Solubility Of CO2 In Aqueous Solutions Research Articles

Numerical investigation of carbon dioxide capture using nanofluids via machine learning

Using nanofluids to capture CO2 is a promising method of reducing emissions. The goal of this study was to develop models that could forecast how well water-based nanofluids would absorb CO2. Several machine learning models, such as Decision Tree, Random Forest, eXtreme Gradient Boosting, and K-Nearest Neighbors, underwent training using a total of 1306 experimental datasets. These datasets contained information on the CO2 solubility in aqueous solutions for different types of nanoparticles. In terms of Average Absolute Relative Deviation (AARD%), Mean Absolute Error (MAE), Relative Absolute Error, Mean Squared Error, and Correlation Coefficient, the predictive performance on separate test data was assessed. The XGBoost model demonstrates a higher degree of accuracy in simulating the absorption capacity of aqueous nanofluid compared to other models. This is evident through the AARD value of 2.8%, MSE of 0.00084, MAE of 0.012 and an R value of 0.992. The research provides insight into the utilization of different machine learning algorithms for simulating the absorption of CO2 by nanofluids. By employing accurate data-driven models, the efficiency of nanofluid-based CO2 capture processes can be enhanced through improvements in their design and operating conditions.

(Invited) Selective and Efficient Electrochemical CO2 Reduction Reaction from Cu and Cu-M Binary Alloys

Electrochemical CO2 reduction reaction (CO2RR) has attracted a great deal of attention due to its potential to convert CO2 into valuable chemical fuels and feedstocks. The production of C2+ chemicals, such as ethylene and ethanol, from CO2RR is particularly important because of their large market demand. However, several challenges must be addressed before CO2RR can be commercially scaled up, including high overpotential, low selectivity, and poor CO2 solubility in aqueous solutions. In this presentation, I will discuss our recent efforts to overcome these challenges in CO2RR by using a gas diffusion electrode (GDE) to achieve a CO2RR current density of over 100 mA/cm2 in a flow reactor. First, I will highlight the crucial role of the local CO2RR environment in promoting C-C coupling on Cu surfaces, and I will present strategies for controlling this environment based on steady-state modeling and monodisperse Cu2O nanoparticles as a model catalyst system. Using this approach, we have been able to produce C2+ products, such as ethylene and ethanol, with a Faraday efficiency of over 70% at a current density of over 300 mA/cm2 in near-neutral electrolytes. Additionally, I will discuss the impact of alloying Cu with Ni and Zn on tailoring product selectivity. By introducing elements with different CO binding strengths, we can significantly alter the selectivity for hydrocarbons and alcohols. I will provide further details on these findings during the presentation.

Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture

One of the key concerns in the 21st century, alongside the growing population, is the increase in energy consumption and the resulting global warming. The impact of CO2, a prominent greenhouse gas, has garnered significant attention in the realm of CO2 capture and gas purification. CO2 absorption can be enhanced by introducing some additives into the aqueous solution. In this study, the accuracies of some of the most up-to-date computational approaches are investigated. The employed machine learning methods are hybrid-adaptive neuro-fuzzy inference system (Hybrid-ANFIS), particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS), least-squares support vector machines (LSSVM) and genetic algorithm-radial basis function (GA-RBF). The developed models were used in estimating the solubility of CO2 in binary and ternary amines aqueous solutions. i.e. blends of monoethanolamine (MEA), triethanolamine (TEA), aminomethyl propanol (AMP), and methyldiethanolamine (MDEA). This modeling study was undertaken over relatively significant ranges of CO2 loading (mole of CO2/mole of solution) as a function of input parameters, which are 0.4–2908 kPa for pressure, 303–393.15 K for temperature, 36.22–68.89 g/mol for apparent molecular weight, and 30–55 wt % for total concentration. In this work, the validity of approaches based on different statistical graphs was investigated, and it was observed that the developed methods, especially the GA-RBF model, are highly accurate in estimating the data of interest. The obtained AARD% values for the developed models are 18.63, 8.25, 12.22, and 7.54 for Hybrid-ANFIS, PSO-ANFIS, LSSVM, and GA-RBF, respectively.

