Equilibrium Solubility Of CO2 Research Articles

Predicting CO2 equilibrium solubility in various amine-CO2 systems using an artificial neural network model

Three proposed reaction mechanisms can occur in an amine-CO2 system: either zwitterionic or termolecular mechanisms for primary/secondary amines and base-catalyzed hydration for tertiary amines. The intricacy of this system hinders the construction of a general model for all types of amines. This study attempts to build an artificial neural network model that predicts the equilibrium solubility of any nonblended aqueous amine-CO2 system under given operating conditions, regardless of the reaction mechanism. This is a novel approach that has not yet been reported. The amines were characterized using molecular descriptors derived from COSMO theory through density functional theory calculations to incorporate molecular structures as model features. Our model achieved performance metrics (R2) of 0.9645 and 0.9481 for the training and validation sets, respectively. For unfamiliar amines that were absent in both the training and validation sets, our model achieved an R2 of 0.8601. Model benchmarking was performed using a previously established thermodynamic model. Interpretations of the model are also provided based on the chosen features. This study also offers exploratory insight into how the molecular structure and operating conditions affect the CO2 equilibrium solubility in amines. The model developed in this study has the potential to reduce the solvent screening time in determining appropriate amines for larger-scale applications.

Prediction of CO2 solubility in aqueous amine solutions using machine learning method

In this study, a quantitative structure–property relationship (QSPR) model has been designed based on machine learning (ML) to offer a new method to accurately predict carbon dioxide (CO2) solubility in aqueous amine solutions. Molecular descriptors are used to denote representative features of the amine molecular structure supplemented with amine solution concentration, CO2 partial pressure, and temperature as inputs to the model. The coefficient of determination (R2) of the well-trained ML model reaches 0.971, and the average absolute deviation (AAD) of independent experimental validation is 4.785 %, effectively demonstrating the model’s reliability and generalization performance. Finally, SHapley Additive exPlanations (SHAP) is adopted to reveal the contribution of different features to the model predictions, making the model more transparent and interpretable. Overall work provides a novel, low-cost, efficient method to predict equilibrium CO2 solubility in aqueous amine solution and offers a new perspective in developing advanced amine for CO2 capture.

Insight into CO2 capture by aqueous solutions of N,N‐diethylethanolamine promoted with potassium salts of amino acids

AbstractN,N‐Diethylethanolamine (DEEA) is a potential high‐capacity CO2‐capturing solvent. The CO2 reactivity of DEEA can be improved by the addition of rate promoters. Both equilibrium CO2 solubility and desorption rate are also influenced by promoters. In this work, the effect of promotion of DEEA with three amino acid salts, potassium arginate (PA), potassium prolinate (PP), and potassium glycinate (PG), was investigated. In a stirred cell reactor, CO2 reactivity of the promoted solutions was studied at 303 K. Rate promotion with PA was most effective; this was then followed by PP and PG. The value of the liquid‐side mass transfer coefficient (0.005 cm/s) for CO2 absorption in water inside the stirred cell was found. Equilibrium CO2 solubility in the promoted mixtures was measured. Empirical equations that predicted solubility data (accuracy 99%) were proposed. Desorption trials were performed at 363 K. PA, PP, and PG lowered sensible energy constraint by 59%, 32%, and 30%. PA was most‐suited for faster desorption of aqueous solutions of DEEA. Overall, potassium salts of arginine, proline, and glycine were promising candidates for improving the performance of the tertiary amine DEEA. Finally, catalytic desorption of loaded solutions of DEEA was studied and it was found that alumina was a promising catalyst for faster desorption.

A comprehensive model analysis of activity coefficients and thermodynamic properties of EAE-1DMA2P blended solvent

Solubility has an important influence on industrial applications of amine solvents. The CO2 equilibrium solubilities of EAE-1DMA2P solvents were obtained in 1:2, 1.5:1.5, and 2:1 ratios at 298–333 K and CO2 partial pressures of 8–100 kPa. Using these data, the accurate prediction of CO2 equilibrium solubility of EAE-1DMA2P solvent was achieved by constructing an activity coefficient model with an AARD of only 0.79%. In addition, by constructing the solubility prediction model of EAE-1DMA2P system based on an empirical model, KE model, Austgen model, Li-Shen model, Hu-Chakma model, Helei Liu model, and Liu et al. model the results were compared to confirm introduction of the activity coefficient has effectively improved the accuracy of the model prediction. Based on thermodynamic parameters of solubility, ΔrGm and ΔrHm, EAE-1DMA2P solvent was screened against other solvents. The results showed that EAE-1DMA2P solvent had excellent absorption, reaction rate and low desorption energy consumption, confirming its potential in CO2 capture.

