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

doi:10.1016/j.cjche.2017.10.002

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 Zhang^5,6

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 Zhang^5,6

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/Abstract

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

Ebrahim Soroush, Shohreh Shahsavari, Mohammad Mesbah, Mashallah Rezakazemi, Zhi'en Zhang. A robust predictive tool for estimating CO₂ solubility in potassium based amino acid salt solutions[J]. Chinese Journal of Chemical Engineering, 2018, 26(4): 740-746.

参考文献

[1] S. Shirazian, A. Marjani, M. Rezakazemi, Separation of CO₂ by single and mixed aqueous amine solvents in membrane contactors:fluid flow and mass transfer modeling, Eng. Comput. 28(2) (2012) 189-198.

[2] M. Rezakazemi, A.E. Amooghin, M.M. Montazer-Rahmati, et al., State-of-the-art membrane based CO₂ separation using mixed matrix membranes (MMMs):an overview on current status and future directions, Prog. Polym. Sci. 39(5) (2014) 817-861.

[3] S.M.R. Razavi, et al., Simulation of CO₂ absorption by solution of ammonium ionic liquid in hollow-fiber contactors, Chem. Eng. Process. Process Intensif. 108(2016) 27-34.

[4] M. Rezakazemi, I. Heydari, Z. Zhang, Hybrid systems:combining membrane and absorption technologies leads to more efficient acid gases (CO₂ and H₂S) removal from natural gas, J. CO₂ Util. 18(2017) 362-369.

[5] M. Fasihi, S. Shirazian, A. Marjani, et al., Computational fluid dynamics simulation of transport phenomena in ceramic membranes for SO₂ separation, Math. Comput. Model. 56(11-12) (2012) 278-286.

[6] A. Mansourizadeh, A.F. Ismail, CO₂ stripping from water through porous PVDF hollow fiber membrane contactor, Desalination 273(2) (2011) 386-390.

[7] M. Mesbah, et al., Mathematical modeling and numerical simulation of CO₂ removal by using hollow fiber membrane contactors, Iran. J. Oil Gas Sci. Technol. (2017)https://doi.org/10.22050/IJOGST.2017.48143(in press).

[8] S. Ma'mun, R. Nilsen, H.F. Svendsen, et al., Solubility of carbon dioxide in 30 mass% monoethanolamine and 50 mass% methyldiethanolamine solutions, J. Chem. Eng. Data 50(2) (2005) 630-634.

[9] S.D. Kenarsari, D. Yang, G. Jiang, et al., Review of recent advances in carbon dioxide separation and capture, RSC Adv. 3(45) (2013) 22739-22773.

[10] T. Payagala, D.W. Armstrong, Chiral ionic liquids:a compendium of syntheses and applications (2005-2012), Chirality 24(1) (2012) 17.

[11] P.S. Kumar, et al., Density, viscosity, solubility, and diffusivity of N₂O in aqueous amino acid salt solutions, J. Chem. Eng. Data 46(6) (2001) 1357-1361.

[12] P.S. Kumar, J.A. Hogendoorn, S.J. Timmer, et al., Equilibrium solubility of CO₂ in aqueous potassium taurate solutions:part 2. Experimental VLE data and model, Ind. Eng. Chem. Res. 42(12) (2003) 2841-2852.

[13] M. Rezakazemi, Z. Niazi, M. Mirfendereski, et al., CFD simulation of natural gas sweetening in a gas-liquid hollow-fiber membrane contactor, Chem. Eng. J. 168(3) (2011) 1217-1226.

[14] H.-J. Song, S. Lee, S. Maken, et al., Solubilities of carbon dioxide in aqueous solutions of sodium glycinate, Fluid Phase Equilib. 246(1) (2006) 1-5.

[15] D.M. Munoz, A.F. Portugal, A.E. Lozano, et al., New liquid absorbents for the removal of CO₂ from gas mixtures, Energy Environ. Sci. 2(8) (2009) 883-891.

[16] M. Majchrowicz, D. Brilman, Solubility of CO₂ in aqueous potassium L-prolinate solutions-absorber conditions, Chem. Eng. Sci. 72(2012) 35-44.

[17] S.C.-C. Wei, G. Puxty, P. Feron, Amino acid salts for CO₂ capture at flue gas temperatures, Energy Procedia 37(2013) 485-493.

[18] H.-J. Song, M.-G. Lee, H. Kim, et al., Density, viscosity, heat capacity, surface tension, and solubility of CO₂ in aqueous solutions of potassium serinate, J. Chem. Eng. Data 56(4) (2011) 1371-1377.

