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Computational Simulation of CO2 Sorption in Polymeric Membranes Using Genetic Programming

  • Research Article-Chemical Engineering
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Abstract

A statistical model based on genetic programming was developed to study the solubility of CO2 in different polymers including polystyrene, poly(vinyl acetate), polybutylene succinate and poly(butylene succinate-co-adipate). The proposed genetic model can predict the CO2 solubility with the average relative deviation of 0.095, 0.0503, 0.0312, 0.039% and R2 values of greater than 0.98. The results showed efficient applicability of the model and its outperformance in predicting the CO2 solubility compared with other modeling approaches. Therefore, the proposed model contributes to enhancing better understanding of gas/polymer systems and aids in alleviating the difficulties arising in the prediction of gas solubilities during the design and optimization of the relevant processes.

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Abbreviations

ANN:

Artificial neural network

ARD:

Average relative deviation

CK-SOFT:

Original statistical associating fluid theory

EDMS:

Extended dual-mode sorption

EOSs:

Equation of states

GA:

Genetic algorithm

GP:

Genetic programming

MAE:

Mean absolute error

MSB:

Magnetic suspension balance

MSE:

Mean square error

PBS:

Polybutylene succinate

PBSA:

Poly(butylene succinate-co-adipate)

PC-SAFT:

Perturbed-chain SAFT

PPO:

2,6-Dimethyl-1,4-phenylene ether

PS:

Polystyrene

PVAc:

Poly(vinyl acetate)

R 2 :

Squared correlation coefficient

SAFT:

Statistical associating fluid theories

SL:

Sanchez–Lacombe

SS:

Simha–Somcynsky

STD:

Standard deviation

SWP:

Sako–Wu–Prausinitz

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Authors and Affiliations

  1. Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Amir Dashti

  2. Separation Processes Research Group (SPRG), Department of Engineering, University of Kashan, Kashan, Iran

    Mojtaba Raji

  3. Department of Mechanical Engineering, Curtin University, Perth, WA, Australia

    Abouzar Azarafza

  4. Fluid Research Group and Curtin Institute for Computation, Curtin University, Perth, WA, Australia

    Abouzar Azarafza

  5. Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran

    Mashallah Rezakazemi

  6. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Saeed Shirazian

  7. Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Saeed Shirazian

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  1. Amir Dashti
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  2. Mojtaba Raji
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  3. Abouzar Azarafza
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  4. Mashallah Rezakazemi
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  5. Saeed Shirazian
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Correspondence to Saeed Shirazian.

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Dashti, A., Raji, M., Azarafza, A. et al. Computational Simulation of CO2 Sorption in Polymeric Membranes Using Genetic Programming. Arab J Sci Eng 45, 7655–7666 (2020). https://doi.org/10.1007/s13369-020-04783-1

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