTCE in supercritical region. The most comprehensive source of data, including around 256 experimental points, was utilized for ANN modeling. Data index plot, scatter plot, relative deviation diagram and root mean square error (RMSE), and coefficient of determination (R²) as the statistical parameters were used in this examination to evaluate the comprehensiveness of the developed ANN model. Results indicate that the GA-ANN is more accurate than DE-ANN to predict TCE in supercritical region. Also, among optimization algorithms, GA has the largest ability for optimizing the ANN network modeling with the RMSE of 4.2966 and determination coefficient (R²) of 0.9640.

KEYWORDS:

• Artificial neural network modeling
• differential evolution
• genetic algorithm
• supercritical ethene
• thermal conductivity

Acknowledgments

Shanxi Province Science and Technology Tackling Key Project (No. 201603D121006-1); Shanxi Scholarship Council of China (No.2016-084).