CO2 solubility in aqueous solutions of blended amines: Experimental data for mixtures with MDEA, AMP and MPA and modeling with the modified Kent-Eisenberg model

CO2 solubility in aqueous solutions of blended amines: Experimental data for mixtures with MDEA, AMP and MPA and modeling with the modified Kent-Eisenberg model

Prediction of CO2 solubility in NaCl brine under geological conditions with an improved binary interaction parameter in the Søreide-Whitson model

The accurate prediction the CO2 solubility in the aqueous NaCl solution under geological conditions is essential in CO2 geo-energy resources recovery and CO2 geological storage practices, therefore has attracted extensive academic interests. In this paper, comprehensive thermodynamical calculations are performed concerning the CO2 solubility in aqueous solutions with various sodium chloride concentrations of 1–6 m (mol/kgw) at the geological pressure and temperature conditions of 313–433 K and 0.5–30 MPa. A new binary interaction parameter (BIP) in Søreide-Whitson (SW) model is proposed to improve the calculation accuracy in the wide salinity, temperature and pressure ranges. Through systematic comparisons with the 42 experimental sets and 206 measurement points on the solubility of CO2 in NaCl brine, the employed thermodynamic model as well as the modeling results is well validated based on the satisfactory Average Absolute Deviation (AAD) value of 3.77%. At last, detailed analysis on effects of salinity, temperature and pressure are carried out based on the calculation results. It is found the solubility of CO2 decreases with increasing salinity and decreasing pressure. There are oversteps for the CO2 solubility under different temperatures when the system pressure is higher than 20 MPa.

CO2 solubility modelling in Non-Precipitating aqueous solutions of potassium lysinate

Modelling of CO2 solubility in aqueous solutions of potassium lysinate (LysK) is mainly hindered by scarcity of experimental vapor–liquid equilibrium data and lack of chemical equilibrium constants associated to the reaction mechanism for the CO2/LysK/H2O system. Therefore, Kent-Eisenberg (KE) correlation stands out from the literature, being among the most used approaches for the description of the equilibrium CO2 partial pressure at different loadings. In this work, a Kent-Eisenberg-like approach has been developed, enhancing the empirical Kent-Eisenberg with Debye-Hückel activity coefficients in order to guide model calibration with reference to selected experimental data for CO2 solubility in 33.1 and 33.5 %w/w aqueous LysK solution; moreover, the KE edition provides an estimation of the missing equilibrium constants. This information has been embedded in a first thermodynamically sound and predictive Deshmukh-Mather (DM) model (average absolute deviation equal to 7 %) validated against additional experimental data in a wide temperature and concentration range.

CO2/N2 separation by glycerol aqueous solution in a hollow fiber membrane contactor module: CFD simulation and experimental validation

In this paper, CO2 separation from CO2/N2 mixed gas was experimentally and theoretically investigated inside a gas–liquid hollow fiber membrane contactor (HFMC) module along with developing and validation of a comprehensive computational fluid dynamic (CFD) model. An aqueous solution of glycerol (10 wt%) (C3H8O3), a green and cost-effective physical solvent, was employed as the liquid absorbent, flowing in the shell side of the hollow fibers. To attain a better insight into the membrane wetting effects on separation performance, all non-wetting, partially-wetting, and completely-wetting conditions were investigated. Moreover, a correlation for CO2 Henry’s law constant was developed to estimate the CO2 solubility in aqueous solutions of glycerol via adopting the literature data. The CFD simulation results revealed that the gas to absorbent flow rate ratio (Fg/Fa) is a dominant parameter in controlling the CO2 removal efficiency. CO2 separation performance was augmented via decreasing the gas flow rate, absorption temperature and increasing the membrane porosity to tortuosity ratio as well as glycerol concentration. In contrast, by increment of the membrane wettability from non-wetting to completely-wetting condition, the CO2 removal percentage was dramatically reduced from 72.5 to 18.5%. A fair agreement between the CFD simulation results and experimental data with an absolute average relative error percentage (AARE%) of 2.7% was achieved, confirming the validity of the developed model.

