Amino Acid Salt Solutions Research Articles

The effects of counter ion on CO2 capture performance of amino acid salt solutions for direct air capture applications

Direct CO2 capture from the atmosphere has received growing attention driven by the imperative of achieving global net-zero emission targets. Amino acid salt solutions are promising candidates for liquid-based direct air capture processes. Utilised as a neutralized salt by reacting initially with hydroxides, it has been speculated that their performance may vary with the type of counter ion present in the solution. This work investigates the influence of counter ions, potassium, sodium, and lithium, of amino acid salt solutions on the physical properties, the CO2 absorption capacities, and the CO2 absorption kinetics under atmospheric CO2 concentration levels. The potassium-containing amino acid solutions exhibited significantly lower viscosities and mildly elevated densities compared to sodium- and lithium-containing ones. At 25 °C, the potassium-prolinate solution demonstrated the highest viscosity (8.5 mPa⋅s), whereas K-glycinate exhibited the lowest viscosity (2.5 mPa⋅s). Additionally, potassium-based solutions consistently displayed the highest CO2 mass transfer coefficients, followed by sodium- and lithium-containing ones. The CO2 mass transfer coefficient was highest for potassium-prolinate (2.99 mmol m−2⋅s−1⋅kPa−1) with the lowest rate observed for potassium-β-alaninate (1.68 mmol m−2⋅s−1⋅kPa−1). Interestingly, the type of counter ion had minimal impact on the CO2 absorption capacity, with potassium-based solutions exhibiting only a slightly elevated capacity compared to sodium- and lithium-based solutions. Specifically, potassium-lysinate displayed the highest CO2 absorption capacity (0.7 mol CO2 /mol amine), while potassium-β-alaninate showed the lowest (0.65 mol CO2/mol amine). It should be noted that all lithium-based solutions of all amino acids formed precipitates during CO2 absorption. From a practical and operational perspective, these findings suggest that the potassium salt solution of amino acids would be the optimal choice for direct air capture applications due to their enhanced solubility and CO2 mass transfer rates compared to the corresponding sodium and lithium counter ion salt solutions.

A study on degradation and CO2 capture performance of aqueous amino acid salts for direct air capture applications

AbstractWe have previously proposed amino acid salts solutions as potential absorption liquids for direct air capture (DAC) of CO2 from the atmosphere. However, little is known about their relevant CO2 solubilities, CO2 mass transfer rates, and susceptibility to oxidative and thermal degradation under conditions relevant to DAC. We report here on the overall solubility of CO2 and CO2 mass transfer rates into a series of amino acid salts solutions. Additionally, the robustness of various amino acid salt solutions to thermal and oxidative degradation has been assessed.CO2 absorption rates into the amino acid salts solutions were observed to be in the same order of magnitude as aqueous monoethanolamine (MEA), with sarcosinate and lysinate solutions providing the fastest and slowest CO2 mass transfer rates at 25°C, respectively. Degradation data revealed that all amino acid salt solutions investigated in this study displayed elevated rates of thermal degradation at both 120 and 150°C relative to MEA. The opposite trend was observed with respect to oxidative degradation where all amino acid salt solutions showed a greater resistance to oxidative degradation than that observed for MEA under the conditions investigated here. Considering the degradation, CO2 absorption capacity, and CO2 mass transfer rate data, we propose the potassium salts of proline and sarcosine as the most promising amino acid salts (of those considered here) for further evaluation in DAC processes. Overall, this study provides valuable insight into the suitability of various amino acid salt solutions as absorption liquid for DAC. © 2024 The Author(s). Greenhouse Gases: Science and Technology published by Society of Chemical Industry and John Wiley & Sons Ltd.