Hybrid behaviors of CO2 absorption into blended DEEA‐based solution for the improvement of capture performance

AbstractBACKGROUNDAt present, the chemical absorption method of CO2 capture is considered to be an effective means to reduce carbon emissions. The development of blended amine systems with excellent CO2 capture performance is the focus of research.RESULTThe hybrid behaviors of N,N‐diethylethanolamine (DEEA) with addition of monoethanolamine (MEA), 2‐(methylamino)ethanol(MAE) and 2‐((2‐aminoethyl)amino)‐ethanol(AEEA) as absorbents were investigated and developed to reduce the energy consumption for solvent regeneration in the CO2 capture process. The equilibrium solubility of CO2 for blended DEEA/MEA, DEEA/MAE and DEEA/AEEA with a series of molar ratios (0.0:2.0, 0.5:1.5, 1.0:1.0, 1.5:0.5 and 2.0:0.0) was examined. The experimental results showed that the values of CO2 equilibrium solubility of amine blends exhibited a compromise behavior between each individual amine solution. Additionally, the effects of molar ratios on the relative CO2 absorption rate, regeneration rate and CO2 cyclic capacity for the tested amine blends were studied using an improved rapid absorbent screening method.CONCLUSIONThe experimental data showed that the blended DEEA/MAE solution exhibits much higher CO2 cyclic capacity than that of DEEA/AEEA under the nitrogen atomic conservation. However, the mixing DEEA and AEEA could obviously reduce the CO2 equilibrium solubility and CO2 cyclic capacity compared with that of individual AEEA solution, indicating the DEEA had an inhibiting effect on AEEA. Furthermore, the mass transfer performances of DEEA, DEEA/MAE and DEEA/AEEA were tested in order to validate the accuracy of the improved rapid absorbent screening method. © 2024 Society of Chemical Industry (SCI).

A generic machine learning model for CO2 equilibrium solubility into blended amine solutions

In this work, three machine learning methods were employed to predict carbon dioxide (CO2) equilibrium solubility in blended amine solutions consisting of ethanolamine (MEA), N,N-diethylethanolamine (DEEA) and methyldiethanolamine (MDEA). Three machine learning algorithms, Radial Basis Function Neural Network (RBFNN), Support Vector Machine Regression (SVR) and Extreme Gradient Boosting (XGBoost), were used to fit the experimental results. We found that the predicted values of the three models developed for the CO2 equilibrium solubility were in good agreement with the experimental results, and the comparison of the mean absolute percentage error (MAPE) and root mean square error (RMSE) results showed that XGBoost had the best prediction accuracy with the MAPE of less than 1%. Four different amine blends were then used to evaluate the expandability of the XGBoost model, namely MEA and 1-dimethylamino-2-propanol (1DMA2P), MDEA and piperazine (PZ), diethylenetriamine (DETA) and PZ, and MEA and triethanolamine (TEA); the MAPE between the experimental and predicted results were 0.30%, 0.91%, 0.86% and 1.63%, respectively. The results obtained showed that the XGBoost model has enormous potential for application in dealing with the CO2 equilibrium solubility in blended amine solutions.

Joint incremental learning network for flexible modeling of carbon dioxide solubility in aqueous mixtures of amines

Vapor-liquid equilibrium (VLE) modeling is one of the most essential tasks for rigorous estimations of thermodynamic properties in chemical process design and analysis. To realize any commercially feasible carbon dioxide (CO2) capture scheme using a carefully chosen aqueous amine solution, the accurate model of CO2 solubility in this water-amine system plays a vital role. However, the experimental data of various mixtures of amine solvents are insufficient and expensive to acquire. In view of this problem, a novel joint incremental learning network (JILN) structure is proposed for modeling equilibrium solubility of CO2 in various bi-solvent aqueous amine mixtures. With the proposed method, the knowledge embedded in the well-trained mono-solvent models can be extracted and effectively transferred to their related multi-solvent models. By adopting the proposed modeling method, the numerical experimental results showed that the mean absolute percentage errors (MAPEs) for various bi-solvent models were 2.1–4.9%, which indicates a maximum of 68% reduction in prediction blunders if compared with their ANN counterparts.