An Accurate Model to Calculate CO2 Solubility in Pure Water and in Seawater at Hydrate–Liquid Water Two-Phase Equilibrium

Understanding of CO2 hydrate–liquid water two-phase equilibrium is very important for CO2 storage in deep sea and in submarine sediments. This study proposed an accurate thermodynamic model to calculate CO2 solubility in pure water and in seawater at hydrate–liquid water equilibrium (HLWE). The van der Waals–Platteeuw model coupling with angle-dependent ab initio intermolecular potentials was used to calculate the chemical potential of hydrate phase. Two methods were used to describe the aqueous phase. One is using the Pitzer model to calculate the activity of water and using the Poynting correction to calculate the fugacity of CO2 dissolved in water. Another is using the Lennard–Jones-referenced Statistical Associating Fluid Theory (SAFT-LJ) equation of state (EOS) to calculate the activity of water and the fugacity of dissolved CO2. There are no parameters evaluated from experimental data of HLWE in this model. Comparison with experimental data indicates that this model can calculate CO2 solubility in pure water and in seawater at HLWE with high accuracy. This model predicts that CO2 solubility at HLWE increases with the increasing temperature, which agrees well with available experimental data. In regards to the pressure and salinity dependences of CO2 solubility at HLWE, there are some discrepancies among experimental data. This model predicts that CO2 solubility at HLWE decreases with the increasing pressure and salinity, which is consistent with most of experimental data sets. Compared to previous models, this model covers a wider range of pressure (up to 1000 bar) and is generally more accurate in CO2 solubility in aqueous solutions and in composition of hydrate phase. A computer program for the calculation of CO2 solubility in pure water and in seawater at hydrate–liquid water equilibrium can be obtained from the corresponding author via email.

Multifunctional Melamine Foam Assisted Lead Halide Perovskites for Highly Efficient and Long‐Term Photocatalytic CO2 Reduction Under Pure Water

Application of lead halide perovskites in CO2 photoreduction in pure H2O medium is restricted, owing to the inherent instability and high toxicity of lead. Herein, 3D melamine foam (MF) with an interconnected porous structure is selected as a multifunctional assistance to solve these problems. Three‐dimensionality of MF can avoid direct touches of CsPbX3 (X = Br, I) with liquid H2O, preventing damage of H2O. Porosity of MF can accelerate H2O evaporation by its strong adsorption and photothermal effects, which will overcome the drawback of low CO2 solubility in aqueous solution, achieving a quick and sufficient mixture of CO2 with H2O vapor. These functions contribute to realize highly efficient CO2 reduction by CsPbX3 in pure H2O medium. The best products and electron consumption yields of the composite reach 42.08 and 161.84 μmol g−1 h−1, respectively, which surpass most of the other CsPbX3‐related works in H2O or organic reaction media. MF/CsPbBr3 also presents long‐term stability, with product yields not showing any obvious decrease after continuous reaction for 104 h. Moreover, strong adherence of MF toward perovskites allows a good recoverability of CsPbX3, avoiding pollution of lead leakage to the environment.

Improving the CO2 solubility in aqueous mixture of MDEA and different polyamine promoters: The effects of primary and secondary functional groups

The main objective of this research is improving the CO2 solubility in aqueous solution of MDEA by different polyamines including ethylenediamine (EDA), diethylenethriamine (DETA), thriethylenetetramine (TETA), tetraethylenepentamine (TEPA) at low pressure conditions. Since the polyamines have more than one basic nitrogen groups, they could absorb CO2 with higher rate and capacity compared to tertiary amines such as MDEA. In this regard, the performance of prepared solutions are experimented in an isothermal batch reactor in the temperature range of 293.15–313.15 K, promoter fraction ranged from 0 to 15%, and pressure range 0–200 kPa. The prepared solvents are evaluated in terms of CO2 absorption capacity, initial absorption rate and regeneration efficiency based on the number of ratio of secondary to primary functional groups in the promoter. The results show that increasing the promoter fraction in the prepared solution improves the absorption capacity and rate. Based on the experimental data, the higher CO2 solubility, absorption rate, the boiling point and lower vapor pressure are the main advantages of the TEPA promoted MDEA solution in addition to higher absorption capacity and effective loading.