INVESTIGATION OF CARBON DIOXIDE ABSORPTION CAPACITY AND DISSOLUTION RATE WITH AMINO ACID SALT SOLUTIONS: SODIUM AND POTASSIUM GLYCINATE

Global warming is a major world problem and causes climate change. The primary cause of global warming is carbon dioxide (CO2) emissions. Ongoing studies are being conducted to mitigate the effects of this problem. Anthropogenic CO2 emissions occur by burning fossil fuels to generate power and heat. To combat this problem, CO2 must be captured from point emission sources or directly from air. The conventional method to remove CO2 at the point emission sources is post-combustion systems. In these systems, absorption process is generally used to diminish CO2 emission and capturing at the source. Current researches aim to find an efficient and alternative solution to absorption. In this work, sodium glycinate (NaGly) and potassium glycinate (KGly), which are amino acid salt solutions, were investigated for CO2 absorption. Amino acid salt solutions have shown similar absorption kinetics and capacities to amine-based solutions due to same functional group. These solutions are usually more stable to oxidative degradation and have low volatilities, higher surface tensions and the viscosities are very close to waters. Experiments were performed using a stirred cell system at ambient temperature (20°C) and atmospheric pressure (91 kPa), in Ankara, Türkiye. In experiments, sodium glycinate and potassium glycinate concentration was ranged between 0.1 and 1.5 M. The experiments were also repeated with sodium hydroxide and potassium hydroxide in the same concentration range for comparison. In addition, the amino acid salt solutions examined in this study were compared with alkaline solutions and glycine in terms of total CO2 absorption capacity and CO2 dissolution rate. As a result of the experiments, the potassium glycinate solutions gave approximately 1.4 times better results than the sodium glycinate solutions at the highest concentration. Also, functional groups were determined by FTIR analysis of pure and CO2-loaded potassium glycinate solution.

Direct air capture of CO2 using green amino acid salts

Direct air capture (DAC) of CO2 using liquid sorbent technology is gaining attention as a promising approach in tackling the looming climate crisis. Despite technical advancements, critical aspects such as contactor selection, energy efficiency, sustainability, and environmental compatibility still pose uncertainties. In this study, various green amino acid salts performances in a DAC system were explored using non-porous hollow fiber membrane contactors (HFMCs). Two DAC absorption and desorption apparatuses were developed. For the DAC-absorption unit, the thermodynamic and kinetic behavior of five types of aqueous amino acid salt solutions were evaluated in long-term operations. High absorption stability for most of the solutions in different solvent loadings (up to 80% CO2 solvent loaded) were observed and potassium glycinate (GlyK) was selected as the most suitable candidate for DAC. To enhance the CO2 separation efficiency, parametric analysis on air and solvent flow rates, solvent temperature and concentration were conducted using GlyK. Vacuum low-temperature desorption experiments were carried out with GlyK to evaluate the CO2 removal efficiency over a range of solvent temperatures and concentrations, CO2 loadings, vacuum pressures, and vacuum/sweep gas mode. The results successfully quantified the effect of each operational parameter under various conditions on CO2 removal in a DAC system. Finally, to investigate the impact of membrane characteristics on DAC absorption–desorption performance, a developed and validated model was used. Taken all together, hybrid technology of membrane modules and green amino acid salts is shown to be a viable pathway towards a sustainable DAC process.

Estimation of CO2Absorption by a Hybrid Aqueous Solution of Amino Acid Salt with Amine

AbstractFour developed machine learning algorithms are proposed to prognosticate the CO2solubility in amino acid salt solutions, blended with amine solutions as additives, in broad ranges of temperature and pressure. From literature 375 experimental data points for CO2solubility were collected. The results from the applied algorithms indicated that the CO2solubility is estimated acceptably close to the experimental values. In the best case, the developed network estimates CO2solubility in the stated solutions with an average relative deviation of 6.53 % and a correlation coefficient of 0.9892.

A thermo-acoustic investigation of L-Histidine amino acid solvation in aqueous ionic salt solutions with the goal of healing Hypokalaemia