Speciation and gas-liquid equilibrium study of CO2 absorption in aqueous MEA-DEEA blends

The blending of primary amines, with fast reaction kinetic, together with tertiary amines, with a large loading capacity, is an increasingly popular approach to develop high-performance CO2 capture sorbents with low energy requirements for regeneration. In this study, we investigated the CO2 absorption performance of several aqueous solutions obtained by mixing ethanolamine (MEA) and diethylethanolamine (DEEA) and compared them with that of individual MEA and DEEA. CO2 loadings at equilibrium were measured experimentally under a wide range of operating conditions, at temperatures between 298 and 323 K, CO2 partial pressures in the range 5–60 kPa, and different amine mixing ratios. In addition, 13C NMR spectroscopy was employed to elucidate the reaction mechanisms and identify the species formed during CO2 capture. The results of the study reveal the intricate interplay of MEA and DEEA in CO2 capture, where MEA carbamate formation predominates in the early stages, gradually shifting to bicarbonate formation as the amine concentration decreases. Moreover, excess properties, which reflect non-ideal behaviors in amine blends, were introduced as a key parameter in the study. An excess property model, based on excess CO2 loading, was developed to accurately predict the CO2 equilibrium solubility without constant experimental measurement. As a result, good agreement was found between the experimental data and those calculated with the model (excess CO2 loading) under the same operating conditions, with a average relative deviation of 1.1%.

New insights into the structure-activity relationship for CO2 capture by tertiary amines from the experimental and quantum chemical calculation perspectives

In comparison with primary and secondary amines, tertiary amines exhibit higher CO2 equilibrium and lower CO2 absorption heat, making them attractive for CO2 capture applications. However, the relationship between the structure of tertiary amines and their CO2 capture capacity needs to be further studied. This study aimed to investigate the structure–activity relationship between the molecular configuration of representative tertiary amines, and their CO2 equilibrium solubility, as well as CO2 absorption heat. The CO2 absorption heat of nine tertiary amines with distinct structures was measured using a high-precision microcalorimeter, while quantum chemical calculations were employed to explore this relationship. The reactivity of tertiary amines was assessed by visualizing the effects of electron density and steric hindrance by using natural population analysis and dual descriptor analysis, respectively. The findings revealed that the stronger electron-donating effect of ethyl groups than the methyl groups enhanced the reactivity of tertiary amines. Additionally, the presence of methyl groups on the side chain of tertiary amines promoted the reactivity. By contrast, the presence of too many hydroxyl groups resulted in steric hindrance thus reducing the reactivity of tertiary amines. Furthermore, interaction region indicators were employed to visually evaluate the influence of intramolecular hydrogen bonds on the reactivity of tertiary amines. The results show that the presence of intramolecular hydrogen bonds enhanced the equilibrium solubility of CO2 while facilitating the reduction of the CO2 absorption heat. The results obtained from this study are important for the selection and development of efficient tertiary amines that are suitable for industrial applications in CO2 capture.

Comprehensive analysis of thermodynamic models for CO2 absorption into a blended N,N‐diethylethanolamine‐1,6‐hexamethyl diamine (DEEA‐HMDA) amine

AbstractIn this work, CO2 equilibrium solubility of 1M N,N‐diethylethanolamine (DEEA):2M 1,6‐hexamethyl diamine (HMDA), 1.5M DEEA:1.5M HMDA and 2M DEEA:1M HMDA was studied with a temperature range of 298–333 K and CO2 partial pressure range of 8–100 kPa. Seven thermodynamic models including Empirical model, Kent and Eisenberg (KE) model, Hu–Chakma model, Austgen model, Helei Liu model, Liu et al. model, and Li–Shen model were developed by correlating reaction equilibrium constants with observed equilibrium solubility of CO2 in mixed amine solvents. The evaluation of those models was conducted in terms of the average absolute relative deviation (AARD). The results indicated that Liu et al. model considering T, [Amine], Ptotal and [CO2(aq)] can better represent this complex system with an AARD of 8.06%. Meanwhile, comprehensive comparison and analysis were also performed to identify the contribution of parameters to develop models, which could provide a guideline for the development of accurate thermodynamic models for representation of thermodynamic behaviors.