Efficient estimation of CO2 solubility in aqueous salt solutions

Industrialization and modernization have led to an increasing trend of greenhouse gas emissions entering the atmosphere. Consequently, many countries are imposing strict environmental regulations due to increasing concern on the climate change, and a significant amount of research is being conducted to tackle this problem. Carbon capture and sequestration are one of the major methods, which can remarkably contribute to this issue since a major greenhouse gas in Earth's atmosphere is carbon dioxide (CO2). The general objective of this work was to investigate the significance of potential use of aqueous salt solutions for capturing CO2 by developing an accurate model for estimation of this phenomenon. In particular, 608 experimental data of CO2 solubility in aqueous solutions of salts as a function of temperature, pressure, salt solution concentration (molality), hydrogen bond acceptor count of salts, molecular weight of salts, and complexity value of the salts were collected from the literature. Four state-of-the-art computational methods (LSSVM, ANN, PSO-ANFIS and GA-ANFIS) were employed to examine their predictive capability for the problem. The results reveal that the LSSVM offers the highest accuracy with R2 = 0.9927 and %AARD = 5.37. Although the neural network and fuzzy interface systems are more complex approaches, the superiority of the LSSVM method for the problem under study demonstrates the easy nature of this phenomenon. Thus, the LSSVM model is recommended for estimating the CO2 solubility in aqueous salt solutions because not only it has more accuracy, but also it is easy to apply, has less mathematically complicated and less time-consuming.

Selective Electrochemical Reduction of Carbon Dioxide Using Cu Based Metal Organic Framework for CO2 Capture.

The conversion efficiency and product selectivity of the electroreduction of carbon dioxide have been largely limited by the low CO2 solubility in aqueous solution. To relieve this problem, Cu3(BTC)2 (Cu-MOF) as CO2 capture agent was introduced into a carbon paper based gas diffusion electrode (GDE) in this study. The faradaic efficiencies (FEs) of CH4 on GDE with Cu-MOF weight ratio in the range of 7.5-10% are 2-3-fold higher than that of GDE without Cu-MOF addition under negative potentials (-2.3 to -2.5 V vs SCE), and the FE of the competitive hydrogen evolution reaction (HER) is reduced to 30%. This work paves the way to develop GDE with high catalytic activity for ERC.

Solubility of CO2 in aqueous solutions of glycerol and monoethanolamine

For decades, carbon dioxide emissions have been an environmental and health issue. Amines like Monoethanolamine (MEA) have long been favoured in strategies based on chemical absorption aimed at CO2 capture and thus reduction of its harmful environmental impacts. However, some drawbacks, such as toxicity, low stability and high cost limit widespread adoption of this technology. New and green solvents are a possible solution to this issue. As a part of the present study, CO2 solubility in aqueous solutions of different molar ratios of MEA and glycerol as a green solvent was studied. The CO2 absorption was performed at three different temperatures (303, 318, and 333K) at normal atmospheric pressure, while CO2 partial pressure was varied from 1 to 15kPa, and the gas flow rate in the mixture was changed from 350 to 700ml/min. The response surface methodology (RSM) based on central composite design (CCD) was used to design the experiment and explore the effects of four independent parameters (molar concentration of MEA, molar concentration of glycerol, temperature, and gas flow rate) on the solubility of CO2 in solution. Analysis of variance (ANOVA) results showed a good agreement between the experimental data and the statistical model. The maximum solubility occurred for 4M MEA+2M glycerol at 333K and 350ml/min CO2 flow rate. The CO2 solubility values pertaining to different pressures and concentrations were in good agreement with the general absorption trend. However, as the temperature increased, so did the loading. The findings further revealed that the optimum CO2 solubility was obtained at low glycerol to MEA ratio, as this ensured the solubility at elevated temperatures. As glycerol is a viscous fluid, it can be confirmed that it is a suitable solvent at low pressures and high temperatures. Therefore, it can be a viable alternative solution for post-combustion CO2 capture. In addition, an artificial neural network (ANN) model and correlations of CO2 solubility with CO2 partial pressure for all the studied solvent mixtures were developed in this study. Both the ANN model and the correlations fit the experimental CO2 solubility data reasonably well.

CO2 absorption using aqueous solution of potassium carbonate: Experimental measurement and thermodynamic modeling

Potassium carbonate solution is a widely used solvent for CO2 removal due to its benefit in energy consumption and other economic concerns like degradation and corrosion problem. In this study, the solubility of CO2 in aqueous solutions of potassium carbonate was measured using pressure-decay method at temperatures of 313.15 K, 323.15 K, and 333.15 K, different pressures (up to 1.2 MPa), and different solution concentrations of 15 %wt., 20 %wt., and 30 %wt. Also, two equations of state were utilized to anticipate CO2 solubility in aqueous solution of potassium carbonate. The model is a combination of chemical equilibrium in the liquid phase and physical equilibrium between the liquid and vapor phases. For vapor-liquid equilibrium calculations, Peng-Robinson equation of state (PR-EOS) was used to present the fugacity coefficient in the vapor phase. The activity coefficient in the liquid phase was presented by Pitzer equation. Also, the values of the primitive interaction coefficients (λCO2,K+ and μCO2,CO2,K+) were optimized using the available literature data of the similar system. The results demonstrated that the performance of the thermodynamic modeling procedure was acceptable and the average absolute relative deviation (AARD) between the experimental and the predicted data was less than 2.7%.