At two distinct temperatures, L-Histidine (C6H9N3O2) has been the subject of a thermodynamic investigation in an ionic salt (KCl & KI) solution (i.e., 283 & 293 K). C6H9N3O2 has been studied at various concentration ranges with ultrasonic velocities (U) and densities (ρ) of 0.1 mol-kg−1 in water and aqueous ionic salt solutions (KCl & KI) (i.e., 0.02 – 0.2 mol-kg−1). Ultrasonic velocity and density data have been used to derive a number of thermos-acoustical characteristics, including adiabatic compressibility, intermolecular free length, acoustic impedance, non-linearity parameter, isothermal compressibility, and surface tension. Using the Hartmann-Balizar method and considering its significance from the perspective of interaction in the liquid mixture ((C6H9N3O2) + H2O + KCl/KI), the thermodynamic and acoustical behavior of the non-linearity parameter has been explained in terms of concentration and thermal change in the system under investigation. This work gives foundational knowledge that is essential for understanding the interaction between ionic salt and C6H9N3O2. According to the results, all systems have a stronger link when the solute (H2O/KCl/KI) and solvent C6H9N3O2 are present at higher quantities.The current research offers a thorough understanding of the nature and kind of interactions existing in a solution of an ionic salt and C6H9N3O2, and it also demonstrates the tendency for structures to develop in solvents that are soluble in them. This kind of study looks at a solid foundation to comprehend how vital amino acids and ionic salts function in biochemical processes and how to comprehend a bio-physical characteristic to treat low potassium levels in the human body (i.e., Hypokalaemia) coz the order of intermolecular interaction between amino acid and ionic salt solutions found to be: (C6H9N3O2 + KI + H2O) > (C6H9N3O2 + KCl + H2O) > (C6H9N3O2 + H2O).

Facile estimation of viscosity of natural amino acid salt solutions: Empirical models vs artificial intelligence

Natural amino acid salt solutions (NAASs) are paving the way for greener carbon capture. This study developed simple and precise methods for the viscosity modeling of NAASs. Two approaches, namely, empirical correlations and artificial intelligence, were assessed using a large databank (16 NAAs, 3 alkaline compounds, 25 NAASs, and 1582 data points). Two general correlations and a global equation were suggested. Benefitting from the input of single reference-point data, the modified global equation yielded the best results with a 2.28% deviation. The other empirical models represented viscosities with less than a 7.20% error. The second approach, employing artificial neural networks (ANNs) with different algorithms, was also proposed. The best ANNs were a single-layer perceptron network with tansig + trainlm functions, a double-layer perceptron network with logsig + tansig + trainlm functions, and a radial basis function network with the maximum neurons. They managed to calculate the viscosities with errors of 2.82%, 1.82%, and 0.47%, respectively.

Generalized viscosity model based on free-volume theory for amino acid salt solutions as green CO2 capture solvents

Green processes that use environmentally friendly materials have gained significant attention, in particular in the fuel processing section. Alkaline salts of natural amino acids (NAAs), known as NAA salt solutions (NAASs), have significant potential to replace conventional solvents for achieving greener CO2 capture. Thermophysical properties, including the viscosity of NAASs, are essential for developing processes and their optimization. This study aims to develop a generalized theoretical model derived from free-volume theory (FVT) for estimating the viscosity of NAASs. Four different cases for parameter regression were applied to the largest viscosity databank investigated thus far (19 NAAs, three alkaline compounds, 29 NAASs, and 2039 data points), and the performance of the model using the parameters from the cases was evaluated. The highest accuracy was achieved (average absolute relative deviation percentage (AARD) = 1.54 %, average relative deviation percentage (ARD) = 0.09 %, R-squared (R2) = 0.9969) in the concentration- and solution-specific cases, but many parameters were required for the whole databank. In contrast, the most general case using global parameters resulted in the least number of parameters (six adjustable parameters) and showed reasonable accuracy (AARD = 10.42 %). A specific case in which the model parameters were obtained from each NAAS with a simple modification of the barrier energy parameter (α) yielded a higher accuracy (AARD = 4.12 %, ARD = 0.53 %, R2 = 0.9606) with a smaller number of parameters for the entire databank. The results demonstrated that the FVT is capable of modeling the viscosities of NAASs and can thus be employed for the simulation and optimization of thermophysical properties.

High-efficiency carbon dioxide capture using an algal amino acid salt solution

This study developed a novel, high-efficiency, and bio-based CO2 absorbent – microalgal amino acid salt solution (MAASS). The MAASS was prepared by hydrolyzing microalgal biomass with potassium hydroxide. Alanine, glutamic acid, glycine, aspartic acid, leucine, lysine, and proline are the primary amino acids in the MAASS with a total amino acid concentration of 0.592 mol/L. The absorption and desorption results on a synthetic flue gas with 10 %(v/v) CO2 and 90 %(v/v) air showed that the MAASS had an absorption capacity of 1.27 mol CO2/mol amine, which is 3.4 and 3.1 times higher than monoethanolamine (MEA) and the reported pure amino acid salt solution, respectively. The MAASS was regenerable, showing no signs of deterioration in the absorption capacity after eight consecutive absorption and desorption cycles. The mass and energy balance analysis concluded that combining algal cultivation and MAASS absorption would lead to a technically feasible and environmentally friendly CO2 capture technology.

