Exploring of CO2 adsorption behavior by Carbazole-based hypercrosslinked polymeric adsorbent using deep learning and response surface methodology

Aber S, Amani-Ghadim AR, Mirzajani V (2009) Removal of Cr(VI) from polluted solutions by electrocoagulation: Modeling of experimental results using artificial neural network. J Hazard Mater 171(1):484–490. https://doi.org/10.1016/j.jhazmat.2009.06.025

Amini Y, Fattahi M, Khorasheh F, Sahebdelfar S (2013) Neural network modeling the effect of oxygenate additives on the performance of Pt–Sn/γ-Al2O3 catalyst in propane dehydrogenation. Appl Petrochem Res 3(1):47–54

Amiri M, Shahhosseini S, Ghaemi A (2017) Optimization of CO2 capture process from simulated flue gas by dry regenerable alkali metal carbonate based adsorbent using response surface methodology. Energy Fuels 31(5):5286–5296. https://doi.org/10.1021/acs.energyfuels.6b03303

Chairez I, García-Peña I, Cabrera A (2009) Dynamic numerical reconstruction of a fungal biofiltration system using differential neural network. J Process Control 19(7):1103–1110. https://doi.org/10.1016/j.jprocont.2008.12.009

Ghannadzadeh A, Meymivand A (2019) Environmental sustainability assessment of an ethylene oxide production process through Cumulative Exergy Demand and ReCiPe. Clean Technol Environ Policy 21(9):1765–1777. https://doi.org/10.1007/s10098-019-01748-3

Gholamiyan S, Hamzehloo M, Farrokhnia A (2020) RSM optimized adsorptive removal of erythromycin using magnetic activated carbon: Adsorption isotherm, kinetic modeling and thermodynamic studies. Sustain Chem Pharm 17:100309. https://doi.org/10.1016/j.scp.2020.100309

Ghosh I, Kar S, Chatterjee T, Bar N, Das SK (2021) Removal of methylene blue from aqueous solution using Lathyrus sativus husk: adsorption study, MPR and ANN modelling. Process Saf Environ Prot 149:345–361. https://doi.org/10.1016/j.psep.2020.11.003

Haykin S, Network N (2004) A Comp Foundation Neural Networks 2(2004):41

Hemmati A, Ghaemi A, Asadollahzadeh M (2020) RSM and ANN modeling of hold up, slip, and characteristic velocities in standard systems using pulsed disc-and-doughnut contactor column. Sep Sci Technol, pp 1–16. Doi:https://doi.org/10.1080/01496395.2020.1842890

Li Z, Gerdroodbary MB, Valipour P, Moradi R, Babazadeh H (2019) The optimization via response surface method for micro hydrogen gas actuator. Int J Hydrogen Energy 44(59):31633–31643

Maslahati A, Chelliapan S, wan mohtar H, Kamyab H, (2018) Prediction and optimization of the fenton process for the treatment of landfill leachate using an artificial neural network. Water 10:595. https://doi.org/10.3390/w10050595

Meng B, Li H, Mahurin S, Liu H, Dai S (2016) Hyper-crosslinked cyclodextrin porous polymer: an efficient CO2 capturing material with tunable porosity. RSC Adv 6:110307–110311. https://doi.org/10.1039/C6RA18307G

Mohammadi F, Esrafili A, Sobhi HR, Behbahani M, Kermani M, Asgari E, Fasih Z (2018) Evaluation of adsorption and removal of methylparaben from aqueous solutions using amino-functionalized magnetic nanoparticles as an efficient adsorbent: Optimization and modeling by response surface methodology (RSM). Desalin Water Treat 103:248–260. https://doi.org/10.5004/dwt.2018.21781

Montgomery DC (2017) Design and analysis of experiments. John wiley & sons, London

Naeem S, Shahhosseini S, Ghaemi A (2016) Simulation of CO2 Capture using sodium hydroxide solid sorbent in a fluidized bed reactor by a multilayer perceptron neural network. J Nat Gas Sci Eng 31:305–312. https://doi.org/10.1016/j.jngse.2016.03.028

Najafi P, Ramezanipour Penchah H, Ghaemi A (2020) Improving CO2/N2 and CO2/H2 selectivity of hypercrosslinked carbazole-based polymeric adsorbent for environmental protection. J Chem Petrol Eng 54(2):311–321

Najafi P, Penchah HR, Ghaemi A (2021) Synthesis and characterization of Benzyl chloride-based hypercrosslinked polymers and its amine-modification as an adsorbent for CO2 Capture. Environ Technol Innov 23:101746

Penchah HR, Ghaemi A, Godarziani H (2021a) Eco-friendly CO2 adsorbent by impregnation of diethanolamine in nanoclay montmorillonite. Environ Sci Pollut Res, pp 1–17

Penchah HR, Ghaemi A, Gilani HG (2021b) Efficiency increase in hypercrosslinked polymer based on polystyrene in CO2 adsorption process. Polym Bull, pp 1–22

Puthiaraj P, Ahn W-S (2016) CO2 capture by porous hyper-cross-linked aromatic polymers synthesized using tetrahedral precursors. Ind Eng Chem Res 55(29):7917–7923. https://doi.org/10.1021/acs.iecr.5b03963

Ramezanipour Penchah H, Ghaemi A, Ganadzadeh Gilani H (2019) Benzene-based hyper-cross-linked polymer with enhanced adsorption capacity for CO2 capture. Energy Fuels 33(12):12578–12586. https://doi.org/10.1021/acs.energyfuels.9b03136

Ramezanipour Penchah H, Ghanadzadeh Gilani H, Ghaemi A (2020) CO2, N2, and H2 adsorption by hyper-cross-linked polymers and their selectivity evaluation by gas-solid equilibrium. J Chem Eng Data 65(10):4905–4913

