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A novel statistical approach for prediction of thermal conductivity of CO2 by Response Surface Methodology

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  • Rostamian, Hossein
  • Lotfollahi, Mohammad Nader

Abstract

In the present article, a novel statistical approach was developed to accurately predict the thermal conductivity of CO2 using response surface methodology (RSM). The Artificial Neural Network (ANN) was also used for the modeling of the thermal conductivity of CO2. To develop the proposed models, 1910 experimental data were used including pressure, temperature and density of carbon dioxide as the input variables in the models. The performance of the models was fortified using statistical analysis and the values of 0.57, 1.25, 0.99993 and 0.99931 were obtained for AARD% and R2 of ANN and RSM models, respectively. The obtained results were compared with four conventional models to investigate the ANN and RSM accuracy. The results showed that the developed models were useful to predict CO2 thermal conductivity at wide ranges of temperature and pressure. It was found that the developed ANN model gives the best fit and satisfactory agreement with the experimental data. Also the proposed correlation presents higher accuracy compared with all previous correlations for prediction of CO2 thermal conductivity at different condition.

Suggested Citation

  • Rostamian, Hossein & Lotfollahi, Mohammad Nader, 2019. "A novel statistical approach for prediction of thermal conductivity of CO2 by Response Surface Methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s037843711930706x
    DOI: 10.1016/j.physa.2019.121175
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    Cited by:

    1. Peng, Yeping & Khaled, Usama & Al-Rashed, Abdullah A.A.A. & Meer, Rashid & Goodarzi, Marjan & Sarafraz, M.M., 2020. "Potential application of Response Surface Methodology (RSM) for the prediction and optimization of thermal conductivity of aqueous CuO (II) nanofluid: A statistical approach and experimental validatio," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    2. Arani, Ali Akbar Abbasian & Alirezaie, Ali & Kamyab, Mohammad Hassan & Motallebi, Sayyid Majid, 2020. "Statistical analysis of enriched water heat transfer with various sizes of MgO nanoparticles using artificial neural networks modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    3. Rostamian, Hossein & Lotfollahi, Mohammad Nader, 2020. "Statistical modeling of aspirin solubility in organic solvents by Response Surface Methodology and Artificial Neural Networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).

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