Development of ANFIS technique for estimation of CO2 solubility in amino acids and study on impact of input parameters

ANFIS (Adaptive neuro fuzzy inference system) modeling of CO2 capture using chemical absorbent was carried out in this study to correlate the solubility of CO2 to the solvent and operational parameters. In the ANFIS model, the input parameters including temperature, pressure, and physio-chemical properties of the solvent were considered, while the loading of CO2 in the absorbent was considered as the sole target output to be predicted by the model. Indeed, we developed a machine learning based model for predicting the CO2 loading capacity in amino acid salt solutions as the chemical absorbent of carbon dioxide. This model uses a metaheuristic optimized ANFIS based on a wide range of amino acids. This study's novel part is the use of Differential Evolution (DE) and Firefly Algorithm (FA) metaheuristics in order to solve hyper-parameter tuning of ANFIS as an optimization problem based on differential evolution. Accordingly, the optimized ANFIS model has an R2 score of 0.9520 for the test data and a score of 0.9841 for the training data. This indicates that the proposed model is both general and accurate in terms of its predictions for CO2 loading in amino acid salt solutions. The MAPE and RMSE error rates are also 1.17E-01, respectively, while the MAPE error rate is 1.14E-01.

A Review on Carbon Dioxide Minimization in Biogas Upgradation Technology by Chemical Absorption Processes.

With an ever-increasing population and unpredictable climate changes, meeting energy demands and maintaining a sustainable environment on Earth are two of the greatest challenges of the future. Biogas can be a very significant renewable source of energy that can be used worldwide. However, to make it usable, upgrading the gas by removing the unwanted components is a very crucial step. CO2 being one of the major unwanted components and also being a major greenhouse gas must be removed efficiently. Different methods such as physical adsorption, cryogenic separation, membrane separation, and chemical absorption have been discussed in detail in this review because of their availability, economic value, and lower environmental footprint. Three chemical absorption methods, including alkanolamines, alkali solvents, and amino acid salt solutions, are discussed. Their primary works with simple chemicals along with the latest works with more complex chemicals and different mechanical processes, such as the DECAB process, are discussed and compared. These discussions provide valuable insights into how different processes vary and how one is more advantageous or disadvantageous than the others. However, the best method is yet to be found with further research. Overall, this review emphasizes the need for biogas upgrading, and it discusses different methods of carbon capture while doing that. Methods discussed here can be a basic foundation for future research in carbon capture and green chemistry. This review will enlighten the readers about scientific and technological challenges regarding carbon dioxide minimization in biogas technology.

Modelling the effect of CO2 loading of aqueous potassium glycinate on CO2 absorption in a membrane contactor

CO2absorption into aqueous potassium glycinate in a polypropylene membrane contactor was modelled using two alternative models: a 1D model and a 1D-2D model considering axial diffusion in the liquid phase. Models were fitted to experimental data using various fitting parameters, which were compared. Experiments were carried out under industrially relevant conditions characterized by CO2-loaded absorbent entering the contactor and high degree of reactant conversion over the contactor. The experiments and models were developed to specifically investigate the effect of changes in solution CO2loading at contactor inlet. This is a key issue rarely reported in the literature, especially for amino acid salt solutions. Unexpectedly, the 1D model was found to explain the experimental results more accurately compared to the more complex 1D-2D model. This was the case for the base models, using only the membrane mass transfer coefficient as a fitting parameter, and the final models introducing secondary fitting parameters. The 1D model was found to show the best experimental fit following fitting of the equilibrium constant used in prediction of the enhancement factor. The 1D-2D model showed the best fit following correction of potassium glycinate diffusivity as a function of solution CO2loading. The 1D approach was found to result in a computationally effective model with good fit to the present experimental data. This model provides a good basis for further development and could be considered for use in contactor design and optimization studies. It is suggested that various model simplifications led to inability of the 1D-2D model to accurately predict the experimental results.

