[1]
Zhang K, Wu J, Yoo H, Lee Y. Machine Learning-based approach for Tailor-Made design of ionic Liquids: Application to CO2 capture. Separation and Purification Technology. https://doi.org/https://doi.org/10.1016/j.seppur.2021.119117
[2]
Dashti A, Amirkhani F, Hamedi A-S, Mohammadi AH. Evaluation of CO2 Absorption by Amino Acid Salt Aqueous Solution Using Hybrid Soft Computing Methods. ACS Omega. 2021;6(19):12459-69. https://doi.org/10.1021/acsomega.0c06158
[3]
Jiang L, Gonzalez-Diaz A, Ling-Chin J, Roskilly AP, Smallbone AJ. Post-combustion CO2 capture from a natural gas combined cycle power plant using activated carbon adsorption. Applied Energy. 2019;245:1-15. https://doi.org/https://doi.org/10.1016/j.apenergy.2019.04.006
[4]
Monjezi AH, Mesbah M, Rezakazemi M, Younas M. Prediction bubble point pressure for CO 2/CH 4 gas mixtures in ionic liquids using intelligent approaches. Emergent Materials. 2021;4(2):565-78.
[5]
Yu B, Yu H, Yang Q, Li K, Ji L, Zhang R, et al. Postcombustion Capture of CO2 by Diamines Containing One Primary and One Tertiary Amino Group: Reaction Rate and Mechanism. Energy & Fuels. 2019;33(8):7500-8.
[6]
Usubharatana P, Tontiwachwuthikul P. Enhancement factor and kinetics of CO2 capture by MEA-methanol hybrid solvents. Energy Procedia. 2009;1(1):95-102. https://doi.org/https://doi.org/10.1016/j.egypro.2009.01.015
[7]
Vaidya PD, Mahajani VV. Kinetics of the reaction of CO2 with aqueous formulated solution containing monoethanolamine, N-methyl-2-pyrrolidone, and diethylene glycol. Industrial & engineering chemistry research. 2005;44(6):1868-73.
[8]
Muchan P, Saiwan C, Narku-Tetteh J, Idem R, Supap T, Tontiwachwuthikul P. Screening tests of aqueous alkanolamine solutions based on primary, secondary, and tertiary structure for blended aqueous amine solution selection in post combustion CO2 capture. Chemical Engineering Science. 2017;170:574-82.
[9]
Arthur L Kohl RN. Gas Purification. 5th ed: Elsevier; 1997.
[10]
Conway W, Bruggink S, Beyad Y, Luo W, Melián-Cabrera I, Puxty G, et al. CO2 absorption into aqueous amine blended solutions containing monoethanolamine (MEA), N,N-dimethylethanolamine (DMEA), N,N-diethylethanolamine (DEEA) and 2-amino-2-methyl-1-propanol (AMP) for post-combustion capture processes. Chemical Engineering Science. 2015;126:446-54. https://doi.org/https://doi.org/10.1016/j.ces.2014.12.053
[11]
Liu S, Gao H, He C, Liang Z. Experimental evaluation of highly efficient primary and secondary amines with lower energy by a novel method for post-combustion CO2 capture. Applied Energy. 2019;233-234:443-52. https://doi.org/https://doi.org/10.1016/j.apenergy.2018.10.031
[12]
Nematollahi MH, Carvalho PJ. Green solvents for CO2 capture. Current Opinion in Green and Sustainable Chemistry. 2019;18:25-30. https://doi.org/https://doi.org/10.1016/j.cogsc.2018.11.012
[13]
Zhao B, Liu F, Cui Z, Liu C, Yue H, Tang S, et al. Enhancing the energetic efficiency of MDEA/PZ-based CO2 capture technology for a 650MW power plant: Process improvement. Applied Energy. 2017;185:362-75. https://doi.org/https://doi.org/10.1016/j.apenergy.2016.11.009
[14]
Freeman BC, Bhown AS. Assessment of the technology readiness of post-combustion CO2 capture technologies. Energy Procedia. 2011;4:1791-6. https://doi.org/https://doi.org/10.1016/j.egypro.2011.02.055
[15]
Asadi E, Haghtalab A, Shirazizadeh HA. High-pressure measurement and thermodynamic modeling of the carbon dioxide solubility in the aqueous 2-((2-aminoethyl)-amino)-ethanol + sulfolane system at different temperatures. Journal of Molecular Liquids. 2020;314:113650. https://doi.org/https://doi.org/10.1016/j.molliq.2020.113650
[16]
Mäkelä M. Experimental design and response surface methodology in energy applications: A tutorial review. Energy Conversion and Management. 2017;151:630-40. https://doi.org/https://doi.org/10.1016/j.enconman.2017.09.021
[17]
Pashaei H, Ghaemi A, Nasiri M, Karami B. Experimental Modeling and Optimization of CO2 Absorption into Piperazine Solutions Using RSM-CCD Methodology. ACS Omega. 2020;5(15):8432-48. https://doi.org/10.1021/acsomega.9b03363
[18]
Hemmati A, Ghaemi A, Asadollahzadeh M. RSM and ANN modeling of hold up, slip, and characteristic velocities in standard systems using pulsed disc-and-doughnut contactor column. Separation Science and Technology. 2021;56(16):2734-49. https://doi.org/10.1080/01496395.2020.1842890
[19]
Ghaemi A, Hemmati A, Asadollahzadeh M, Molaee M. Hydrodynamic behavior of standard liquid-liquid systems in Oldshue–Rushton extraction column; RSM and ANN modeling. Chemical Engineering and Processing - Process Intensification. 2021;168:108559. https://doi.org/https://doi.org/10.1016/j.cep.2021.108559
[20]
Mashhadimoslem H, Vafaeinia M, Safarzadeh M, Ghaemi A, Fathalian F, Maleki A. Development of Predictive Models for Activated Carbon Synthesis from Different Biomass for CO2 Adsorption Using Artificial Neural Networks. Industrial & Engineering Chemistry Research. 2021.
