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Predictive modeling of CO2 solubility in aqueous DIPA-based solvents using machine learning

https://doi.org/10.2139/ssrn.5577330
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/jccs.200600034openalex-snapshot
Solubility of Carbon Dioxide in Aqueous Mixtures of DIPA + MDEA and DIPA + PZ Solutions
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Selective carbon-based adsorbents for carbon dioxide capture from mixed gas streams and catalytic hydrogenation of CO2 into renewable energy source: A review
inferredcites →https://doi.org/10.1016/j.envres.2023.117503openalex-snapshot
Comprehensive review and assessment of carbon capturing methods and technologies: An environmental research
inferredcites →https://doi.org/10.1016/j.fluid.2010.02.035openalex-snapshot
Solubility of carbon dioxide in aqueous solutions of diisopropanolamine and methyldiethanolamine
inferredcites →https://doi.org/10.1016/j.fluid.2011.08.025openalex-snapshot
Thermodynamic modeling for CO2 absorption in aqueous MEA solution with electrolyte NRTL model
inferredcites →https://doi.org/10.1016/j.fluid.2014.05.017openalex-snapshot
Simultaneous measurement solubility of carbon dioxide+hydrogen sulfide into aqueous blends of alkanolamines at high pressure
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Universal intelligent models for liquid density of CO2 + hydrocarbon mixtures
inferredcites →https://doi.org/10.1016/j.ijggc.2011.12.001openalex-snapshot
Experimental investigation on the effect of addition of amine activators to aqueous solutions of N-methyldiethanolamine on the rate of carbon dioxide absorption
inferredcites →https://doi.org/10.1016/j.ijggc.2015.02.024openalex-snapshot
Positioning and revision of CCUS technology development in China
inferredcites →https://doi.org/10.1016/j.ijggc.2015.05.005openalex-snapshot
Artificial neural network models for the prediction of CO2 solubility in aqueous amine solutions
inferredcites →https://doi.org/10.1016/j.jclepro.2019.119707openalex-snapshot
Recent trends in the development of adsorption technologies for carbon dioxide capture: A brief literature and patent reviews (2014–2018)
inferredcites →https://doi.org/10.1016/j.jclepro.2023.139435openalex-snapshot
Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture
inferredcites →https://doi.org/10.1016/j.jct.2013.11.025openalex-snapshot
Experiment and modeling solubility of CO2 in aqueous solutions of Diisopropanolamine + 2-amino-2-methyl-1-propanol + Piperazine at high pressures
inferredcites →https://doi.org/10.1016/j.jhydrol.2020.125574openalex-snapshot
Radial basis function artificial neural network (RBF ANN) as well as the hybrid method of RBF ANN and grey relational analysis able to well predict trihalomethanes levels in tap water
inferredcites →https://doi.org/10.1016/j.jngse.2017.07.023openalex-snapshot
Measurement of carbon dioxide solubility in aqueous diisopropanolamine solutions blended by N-(2-aminoethyl) ethanolamine + piperazine and density measurement of solutions
inferredcites →https://doi.org/10.1016/j.molliq.2023.121310openalex-snapshot
Vapor-liquid equilibrium of carbon dioxide solubility in a deep eutectic solvent (choline chloride: MDEA) and a mixture of DES with piperazine-experimental study and modeling
inferredcites →https://doi.org/10.1016/j.molliq.2024.126327openalex-snapshot
CO2 solubility and physicochemical properties of 2-amino 2-methyl-1-propanol based solvents for post-combustion CO2 capture. Predictive modeling using machine learning
inferredcites →https://doi.org/10.1016/j.psep.2025.107050openalex-snapshot
Recent advances in ultrasound-assisted CO2 absorption/desorption processes: A review
inferredcites →https://doi.org/10.1016/j.rser.2017.03.109openalex-snapshot
Carbon capture by absorption – Path covered and ahead
inferredcites →https://doi.org/10.1016/j.seta.2023.103365openalex-snapshot
Available challenges and recent progress in carbon dioxide capture, and reusing methods toward renewable energy
inferredcites →https://doi.org/10.1021/acs.energyfuels.0c00880openalex-snapshot
Solubility and Heat of Absorption of CO2 into Diisopropylamine and N , N -Diethylethanolamine Mixed with Organic Solvents
inferredcites →https://doi.org/10.1021/acsomega.0c04046openalex-snapshot
Effect of Blending Ratio and Temperature on CO 2 Solubility in Blended Aqueous Solution of Monoethanolamine and 2-Amino-2-methyl-propanol: Experimental and Modeling Study Using the Electrolyte Nonrandom Two-Liquid Model
inferredcites →https://doi.org/10.1021/ie0306067openalex-snapshot
Characterization and Comparison of the CO 2 Absorption Performance into Single and Blended Alkanolamines in a Packed Column
inferredcites →https://doi.org/10.1021/je60072a004openalex-snapshot
Solubility of hydrogen sulfide and carbon dioxide in an aqueous diisopropanolamine solution
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Induction of Decision Trees
inferredcites →https://doi.org/10.1038/s41598-023-30777-8openalex-snapshot
Intelligent modeling of hydrogen sulfide solubility in various types of single and multicomponent solvents
inferredcites →https://doi.org/10.1038/s41598-024-73070-yopenalex-snapshot
Predictive modeling of CO2 solubility in piperazine aqueous solutions using boosting algorithms for carbon capture goals
inferredcites →https://doi.org/10.1109/5.58326openalex-snapshot
Networks for approximation and learning
inferredcites →https://doi.org/10.1109/72.80341openalex-snapshot
Orthogonal least squares learning algorithm for radial basis function networks
inferredcites →https://doi.org/10.1109/access.2022.3182659openalex-snapshot
Methods for Pruning Deep Neural Networks
inferredcites →https://doi.org/10.1162/neco.1991.3.2.246openalex-snapshot
Universal Approximation Using Radial-Basis-Function Networks
inferredcites →https://doi.org/10.5194/gmd-7-1247-2014openalex-snapshot
Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature
inferredcites →https://doi.org/10.7551/mitpress/4175.001.0001openalex-snapshot
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