RETRACTED: Development of hybrid machine learning model for simulation of chemical reactors in water treatment applications: Absorption in amino acid

Separation and capture of CO2 from gas mixtures is of great importance from environmental point of view which can be effectively achieved using amino acids as new class of chemical absorbents. However, screening the proper absorbent with desired separation properties using experimental measurements is tedious and costly. The predictive computational techniques can be employed to overcome this problem. In this study, for estimating and analyzing CO2 solubility in chemical solvents based on amino acid salt solutions, we created two regression models from different classes of machine learning methods. The main aim is to analyze the effect of physico-chemical parameters on the CO2 dissolution in solvent which can be carried out in chemical reactors for separation/conversion of CO2 for environmental applications. A number of CO2 solubility data are collected from resources and used for training and validation of machine learning computations. Several inputs were considered for the developed machine learning models. Inputs in this regression task are T (temperature), weight% (overall mass percentage of solvent), PCO2 (partial pressure of CO2 in the gas), MW-am (molecular weight of amino acid salt), MPC (melting point of amino acid salt), MWC (molecular mass of cation). In this task, we must predict alpha (CO2 loading in the amino acid solution) as the only output of the developed models. The models studied in this research are the Gaussian process and the decision tree boosted with Gradient boosting. With the R2 criterion, the scores of the two Gradient boosting and Gaussian process models were obtained 0.985 and 0.993, respectively. As the third efficiency metric of the models, the Gradient boosting and regression of the Gaussian process with the RMSE criterion is the error rates of 1.10E−01 and 1.44E−01. The models developed in this work indicated to be reliable and robust enough for screening the solvents for a particular application and to save time and cost of experimental measurements.

Facile and Accurate Calculation of the Density of Amino Acid Salt Solutions: A Simple and General Correlation vs Artificial Neural Networks

Facile and Accurate Calculation of the Density of Amino Acid Salt Solutions: A Simple and General Correlation vs Artificial Neural Networks

Computational simulation using machine learning models in prediction of CO2 absorption in environmental applications

We developed two distinct regression models based on machine learning approach for estimating CO2 loading in solvents in this study. The methods of Adaptive boosted support vector (SVR) machines and the Adaptive boosted Gaussian process (GPR) are the models employed in this study for approximating carbon dioxide loading in the solvent for the purpose of environmental applications. Indeed, the case study is molecular separation for capture of CO2 from a mixture using amino acid salt solutions and understanding the influence of variables on variation of gas solubility. The target output parameter in the computations is the CO2 solubility in liquid phase (alpha). The scores of the two models of boosted SVR and boosted GPR were 0.830 and 0.991, respectively, using the R2 criterion. Furthermore, the error rate with the MAPE standard is 2.21758E-01 and 1.06938E-01 for the two models, respectively. The boosted SVR and regression of the boosted GPR with the MAE criterion have error rates of 1.64937E-01 and 7.28501E-02, respectively, as the models' third efficiency metric. The used machine learning models indicated to be robust for simulation of CO2 absorption in liquid phase for environmental applications and can be used to save time and costs of measurements.

Multiple machine learning models for prediction of CO2 solubility in potassium and sodium based amino acid salt solutions

In this work, we developed artificial intelligence-based models for prediction and correlation of CO2 solubility in amino acid solutions for the purpose of CO2 capture. The models were used to correlate the process parameters to the CO2 loading in the solvent. Indeed, CO2 loading/solubility in the solvent was considered as the sole model’s output. The studied solvent in this work were potassium and sodium-based amino acid salt solutions. For the predictions, we tried three potential models, including Multi-layer Perceptron (MLP), Decision Tree (DT), and AdaBoost-DT. In order to discover the ideal hyperparameters for each model, we ran the method multiple times to find out the best model. R2 scores for all three models exceeded 0.9 after optimization confirming the great prediction capabilities for all models. AdaBoost-DT indicated the highest R2 Score of 0.998. With an R2 of 0.98, Decision Tree was the second most accurate one, followed by MLP with an R2 of 0.9.