Ramezanipour Penchah H, Ghaemi A, Jafari F (2021a) Piperazine-modified activated carbon as a novel adsorbent for CO2 Capture: modeling and characterization. Environmental Science and Pollution Research:1–10

Ramezanipour Penchah H, Najafi P, Ghaemi A, Ghanadzadeh Gilani H (2021b) Characterization of hypercrosslinked polymer adsorbent based on carbazole to achieve higher CO2 Capture. Environ Prog Sustain Energy 40(4):e13586. https://doi.org/10.1002/ep.13586

Rasoulzadeh H, Dehghani MH, Mohammadi AS, Karri RR, Nabizadeh R, Nazmara S, Kim K-H, Sahu JN (2020) Parametric modelling of Pb(II) adsorption onto chitosan-coated Fe3O4 particles through RSM and DE hybrid evolutionary optimization framework. J Mol Liq 297:111893. https://doi.org/10.1016/j.molliq.2019.111893

Saeidi M, Ghaemi A, Tahvildari K, Derakhshi P (2018) Exploiting response surface methodology (RSM) as a novel approach for the optimization of carbon dioxide adsorption by dry sodium hydroxide. J Chin Chem Soc 65(12):1465–1475. https://doi.org/10.1002/jccs.201800012

Salehi I, Shirani M, Semnani A, Hassani M, Habibollahi S (2016) Comparative study between response surface methodology and artificial neural network for adsorption of crystal violet on magnetic activated carbon. Arab J Sci Eng 41(7):2611–2621. https://doi.org/10.1007/s13369-016-2109-3

Sheikholeslami M, Gerdroodbary MB, Moradi R, Shafee A, Li Z (2019) Application of Neural Network for estimation of heat transfer treatment of Al2O3-H2O nanofluid through a channel. Comput Methods Appl Mech Eng 344:1–12

Shojaeimehr T, Rahimpour F, Khadivi MA, Sadeghi M (2014) A modeling study by response surface methodology (RSM) and artificial neural network (ANN) on Cu2+ adsorption optimization using light expended clay aggregate (LECA). J Ind Eng Chem 20(3):870–880. https://doi.org/10.1016/j.jiec.2013.06.017

Sjostrom S, Krutka H (2010) Evaluation of solid sorbents as a retrofit technology for CO2 Capture. Fuel 89(6):1298–2130. https://doi.org/10.1016/j.fuel.2009.11.019

Taheri FS, Ghaemi A, Maleki A, Shahhosseini S (2019) High CO2 adsorption on amine-functionalized improved mesoporous silica nanotube as an eco-friendly nanocomposite. Energy Fuels 33(6):5384–5397. https://doi.org/10.1021/acs.energyfuels.9b00703

Tatar A, Naseri S, Bahadori M, Hezave AZ, Kashiwao T, Bahadori A, Darvish H (2016) Prediction of carbon dioxide solubility in ionic liquids using MLP and radial basis function (RBF) neural networks. J Taiwan Inst Chem Eng 60:151–164. https://doi.org/10.1016/j.jtice.2015.11.002

Tayebi H-A, Ghanei M, Aghajani K, Zohrevandi M (2019) Modeling of reactive orange 16 dye removal from aqueous media by mesoporous silica/cross-linked polymer hybrid using RBF, MLP and GMDH neural network models. J Mol Struct 1178:514–523. https://doi.org/10.1016/j.molstruc.2018.10.040

Tian K, Zhu T-T, Lan P, Wu Z-C, Hu W, Xie F-F, Li L (2018) Massive preparation of coumarone-indene resin-based hyper-crosslinked polymers for gas adsorption. Chin J Polym Sci 36:1168–1174. https://doi.org/10.1007/s10118-018-2127-6

Vázquez G, Calvo M, Sonia Freire M, González-Alvarez J, Antorrena G (2009) Chestnut shell as heavy metal adsorbent: Optimization study of lead, copper and zinc cations removal. J Hazard Mater 172(2):1402–1414. https://doi.org/10.1016/j.jhazmat.2009.08.006

Wang S, Shao L, Sang Y, Huang J (2019) Hollow hyper-cross-linked polymer microspheres for efficient rhodamine b adsorption and CO2 capture. J Chem Eng Data 64(4):1662–1670. https://doi.org/10.1021/acs.jced.8b01197

Wang L, Zhang Y, Jiang H, Wang H (2020) Carbonyl-incorporated aromatic hyper-cross-linked polymers with microporous structure and their functional materials for CO2 adsorption. Ind Eng Chem Res. https://doi.org/10.1021/acs.iecr.0c02165

Witek-Krowiak A, Chojnacka K, Podstawczyk D, Dawiec A, Pokomeda K (2014) Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process. Biores Technol 160:150–160. https://doi.org/10.1016/j.biortech.2014.01.021

Xu S, Luo Y, Tan B (2013) Recent development of hypercrosslinked microporous organic polymers. Macromol Rapid Commun 34:471–484. https://doi.org/10.1002/marc.201200788

Yetilmezsoy K, Demirel S, Vanderbei RJ (2009) Response surface modeling of Pb(II) removal from aqueous solution by Pistacia vera L.: Box–Behnken experimental design. J Hazard Mater 171(1):551–562. Doi:https://doi.org/10.1016/j.jhazmat.2009.06.035

Yildiz S (2017) Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process. Korean J Chem Eng 34(9):2423–2434. https://doi.org/10.1007/s11814-017-0157-3

Younas M, Sohail M, Leong LK, Bashir MJ, Sumathi S (2016) Feasibility of CO2 adsorption by solid adsorbents: a review on low-temperature systems. Int J Environ Sci Technol 13(7):1839–1860. https://doi.org/10.1007/s13762-016-1008-1