Comparison of two soft computing approaches for predicting CO2 solubility in aqueous solution of piperazine

Removal of sour gases such as carbon dioxide (CO2) and hydrogen sulfide (H2S) is of great importance in various processes such as streams of sour natural gas, production of ammonia by treating synthesis gas, and hydrogen purification processes. Hence, developing accurate tools for prediction of solubility of CO2 in aqueous solutions of different substrates seems to be of great importance. This study aims to develop two reliable models namely CHPSO-ANFIS and CSA-LSSVM for prediction of CO2 solubility in aqueous solution of mixture of 1,4-diazacyclohexane (piperazine, PZ). The experimental data were gathered from several published works in the literature. The effectiveness and accuracy of the proposed models were evaluated by both statistical and graphical approaches. Results show that the developed CHPSO-ANFIS model presents more accurate and reliable predictions compared to the outcomes of CSA-LSSVM model. The overall R2 and AARD% values for CHPSO-ANFIS model were 0.9902 and 3.76 which indicate accuracy and robustness of the developed model.

CO2 solubility in aqueous solutions of potassium prolinate and (potassium prolinate + 2-amino-2-methyl-1-propanol) as new absorbents

In current work aqueous solution of potassium prolinate (KPr) and potassium prolinate blended with 2-amino-2-methyl-1-propanol (AMP) have been exploited in order to investigate the possible use of these salts of amino acid blended with alkanolamine for CO2 capturing. The equilibrium solubility data of CO2 for KPr blended with AMP solutions at 1.0, 4.0 and 10.0 wt% overall mass concentration are experimentally measured with an equilibrium cell at CO2 partial pressure between 5.2 and 2583.9 kPa and temperatures ranging from 293.15 to 323.15 K. Measured data show that with increase in temperature and concentration of KPr and KPr blended with AMP, loading capacity decreases. Obtained CO2 loadings are very high at low concentration of KPr. In order to predict CO2 solubility in KPr blended with AMP, Deshmkh-Mather model is extended. The values of MSE, ARD and R2 for the extended Deshmukh-Mather model are 8.8092, 0.0239 and 0.9864, respectively.

Experimental and theoretical study of carbon dioxide solubility in aqueous solution of potassium glycinate blended with piperazine as new absorbents

In this work aqueous solution of potassium glycinate, piperazine and potassium glycinate blended with piperazine have been utilized through CO2 solubility measurements in order to investigate the possible use of these salts of amino acid blended with alkanolamine for CO2 absorption. The equilibrium solubility of CO2 for aqueous solutions of potassium glycinate, piperazine and potassium glycinate blended with piperazine at 1.0, 4.0 and 10.0wt.% overall mass concentration are experimentally measured with an equilibrium cell at CO2 partial pressure ranging from 5.1 to 2508.7kPa and temperatures between 293.15 and 323.15K. Obtained data show that loading capacity decreases with increase in temperature and concentration of potassium glycinate and potassium glycinate blended with piperazine. Obtained CO2 loadings are very high at low concentration of potassium glycinate. In order to predict CO2 solubility in aqueous solutions potassium glycinate, piperazine and potassium glycinate blended with piperazine, Deshmkh-Mather model is extended. The values of MSE, ARD and R2 for the extended Deshmukh-Mather model are 0.0741, 8.9582 and 0.9826, respectively. In addition, an artificial neural network (ANN) is developed to predict CO2 solubility in aqueous solutions of piperazine, potassium glycinate and potassium glycinate blended with piperazine. The values of MSE, ARD and R2 for the optimal trained ANN are 0.0179, 4.1528 and 0.9962, respectively.