A modified semi-empirical model for correlating and predicting CO2 equilibrium solubility in aqueous 2-[2-(dimethylamino)ethoxy]ethanol solution

Amine-based post-combustion capture is considered an effective method to control CO2 emissions from industrial flue gases, but some improvements are still needed for its widespread use. Looking for low-corrosive sorbents for steel, we decided to investigate the properties of the tertiary amine 2-[2-(dimethylamino) ethoxy] ethanol (DMAEE), and in particular its CO2 equilibrium solubility at temperature in the range 298.15–323.15 K and CO2 partial pressure between 5.0 and 60 kPa. A correction factor based on the physical solubility was applied to modify the Kent-Eisenberg model and then to predict the CO2 solubility in DMAEE solution, and the absolute average deviation (AAD) between the prediction and the experimental data was found to be 3.0%, lower than that obtained by reported Kent-Eisenberg, Austgen, Hu-Chakma, and Li-Shen models. The proposed model was then further correlated with published solubility data of four other tertiary amines, and the results indicate that the proposed semi-empirical model can be used for these amines with acceptable AADs. In addition, the heat of CO2 absorption and the dissociation constant (pKa) were also systematically evaluated and compared with other reported amines, and the obtained results indicate that DMAEE has the potential to be a suitable absorbent for post-combustion CO2 capture.

Analysis of the energy consumption in solvent regeneration processes using binary amine blends for CO2 capture

Blending two or more amines has recently been recognized as an efficient strategy to formulate competitive sorbents for post-combustion CO2 capture, as it allows to benefit from the advantages of each individual amine. In the present work, we devised four new dual-amine aqueous blends, based on the tertiary amine 2-dimethylamino-2-methyl-1-propanol (2DMA2M1P) mixed with different primary and secondary amines, namely ethanolamine (MEA), benzylamine (BZA), 2-amino-2-methyl-1-propanol (AMP) and diethanolamine (DEA). The CO2 capture and release performance of the formulated blends were experimentally evaluated by measuring CO2 equilibrium solubility, CO2 absorption and desorption rates, and energy consumption during the regeneration process, and results were compared with those obtained under the same operating conditions with 5 M aqueous MEA, the reference sorbent for CO2 capture processes. Moreover, 13C NMR spectroscopy was used to identify and quantify the species formed during the CO2 absorption process to better understand the reaction mechanism in each blend. We found that all formulated blends showed appreciable CO2 capture performance and, most importantly, significantly better desorption performance than conventional MEA. In particular, BZA+2DMA2M1P provided the best capture performance, while AMP+2DMA2M1P showed a faster CO2 desorption rate and lower energy consumption than the other tested blends.

Investigation of the improvement of the CO2 capture performance of aqueous amine sorbents by switching from dual-amine to trio-amine systems

Mixing amines with different properties is considered a smart strategy for developing efficient aqueous solutions for reversible CO2 capture. Specifically, dual-amine blends containing the tertiary amine 2-dimethylamino-2-methyl-1-propanol (2DMA2M1P) have proven to be particularly advantageous due to significantly lower regeneration costs compared to conventional sorbents. With the aim of formulating high-efficiency sorbents, in this work we evaluated the improvement in the CO2 capture performance of such dual-amine blends by adding a further amine to form trio-amine systems: the use of three amines with different characteristics could further emphasize the advantages obtainable during both absorption and desorption. We formulated three new different aqueous solutions prepared by mixing 2DMA2M1P with the reaction rate promoter ethanolamine (MEA) and the absorption capacity promoter 2-amino-2-methyl-1-propanol (AMP) in different molar ratios. For each blend, the CO2 solubility at equilibrium, the CO2 absorption rate from a gaseous mixture, the regeneration rate at T = 373 K and the energy requirement for desorption were determined experimentally. In addition, to better understand the capture mechanism, 13C NMR spectroscopy was employed to evaluate the speciation of each blend during the CO2 absorption. Their CO2 capture performance was compared with that of the benchmark aqueous MEA and two 2DMA2M1P-based dual-amine mixtures, obtained with the same procedure. As a finding, all formulated trio-amine blends have superior CO2 equilibrium solubility and desorption performance compared with MEA 5 M. Noteworthy, the comparison with similar dual-amine systems highlighted the role of a suitable third amine in order to obtain enhance both absorption and desorption efficiencies. Among the formulated systems, MEA-2DMA2M1P-AMP (1:2:2) is potentially the most suitable to replace MEA in CO2 capture processes after a careful cost-benefit analysis on larger scale systems.