[19] U.E. Aronu, E.T. Hessen, T. Huang-Warberg, et al., Vapor-liquid equilibrium in amino acid salt system:experiments and modeling, Chem. Eng. Sci. 66(10) (2011) 2191-2198.

[20] R.L. Kent, B. Eisenberg, Better data for amine treating, Hydrocarb. Process. 55(2) (1976) 87-90.

[21] P.S. Kumar, J.A. Hogendoorn, P.H.M. Feron, et al., Equilibrium solubility of CO₂ in aqueous potassium taurate solutions:part 1. Crystallization in carbon dioxide loaded aqueous salt solutions of amino acids, Ind. Eng. Chem. Res. 42(12) (2003) 2832-2840.

[22] D.M. Austgen, et al., Model of vapor-liquid equilibria for aqueous acid gasalkanolamine systems using the electrolyte-NRTL equation, Ind. Eng. Chem. Res. 28(7) (1989) 1060-1073.

[23] S.L. Clegg, K.S. Pitzer, Thermodynamics of multicomponent, miscible, ionic solutions:generalized equations for symmetrical electrolytes, J. Phys. Chem. 96(8) (1992) 3513-3520.

[24] C.C. Chen, L.B. Evans, A local composition model for the excess Gibbs energy of aqueous electrolyte systems, AIChE J. 32(3) (1986) 444-454.

[25] R. Deshmukh, A. Mather, A mathematical model for equilibrium solubility of hydrogen sulfide and carbon dioxide in aqueous alkanolamine solutions, Chem. Eng. Sci. 36(2) (1981) 355-362.

[26] G. Cybenko, Approximation by superpositions of a sigmoidal function, Math. Control Signals Syst. 2(4) (1989) 303-314.

[27] M. Rezakazemi, S. Razavi, T. Mohammadi, et al., Simulation and determination of optimum conditions of pervaporative dehydration of isopropanol process using synthesized PVA-APTEOS/TEOS nanocomposite membranes by means of expert systems, J. Membr. Sci. 379(1-2) (2011) 224-232.

[28] M. Rostamizadeh, M. Rezakazemi, K. Shahidi, et al., Gas permeation through H₂-selective mixed matrix membranes:experimental and neural network modeling, Int. J. Hydrog. Energy 38(2) (2013) 1128-1135.

[29] M. Rezakazemi, T. Mohammadi, Gas sorption in H₂-selective mixed matrix membranes:experimental and neural network modeling, Int. J. Hydrog. Energy 38(32) (2013) 14035-14041.

[30] M. Rezakazemi, A. Dashti, M. Asghari, et al., H₂-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS, Int. J. Hydrog. Energy 42(22) (2017) 15211-15225.

[31] N. Azizi, M. Rezakazemi, M.M. Zarei, An intelligent approach to predict gas compressibility factor using neural network model, Neural Comput. & Applic. (2017) 1-10.

[32] S. Mazinani, R. Ramazani, A. Samsami, et al., Equilibrium solubility, density, viscosity and corrosion rate of carbon dioxide in potassium lysinate solution, Fluid Phase Equilib. 396(2015) 28-34.

[33] S. Shen, Y. Yang, Y. Wang, et al., CO₂ absorption into aqueous potassium salts of lysine and proline:density, viscosity and solubility of CO₂, Fluid Phase Equilib. 399(2015) 40-49.

[34] P.J. Rousseeuw, A.M. Leroy, Robust Regression and Outlier Detection, Vol. 589, John Wiley & Sons Inc., Hoboken, New Jersey, 2005.

[35] M. Mesbah, E. Soroush, V. Azari, et al., Vapor liquid equilibrium prediction of carbon dioxide and hydrocarbon systems using LSSVM algorithm, J. Supercrit. Fluids 97(2015) 256-267.

[36] M. Mesbah, E. Soroush, A. Shokrollahi, et al., Prediction of phase equilibrium of CO₂/cyclic compound binary mixtures using a rigorous modeling approach, J. Supercrit. Fluids 90(2014) 110-125.

[37] E. Soroush, M. Mesbah, A. Shokrollahi, et al., Prediction of methane uptake on different adsorbents in adsorbed natural gas technology using a rigorous model, Energy Fuel 28(k) (2014) 6299-6314.

[38] E. Soroush, M. Mesbah, A. Shokrollahi, et al., Evolving a robust modeling tool for prediction of natural gas hydrate formation conditions, J. Unconv. Oil Gas Res. 12(2015) 45-55.

[39] M. Mesbah, E. Soroush, M. Rostampour Kakroudi, Predicting physical properties (viscosity, density, and refractive index) of ternary systems containing 1-octyl-3-methyl-imidazolium bis(trifluoromethylsulfonyl)imide, esters and alcohols at 298.15 K and atmospheric pressure, using rigorous classification techniques, J. Mol. Liq. 225(2017) 778-787.