CO2 solubility in aqueous solutions containing Na+, Ca2+, Cl−, SO42− and HCO3-: The effects of electrostricted water and ion hydration thermodynamics

Dissolution of CO2 into deep subsurface brines for carbon sequestration is regarded as one of the few viable means of reducing the amount of CO2 entering the atmosphere. Ions in solution partially control the amount of CO2 that dissolves, but the mechanisms of the ion's influence are not clearly understood and thus CO2 solubility is difficult to predict. In this study, CO2 solubility was experimentally determined in water, NaCl, CaCl2, Na2SO4, and NaHCO3 solutions and a mixed brine similar to the Bravo Dome natural CO2 reservoir; ionic strengths ranged up to 3.4 molal, temperatures to 140 °C, and CO2 pressures to 35.5 MPa. Increasing ionic strength decreased CO2 solubility for all solutions when the salt type remained unchanged, but ionic strength was a poor predictor of CO2 solubility in solutions with different salts. A new equation was developed to use ion hydration number to calculate the concentration of electrostricted water molecules in solution. Dissolved CO2 was strongly correlated (R2 = 0.96) to electrostricted water concentration. Strong correlations were also identified between CO2 solubility and hydration enthalpy and hydration entropy. These linear correlation equations predicted CO2 solubility within 1% of the Bravo Dome brine and within 10% of two mixed brines from literature (a 10 wt % NaCl + KCl + CaCl2 brine and a natural Na+, Ca2+, Cl− type brine with minor amounts of Mg2+, K+, Sr2+ and Br−).

Prediction of acid gas solubility in amine, ionic liquid and amino acid salt solutions using artificial neural network and evaluating with new experimental measurements

In this work presented here attempt is prediction of acid gases (carbon dioxide and hydrogen sulfide) loading capacities by employing artificial neural network (ANN) model in 51 single and blended alkanolamine, ionic liquid and amino acid salt solutions as commonly and new industrial absorbents in large domain of operational conditions. Also for evaluating extrapolation capability of ANN, new experimental data on CO2 solubility in aqueous solutions of Potassium Glycinate blended with Piperazine (PZ) and 2-amino-2-methyl-1-propanol (AMP) at different temperatures and pressures are measured. It should be mention that CO2 solubility data for these two solutions are not available in literature. For developing ANN, solution pH, total mass concentration, partial pressure of CO2 and H2S, apparent molecular weight, critical temperature, critical pressure and temperature are assumed as inputs. A band of 2982 experimental data points for CO2 and H2S loading capacities have been collected from literature to create the suggested ANN. The best structure of the suggested network is achieved by employing these literature data points. The network is trained by algorithm of Levenberg–Marquardt back-propagation, consists of 9 and 6 neurons in first and second hidden layers, respectively. For the hidden and output layers, Tan-sigmoid transfer function is utilized. The output results of developed network show that suggested network that is created with solubility data of single and blended alkanolamine, ionic liquid and amino acid salt solutions has capability to predict accurately CO2 and H2S loading capacities in dissimilar commonly and new industrial solutions with Average Relative Deviation (ARD %) equal to 2.7992, Mean Square Error (MSE) value of 3.7468 × 10−5 and correlation coefficient (R2) equal to 0.9984.

VLE Modeling of Aqueous Solutions of Unloaded and Loaded Hydroxides of Lithium, Sodium and Potassium

This work focuses on equilibrium modeling of CO2 solubility in aqueous solutions of unloaded (as hydroxides) and loaded (as carbonates) hydroxides containing Li+, Na+ and K+ counter ions. Due to the ionic nature of the LiOH/NaOH/KOH-CO2-H2O solutions, the electrolyte-Non-Random-Two-Liquid (e-NRTL) model, described by Chen and Evans [1], is applied to correct the non-idealities in the liquid phase. The special feature of the equilibrium model employed in this work is the simultaneously regression of Vapor-Liquid Equilibrium (PCO2, Ptotal) and apparent Henry's law constant data selected from literature and Ptotal data for LiOH measured in this work. This approach will give better prediction for the molecular CO2 activity coefficients and the liquid phase activities calculated by use of the resulting e-NRTL model will be consistent with the apparent Henry's law constant. The equilibrium model developed in this work is based on and represented 112 data points for Li+ with 2.7% total AARD, 432 selected data points for Na+ with total 7.2% AARD and 354 selected data points for K+ with total 9.9% AARD. The total pressure data over aqueous LiOH solutions were measured for a range of concentrations (0.25 – 8.5wt. %) and temperatures (40 – 90°C) using an ebulliometer.