[21]
Kolbadinejad S, Mashhadimoslem H, Ghaemi A, Bastos-Neto M. Deep learning analysis of Ar, Xe, Kr, and O2 adsorption on Activated Carbon and Zeolites using ANN approach. Chemical Engineering and Processing-Process Intensification. 2021:108662.
[22]
Tatar A, Naseri S, Bahadori M, Hezave AZ, Kashiwao T, Bahadori A, et al. Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks. Journal of the Taiwan Institute of Chemical Engineers. 2016;60:151-64. https://doi.org/https://doi.org/10.1016/j.jtice.2015.11.002
[23]
Chen G, Luo X, Zhang H, Fu K, Liang Z, Rongwong W, et al. Artificial neural network models for the prediction of CO2 solubility in aqueous amine solutions. International Journal of Greenhouse Gas Control. 2015;39:174-84. https://doi.org/https://doi.org/10.1016/j.ijggc.2015.05.005
[24]
Hamzehie ME, Mazinani S, Davardoost F, Mokhtare A, Najibi H, Van der Bruggen B, et al. Developing a feed forward multilayer neural network model for prediction of CO2 solubility in blended aqueous amine solutions. Journal of Natural Gas Science and Engineering. 2014;21:19-25. https://doi.org/https://doi.org/10.1016/j.jngse.2014.07.022
[25]
Norouzbahari S, Shahhosseini S, Ghaemi A. Modeling of CO2 loading in aqueous solutions of piperazine: Application of an enhanced artificial neural network algorithm. Journal of Natural Gas Science and Engineering. 2015;24:18-25. https://doi.org/https://doi.org/10.1016/j.jngse.2015.03.011
[26]
Shahsavand A, Derakhshan Fard F, Sotoudeh F. Application of artificial neural networks for simulation of experimental CO2 absorption data in a packed column. Journal of Natural Gas Science and Engineering. 2011;3(3):518-29. https://doi.org/https://doi.org/10.1016/j.jngse.2011.05.001
[27]
Taud H, Mas JF. Multilayer Perceptron (MLP). In: Camacho Olmedo MT, Paegelow M, Mas J-F, Escobar F, editors. Geomatic Approaches for Modeling Land Change Scenarios. Cham: Springer International Publishing; 2018. p. 451-5. https://doi.org/10.1007/978-3-319-60801-3_27
[28]
Hagan MT, Demuth HB, Beale M. Neural network design: PWS Publishing Co.; 1997.
[29]
Sodeifian G, Niazi Z. Prediction of CO2 absorption by nanofluids using artificial neural network modeling. International Communications in Heat and Mass Transfer. 2021;123:105193.
[30]
Mas J-F, Puig H, Palacio JL, Sosa-López A. Modelling deforestation using GIS and artificial neural networks. Environmental Modelling & Software. 2004;19(5):461-71.
[31]
Adhikary PP, Dash CJ. Comparison of deterministic and stochastic methods to predict spatial variation of groundwater depth. Applied Water Science. 2017;7(1):339-48. https://doi.org/10.1007/s13201-014-0249-8
[32]
Yu H, Xie T, Paszczynski S, Wilamowski BM. Advantages of radial basis function networks for dynamic system design. IEEE Transactions on Industrial Electronics. 2011;58(12):5438-50. https://doi.org/10.1021/acs.energyfuels.9b00961
[33]
Broomhead DS, Lowe D. Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals and Radar Establishment Malvern (United Kingdom); 1988.
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