Predicting the equilibrium solubility of CO2 in alcohols, ketones, and glycol ethers: Application of ensemble learning and deep learning approaches

In the present work, the solubility of CO2 in solutions comprising various physical solvents (Methanol, Ethanol, Propylene Glycol, n-Pentanol, n-Butanol, n-Propanol, Ethylene Glycol, 2-Butanone, 2-Methoxyethanol, 2-Ethoxyethanol, and Acetone) was modeled using machine learning approaches. In this regard, ensemble learning and deep learning approaches including Bagging Regressor with support vector regression (SVR) as the based estimator (Bagging_SVR), Adaptive Boosting Regressor with Decision Tree as the based estimator (ADA_DT), Decision Tree (DT), and Convolutional Neural Network (CNN) were developed. To develop the models, a broad set of CO2 solubility data (810 experimental data points for 11 alcohol, ketone, and glycol ether solvents) was acquired over various operating temperatures (283.15-373.15 K) and pressures (0.0142-57.38 MPa). In addition, the pressure, temperature, critical properties, and acentric factor of physical solvents were considered as the inputs to the machine learning approaches. To evaluate models’ performance, different graphical and statistical analyzes were conducted. The results revealed that the CNN model is more accurate than other proposed models. Root mean square error (RMSE) and determination coefficient (R2) values for CNN model were obtained as 0.016027 and 0.9943, respectively. Based on the sensitivity analysis, the dissolution of CO2 in physical solvents was highly affected by acentric factors as well as critical temperature and critical pressure. The use of leverage method showed that the used experimental data have high reliability and the CNN model also displayed high credit. Results of this study provide accurate models to estimate the solubility of CO2 in solutions containing physical solvents, which is more straightforward and user-friendly compared to thermodynamic methods with difficult calculations and procedures.

Experimental investigations of CO2 absorption and catalyst-aided CO2 desorption performance of several different amines blending with a promoter

This research presented the new experimental results of the absorption and desorption performance of various single and blended solvents in terms of initial CO2 absorption rate, initial desorption rate for blank solvents and solvents with the aid of HZSM-5 catalyst, and equilibrium CO2 solubility. MEA and PZ are two promoters used to facilitate several individual aqueous single solvents including BEA, AMP, BDEA, and MDEA. In order to check the altered performance of absorption and desorption, the solvents were specified at 2.5 M. The initial desorption rate was evaluated by varying the temperature, weight of the solid catalyst, and initial rich CO2 loading. The experimental results were then taken to compare with the obtained data of the benchmark solution (2.5 M MEA). The comparison results revealed that all the selected blank solvent blends can provide better absorption and desorption performance than the blank benchmark solution. The results also indicated that the performance of catalyst-aided CO2 desorption could be strongly affected by the rich loading and operating temperature. Meaning that the activation of proton donation by HZSM-5 was patently affected by the above two conditions, which eventually affect the CO2 separation from the rich solvent.

Thermodynamic studies for improving the prediction of CO2 equilibrium solubility in aqueous 2-dimethylamino-2-methyl-1-propanol

In this work, CO2 equilibrium solubility in aqueous 2-(dimethylamino)-2-methyl-1-propanol (2DMA2M1P) solution was investigated by varying temperature, CO2 partial pressure and amine concentration. Dissociation constant (pKa) and CO2 absorption heat of 2DMA2M1P were calculated. Results show that the CO2 equilibrium solubility increases with the CO2 partial pressure but decreases with the increase of temperature. It was also revealed that the CO2 equilibrium solubility declines when the concentration of 2DMA2M1P increases from 1 to 2 mol/L. In addition, a new extended Cf model was proposed for predicting the CO2 equilibrium solubility, and the predicted results show a good agreement with the experimental measured data with an average absolute deviation (AAD) of 2.08%. Furthermore, the proposed model was validated with the published data of tertiary amines, and the results indicate that the developed model has a good universality in predicting the CO2 equilibrium solubility. Finally, a comparison with other reported amines was also made in terms of the CO2 equilibrium solubility and the CO2 absorption heat. All of them proved that the 2DMA2M1P could be a competitive amine solvents for CO2 capture.

