SCI和EI收录∣中国化工学会会刊

Chinese Journal of Chemical Engineering ›› 2018, Vol. 26 ›› Issue (4): 740-746.DOI: 10.1016/j.cjche.2017.10.002

• Separation Science and Engineering • 上一篇    下一篇

A robust predictive tool for estimating CO2 solubility in potassium based amino acid salt solutions

Ebrahim Soroush1, Shohreh Shahsavari2, Mohammad Mesbah3, Mashallah Rezakazemi4, Zhi'en Zhang5,6   

  1. 1 Young Researchers and Elites Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran;
    2 Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran;
    3 Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran;
    4 Department of Chemical Engineering, Shahrood University of Technology, Shahrood, Iran;
    5 School of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, China;
    6 Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education of China, Chongqing University, Chongqing 400044, China
  • 收稿日期:2017-05-02 修回日期:2017-10-15 出版日期:2018-04-28 发布日期:2018-05-19
  • 通讯作者: Mohammad Mesbah,E-mail address:m_mesbah@alum.sharif.edu

A robust predictive tool for estimating CO2 solubility in potassium based amino acid salt solutions

Ebrahim Soroush1, Shohreh Shahsavari2, Mohammad Mesbah3, Mashallah Rezakazemi4, Zhi'en Zhang5,6   

  1. 1 Young Researchers and Elites Club, Ahvaz Branch, Islamic Azad University, Ahvaz, Iran;
    2 Department of Chemical Engineering, Sahand University of Technology, Tabriz, Iran;
    3 Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran, Iran;
    4 Department of Chemical Engineering, Shahrood University of Technology, Shahrood, Iran;
    5 School of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, China;
    6 Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education of China, Chongqing University, Chongqing 400044, China
  • Received:2017-05-02 Revised:2017-10-15 Online:2018-04-28 Published:2018-05-19
  • Contact: Mohammad Mesbah,E-mail address:m_mesbah@alum.sharif.edu

摘要: The acid gas absorption in four potassium based amino acid salt solutions was predicted using artificial neural network (ANN). Two hundred fifty-five experimental data points for CO2 absorption in the four potassium based amino acid salt solutions containing potassium lysinate, potassium prolinate, potassium glycinate, and potassium taurate were used in this modeling. Amine salt solution's type, temperature, equilibrium partial pressure of acid gas, the molar concentration of the solution, molecular weight, and the boiling point were considered as inputs to ANN to prognosticate the capacity of amino acid salt solution to absorb acid gas. Regression analysis was employed to assess the performance of the network. Levenberg-Marquardt back-propagation algorithm was used to train the optimal ANN with 5:12:1 architecture. The model findings indicated that the proposed ANN has the capability to predict precisely the absorption of acid gases in various amino acid salt solutions with Mean Square Error (MSE) value of 0.0011, the Average Absolute Relative Deviation (AARD) percent of 5.54%, and the correlation coefficient (R2) of 0.9828.

关键词: Amino acid salt solutions, Acid gas absorption, Neural network, CO2 capture

Abstract: The acid gas absorption in four potassium based amino acid salt solutions was predicted using artificial neural network (ANN). Two hundred fifty-five experimental data points for CO2 absorption in the four potassium based amino acid salt solutions containing potassium lysinate, potassium prolinate, potassium glycinate, and potassium taurate were used in this modeling. Amine salt solution's type, temperature, equilibrium partial pressure of acid gas, the molar concentration of the solution, molecular weight, and the boiling point were considered as inputs to ANN to prognosticate the capacity of amino acid salt solution to absorb acid gas. Regression analysis was employed to assess the performance of the network. Levenberg-Marquardt back-propagation algorithm was used to train the optimal ANN with 5:12:1 architecture. The model findings indicated that the proposed ANN has the capability to predict precisely the absorption of acid gases in various amino acid salt solutions with Mean Square Error (MSE) value of 0.0011, the Average Absolute Relative Deviation (AARD) percent of 5.54%, and the correlation coefficient (R2) of 0.9828.

Key words: Amino acid salt solutions, Acid gas absorption, Neural network, CO2 capture