A robust predictive tool for estimating CO<sub>2</sub> solubility in potassium based amino acid salt solutions

Ebrahim Soroush; Shohreh Shahsavari; Mohammad Mesbah; Mashallah Rezakazemi; Zhi'en Zhang

doi:10.1016/j.cjche.2017.10.002

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

Separation Science and Engineering | Updated：2026-01-06

- A robust predictive tool for estimating CO₂ solubility in potassium based amino acid salt solutions
- A robust predictive tool for estimating CO₂ solubility in potassium based amino acid salt solutions
- 中国化学工程学报（英文版） 2018年26卷第4期页码：740-746
- Affiliations：
  
  1. 2 Department of Chemical Engineering, Sahand University of Technology, Tabriz,Iran
  2. 3 Young Researchers and Elites Club, Science and Research Branch, Islamic Azad University, Tehran,Iran
  3. 4 Department of Chemical Engineering, Shahrood University of Technology, Shahrood,Iran
  4. 5 School of Chemistry and Chemical Engineering, Chongqing University of Technology,Chongqing,China,400054
  5. 6 Key Laboratory of Low-grade Energy Utilization Technologies and Systems, Ministry of Education of China, Chongqing University,Chongqing,China,400044
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2017.10.002
  中图分类号：
- 纸质出版：2018
- Accepted：
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Ebrahim Soroush, Shohreh Shahsavari, Mohammad Mesbah, 等. A robust predictive tool for estimating CO₂ solubility in potassium based amino acid salt solutions[J]. 中国化学工程学报（英文版）, 2018,26(4):740-746. DOI: 10.1016/j.cjche.2017.10.002.

solubility in potassium based amino acid salt solutions[J]. Chinese Journal of Chemical Engineering, 2018, 26(4): 740-746.
Ebrahim Soroush, Shohreh Shahsavari, Mohammad Mesbah, 等. A robust predictive tool for estimating CO₂ solubility in potassium based amino acid salt solutions[J]. 中国化学工程学报（英文版）, 2018,26(4):740-746. DOI: 10.1016/j.cjche.2017.10.002. DOI：

solubility in potassium based amino acid salt solutions[J]. Chinese Journal of Chemical Engineering, 2018, 26(4): 740-746. DOI： 10.1016/j.cjche.2017.10.002.

摘要

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 CO

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 (R

) of 0.9828.

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

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

DOI：10.1016/j.cjche.2017.10.002

摘要

Abstract

关键词

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references

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

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