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 313K (absorption condition) and 363K (desorption condition). The effect of the MEA concentration on the CO2 solubility in several amine blends at low CO2 partial pressure (8 to 50.65kPa) were studied in this work, including 0.1, 0.3, 0.5mol/L MEA + 2mol/L AMP; 0.1, 0.3, 0.5mol/L MEA + 2mol/L BEA; and 0.1, 0.3, 0.5mol/L MEA + 1, 2mol/L AMP + 1, 2mol/L BEA. Besides, an additional group of equilibrium CO2 solubility data were conducted at 298K in order to estimate the heat of CO2 absorption of the blended solvents at a temperature range from 298 to 313K. 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.

Understanding the potential benefits of blended ternary amine systems for CO2 capture processes through 13C NMR speciation study and energy cost analysis

Aqueous blends of the three amines 2-aminoethanol (MEA), N-methyldiethanolamine (MDEA) and 2-amino-2-methyl-1-propanol (AMP) were tested as sorbents for CO2 capture processes. We formulated three different blends with the same overall amine concentration (5M) but with different amine molar ratio, namely blend-1 (2M MEA+2M MDEA+1M AMP), blend-2 (1M MEA+2M MDEA+2M AMP) and blend-3 (2M MEA+1M MDEA+2M AMP). Their CO2 absorption and desorption performances were investigated in batch experiments in terms of CO2 equilibrium solubility, CO2 absorption at 40 °C and CO2 desorption at 100 °C as a function of time, and energy consumption during the regeneration process. The results were compared to those obtained under the same operating conditions with 5M aqueous MEA, the benchmark sorbent for CO2 absorption processes. The effect of the different amino composition on the CO2 capture mechanism was evaluated by identifying and quantifying the species formed during the capture process through an accurate 13C NMR speciation study. The results obtained showed that all formulated sorbents have higher desorption performance and require less energy for regeneration than conventional aqueous MEA, confirming that blending different alkanolamines has the potential to improve CO2 capture. In particular, blend-2 can be regarded as a promising sorbent for implementation in commercial systems, as it performed a desorption rate up to 5 times higher than conventional aqueous MEA, combined with a significantly lower relative heat duty for regeneration (41.5%) and lower operating costs for CO2 capture.

Prediction of CO2 solubility in potential blends of ionic liquids with Alkanolamines using statistical non-rigorous and ANN based modeling: A comprehensive simulation study for post combustion CO2 capture

Equilibrium CO2 solubility data plays a pivotal role in the process design of the post-combustion CO2 capture Unit. In the current study, statistical non-rigorous models are developed to predict the solubility of carbon dioxide (CO2) in different aqueous blends of ionic liquids (ILs) and blends of ILs with alkanolamines, ethanol, etc. These correlations are utilized to estimate the liquid phase CO2 loading as a function of CO2 partial pressure at different temperature conditions. The correlations can predict CO2 solubility in the liquid phase of different (ILs + alkanolamine + water/ethanol) in the temperature and CO2 partial pressure range of (298–353) K and (50–6200) kPa respectively. The CO2 mole fraction in the liquid phase pertaining to 19 different solvent formulations is considered in the range of (0–0.487). It has been found that there is a good agreement between the results of the proposed models and reported experimental data of CO2 solubility. In addition to this, the predictability of artificial neural network (ANN) for CO2 solubility is also tested for 22 systems of ionic liquid blends with amines. The main advantages associated with these proposed models are their underlying simplicity and minimal input data, namely temperature and pressure.

Investigation of equilibrium CO2 solubility in 35 wt% aqueous 1-(2-aminoethyl) piperazine (AEP) and performance study over monoethanolamine for CO2 absorption

The present study investigates the CO2 absorption capacity of 1-2-(aminoethyl)piperazine (AEP) as a potential solvent for post combustion CO2 capture. The CO2 absorption performance has been evaluated in terms of Equilibrium CO2 solubility at varied temperature and pressure range. The CO2 solubility measurement was conducted using high pressure stirred autoclave reactor within the temperature and Pressure range of (308.15–328.15) and (2–200) kPa respectively. The equilibrium CO2 solubility data are further correlated using semi-empirical model and equilibrium based Modified Kent-Eisenberg model which shows a better agreement with the generated experimental data. In addition to this, a comprehensive parametric study was also carried out in Aspen-Plus Platform to evaluate the performance of proposed solvent over conventional solvent monoethanolamine (MEA). Influence of various important operating parameters such as flow rates of solvent and Gas medium, amine concentration, solvent inlet temperature and (L/G) ratio on the % CO2 removal was studied.