Cerium-MOF-Derived Composite Hierarchical Catalyst Enables Energy-Efficient and Green Amine Regeneration for CO2 Capture.

The excessive energy consumed restricts the application of traditional postcombustion CO2 capture technology and limits the achievement of carbon-neutrality goals. Catalytic-rich CO2 amine regeneration has the potential to accelerate proton transfer and increase the energy efficiency in the CO2 separation process. Herein, we reported a Ce-metal-organic framework (MOF)-derived composite catalyst named HZ-Ni@UiO-66 with a hierarchical structure, which can increase the CO2 desorbed amount by 57.7% and decrease the relative heat duty by 36.5% in comparison with the noncatalytic monoethanolamine (MEA) regeneration process. The composite catalyst of the CeO2 coating from the UiO-66 precursor on the HZ-Ni carrier shows excellent stability with a long lifespan. The HZ-Ni@UiO-66 catalyst also shows a universal catalytic effect in typical blended amine systems with a large cyclic capacity. The HZ-Ni@UiO-66 catalyst effectively decreases the energy barrier of the CO2 desorption reaction to reduce the time required to reach thermodynamics, consequently saving the energy consumption generated by water evaporation. This research provides a new avenue for advancing amine regeneration with less heat duty at low temperatures.

Comprehensive mass transfer analysis of CO2 absorption in high potential ternary AMP-PZ-MEA solvent using three-level factorial design.

Mass transfer of CO2 absorption in 2-amino-2-methyl-1-propanol (AMP) - piperazine (PZ) - monoethanolamine (MEA) was statistically investigated in terms of overall mass transfer coefficient ([Formula: see text]) and CO2 removal percentage. The parameters of interest were lean solvent flux (A), rich gas flux (B), CO2 loading in the lean solvent (C), and ratio of the sampling height to the total column height [Formula: see text] (D). From ANOVA, A was the most impactable parameter on both responses with three-quarters of the overall contribution. Regarding the three-level factorial design, a second-order polynomial increasing trend of [Formula: see text] was observed as C and/or D increased. Additionally, [Formula: see text] linearly increased as A increased but was not affected by B. On the other hand, the CO2 removal percentage linearly increased as A and/or D increased but linearly decreased as B and/or C increased. Surface analysis suggested the optimum condition for both responses at a high level of A, low level of B, low level of C, and middle level of D. In this work, D was statistically investigated and included in the predictive correlation for [Formula: see text] for the first time. The main advantage of the proposed correlation over the recently reported correlations was that it did not require a measurement of CO2 partial pressure along the column height. For each amine component in the blend, (i) AMP played a positive key role in cyclic capacity and solvent regeneration duty, (ii) PZ enhanced transfer rate, and (iii) MEA elevated total amine concentration. As a result, 1.5:1.5:3 was recommended due to (i) elevations of 68.2% [Formula: see text], 14% CO2 removal percentage, 15.1% absorption capacity, and 66.7% cyclic capacity and (ii) reduction of 50% regeneration duty compared with 5 M MEA. With respect to the other literature-reported solvents, AMP-PZ-MEA is very competitive in terms of transfer coefficient, cyclic capacity, and solvent regeneration heat duty.

Experimental investigations and the modeling approach for CO2 solubility in aqueous blended amine systems of monoethanolamine, 2-amino-2-methyl-1-propanol, and 2-(butylamino)ethanol.

In this work, new CO2 solubility data on three types of aqueous amine blends were reported to complement existing databases. The experiments were conducted at temperatures of 313 K (absorption condition) and 363 K (desorption condition). The effect of the MEA concentration on the CO2 solubility in several amine blends at low CO2 partial pressure (8 to 50.65 kPa) were studied in this work, including 0.1, 0.3, 0.5 mol/L MEA + 2 mol/L AMP; 0.1, 0.3, 0.5 mol/L MEA + 2 mol/L BEA; and 0.1, 0.3, 0.5 mol/L MEA + 1, 2 mol/L AMP + 1, 2 mol/L BEA. Besides, an additional group of equilibrium CO2 solubility data were conducted at 298 K in order to estimate the heat of CO2 absorption of the blended solvents at a temperature range from 298 to 313 K. A new simplified Kent-Eisenberg model was developed for the predictions of blended solvents, and a multilayer neural network model with Levenberg-Marquardt backpropagation algorithm was developed upon five hundred reliable published experimental data. The predictions from two methods are both in good agreement with the experimental CO2 solubility data.

Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification.

Solubility of hydrogen sulfide (H2S) in 46 single and blended physical absorbents, amines, ionic liquids, and hybrid absorbents of amines + ionic liquids and amines + physical absorbents was successfully predicted based on artificial neural networks (ANNs). Three neural network algorithms of Levenberg-Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) were applied for architecting the ANN models. The results showed that both the number of hidden neurons and the prediction algorithm affected the prediction of H2S solubility. Based on the mean square error (MSE) and determination coefficient (R 2), the most attractive model was the LM-ANN model with 17 hidden neurons. As a result, very satisfactory prediction performance (for the testing data set) with an MSE of 0.0014 and an R 2 of 0.9817 was obtained from the developed LM-ANN model. Additionally, a parity chart confirmed that the predicted solubility of H2S well aligned with the experimental data. To effectively absorb H2S and maintain high solubility of H2S, the absorbent should be well complied with the operating pressure. For a low-pressure range of less than 100 kPa, amines are very attractive. As the pressure elevated to 100-1000 kPa, amines and hybrid amine + physical absorbents are suggested. Lastly, at a high pressure over 1000 kPa, physical absorbents and ionic liquids are recommended.