Mass transfer and capture of carbon dioxide using amino acids sodium aqueous solution in microchannel

Capture and storage of CO2 are important to restrain the greenhouse effect. The amino acid salt solution as a new type of CO2 absorbent shows good application potential. Besides, microchannel has a unique enhancement to mass transfer, which is conducive to promote CO2 capture. The absorption performance of CO2 into aqueous solutions of sodium salts of l-threonine, l-valine, l-alanine and glycine in the microchannel was investigated. It was found that the CO2 absorption rate of four sodium amino acid aqueous solutions under the same condition follows in the order: ñ (sodium glycinate) > ñ (sodium l-alaninate) > ñ (sodium l-valinate) > ñ (sodium l-threoninate). The sodium glycinate aqueous solution has maximum CO2 absorption ability, thus the mass transfer and capture of CO2 absorbed into the sodium glycinate aqueous solution were studied systematically. The effects of operating conditions including gas flow rate, liquid flow rate, and sodium glycinate concentration on the specific surface area, overall volumetric mass transfer coefficient, overall mass transfer coefficient and absorption fraction of CO2 were explored. Furthermore, a new correlation was proposed to predict the volumetric mass transfer coefficient.

Production of cooling water by Ti3C2Tx MXene interlayered forward osmosis membranes for post-combustion CO2 capture system

Forward osmosis (FO) technology has the potential to replace the trim cooler and to supply cooling water for a post-combustion CO2 capture system. Herein, a high-performance MXene (Ti3C2Tx) interlayered FO membrane was fabricated. Thanks to the ability of MXene to adjust the structures of substrate and polyamide, the MXene interlayered FO (MXene-FO) membrane showed improved permeability and selectivity in a monoethanolamine (MEA) draw solution. The performance of MXene-FO membrane was investigated at different membrane orientations and temperatures with amino acid/salt (AAS) solutions (taurine/KOH and β-alanine/KOH) as draw solutions. Because the osmotic pressures provided by AAS solutions (104.4 ± 0.3 bar for 3 mol/L taurine/KOH and 153.7 ± 0.2 bar for 5 mol/L β-alanine/KOH solutions) are higher than that of 5 mol/L MEA solution (93.0 ± 0.1 bar), the MXene-FO membrane exhibited higher water fluxes in AAS solutions. Besides, the good performance of the MXene-FO membrane was shown with the presence of small specific amino acid fluxes and specific KOH fluxes. To investigate the feasibility of using saline water as the feed solution, 3.5 wt% NaCl solution was applied and the results indicated that the MXene-FO membrane maintained stable water fluxes and small specific solute fluxes. This work demonstrates the potential of nanomaterials interlayered FO membranes for cooling water production in a post-combustion CO2 capture system.

A Process Intensification Approach for CO2 Absorption Using Amino Acid Solutions and a Guanidine Compound

Environmentally friendly amino-acid salt solutions are used for the absorption of carbon dioxide from concentrated flue-gas streams via chemical absorption. Process intensification reduces operating and capital costs by combining chemical reactions and separation operations. Here, we present a new process-intensification approach that combines the CO2 capture and the amino-acid regeneration steps into a single process carried out in a slurry three-phase reactor. The absorbed CO2 precipitates as a solid carbonated guanidine compound. The cycle is completed by separation of the solid precipitate to strip the CO2 and regenerate the guanidine compound, while the liquid solution is recycled to the slurry reactor. The process was studied by modifying a model developed by the authors for a gas-liquid bubble column without the presence of the guanidine compound. The guanidine precipitation reaction was accounted for using kinetic parameters calculated by the authors in another study. The proposed model was implemented by modifying an existing computer code used for the simulation of gas-liquid bubble columns. The calculated results showed that the proposed cycle can significantly reduce energy, equipment, and operating costs and can make an important contribution to developing a competitive cost-effective large-scale process for CO2 capture.

Neural modeling and simulation of molecular separation using amino acid salt solutions

In this research paper, we studied absorption of carbon dioxide in amino acid solutions theoretically via the artificial intelligence (AI) method. The CO2 loadings in different solutions were predicted as a function of input parameters including amino acid parameters and operational parameters such as pressure and temperature. For the training of the data, we used differential evolution, and for the decision and prediction sections, we used the fuzzy interface system. The model can be abbreviated as differential evolution fuzzy interface system (DEFIS). Moreover, various methods and tuning parameters in the DEFIS were used to increase the accuracy of the model. Cluster influence range (CIR) was used for tuning the model, and different population sizes were used, including 9, 18, 27, and 36, with the purpose of observing the effects of these parameters on the training. The results from AI overlapped measured results meaning that we can use AI for optimization of the separation process for CO2 sorption in chemical solvents. The neural methodology indicated a reliable and robust prediction of CO2 solubility in different chemical solvents.