[40] M. Mesbah, E. Soroush, M. Rezakazemi, Development of a least squares support vector machine model for prediction of natural gas hydrate formation temperature, Chin. J. Chem. Eng. 25(9) (2017) 1238-1248.

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	Jian Wang, Yuanhui Shen, Donghui Zhang, Zhongli Tang, Wenbin Li. Integrated vacuum pressure swing adsorption and Rectisol process for CO₂ capture from underground coal gasification syngas[J]. 中国化学工程学报, 2023, 57(5): 265-279.
[2]	Shanwei Xiong, Li Zhou, Yiyang Dai, Xu Ji. Attention-based long short-term memory fully convolutional network for chemical process fault diagnosis[J]. 中国化学工程学报, 2023, 56(4): 1-14.
[3]	Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao. Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration[J]. 中国化学工程学报, 2023, 56(4): 46-57.
[4]	Xingzhong Li, Kunlin Yu, Zibo He, Bo Liu, Rongfei Zhou, Weihong Xing. Improved SSZ-13 thin membranes fabricated by seeded-gel approach for efficient CO₂ capture[J]. 中国化学工程学报, 2023, 56(4): 273-280.
[5]	Kun Ren, Zheng Jiao, Xiaolong Wu, Honggui Han. Multivariable identification of membrane fouling based on compacted cascade neural network[J]. 中国化学工程学报, 2023, 53(1): 37-45.
[6]	Zhong Ma, Guofu Liu, Hui Zhang, Yonggang Lu. Investigation of the redox performance of pyrite cinder calcined at different temperature in chemical looping combustion[J]. 中国化学工程学报, 2022, 48(8): 98-105.
[7]	Denglong Ma, Ruitao Wu, Zekang Li, Kang Cen, Jianmin Gao, Zaoxiao Zhang. A new method to forecast multi-time scale load of natural gas based on augmentation data-machine learning model[J]. 中国化学工程学报, 2022, 48(8): 166-175.
[8]	Ding-Ming Xue, Wen-Juan Zhang, Xiao-Qin Liu, Shi-Chao Qi, Lin-Bing Sun. Fabrication of azobenzene-functionalized porous polymers for selective CO₂ capture[J]. 中国化学工程学报, 2022, 43(3): 24-30.
[9]	Xiaobin Chen, Yuting Tang, Chuncheng Ke, Chaoyue Zhang, Sichun Ding, Xiaoqian Ma. CO₂ capture by double metal modified CaO-based sorbents from pyrolysis gases[J]. 中国化学工程学报, 2022, 43(3): 40-49.
[10]	Wanqiao Liang, Jihuan Huang, Penny Xiao, Ranjeet Singh, Jining Guo, Leila Dehdari, Gang Kevin Li. Amine-immobilized HY zeolite for CO₂ capture from hot flue gas[J]. 中国化学工程学报, 2022, 43(3): 335-342.
[11]	Xiuxin Yu, Bing Liu, Yuanhui Shen, Donghui Zhang. Design and experiment of high-productivity two-stage vacuum pressure swing adsorption process for carbon capturing from dry flue gas[J]. 中国化学工程学报, 2022, 43(3): 378-391.
[12]	Xionghui Liu, Jianfeng Du, Yu Ye, Yuchuan Liu, Shun Wang, Xianyu Meng, Xiaowei Song, Zhiqiang Liang, Wenfu Yan. Boosting selective C₂H₂/CH₄, C₂H₄/CH₄ and CO₂/CH₄ adsorption performance via 1,2,3-triazole functionalized triazine-based porous organic polymers[J]. 中国化学工程学报, 2022, 42(2): 64-72.
[13]	Jiajia Wang, Lizhi Wang, You Wang, Du Zhang, Qin Xiao, Jianhan Huang, You-Nian Liu. Recent progress in porous organic polymers and their application for CO₂ capture[J]. 中国化学工程学报, 2022, 42(2): 91-103.
[14]	Peng Tan, Yao Jiang, Qiurong Wu, Chen Gu, Shichao Qi, Qiang Zhang, Xiaoqin Liu, Linbing Sun. Light-responsive adsorbents with tunable adsorbent-adsorbate interactions for selective CO₂ capture[J]. 中国化学工程学报, 2022, 42(2): 104-111.
[15]	Baowen Wang, Zhongyuan Cai, Heyu Li, Yanchen Liang, Tao Jiang, Ning Ding, Haibo Zhao. Reaction characteristics investigation of CeO₂-enhanced CaSO₄ oxygen carrier with lignite[J]. 中国化学工程学报, 2022, 42(2): 319-328.