Solubility measurement and prediction of phase equilibria in the quaternary system LiCl+NaCl+KCl+H2O and ternary subsystem LiCl+NaCl+H2O at 288.15 K

Using isothermal dissolution method,the phase equilibrium relationship in quaternary system LiCl+NaCl+KCl+H2O and the ternary subsystem LiCl+NaCl+H2O at 288.15 K were investigated.Each phase diagram of two systems was drawn.The phase diagram of LiCl+NaCl+H2O system contains two solid phase regions of crystallization LiCl·2H2O and NaCl.In the phase diagram of LiCl+NaCl+KCl+H2O system,there are three crystallization regions:LiCl·2H2O,NaCl and KCl respectively.In this paper,the solubilities of phase equilibria in two systems were calculated by Pitzer's model at 288.15 K.The predicted phase diagrams generally agree with the experimental phase diagrams.

Machine learning-based solubility prediction and methodology evaluation of active pharmaceutical ingredients in industrial crystallization

Solubility has been widely regarded as a fundamental property of small molecule drugs and drug candidates,as it has a profound impact on the crystallization process.Solubility prediction,as an alternative to experiments which can reduce waste and improve crystallization process efficiency,has attracted increasing attention.However,there are still many urgent challenges thus far.Herein we used seven descriptors based on understanding dissolution behavior to establish two solubility prediction models by machine learning algorithms.The solubility data of 120 active pharmaceutical ingredients(APIs)in ethanol were considered in the prediction models,which were constructed by random decision forests and artificial neural network with optimized data structure and model accuracy.Furthermore,a comparison with traditional prediction methods including the modified solubility equation and the quantitative structure-property relationships model was carried out.The highest accuracy shown by the testing set proves that the ML models have the best solubility prediction ability.Multiple linear regression and stepwise regression were used to further investigate the critical factor in determining solubility value.The results revealed that the API properties and the solute-solvent interaction both provide a nonnegligible contribution to the solubility value.

Application of the Ion-interaction Model to the Solubility Prediction of LiCl-HCl-MgCl2-H2O System at 20℃

Component solubility in HCl-LiCl-MgCl2-H2O system of high ionic strength at 20℃ was predicted by using the Pitzer＇s ion-interaction model. The results indicated that the model supplied a very good prediction of the component solubility of the system mentioned above. The values of parameters of β^0, β^1 and C^＊ of HCl, LiCl and MgCl2 were obtained from optimization of literature data, while those of θMN and ψMNX were calculated from a least-squares optimization procedure to couple activity coefficient with solubility data. According to the ion-interaction model, no additional parameters need to be determined for more complex systems. The study provided theoretical basis for the manufacture process, which was proposed by Gao and employed to extract LiCl and MgCl2·6H2O from salt lake brine.

Study of the toluene absorption capacity and mechanism of ionic liquids using COSMO-RS prediction and experimental verification

As green solvents,ionic liquids(ILs)are quite suitable for the absorption of volatile organic compounds(VOCs)such as benzene and its homologues.However,solvent selection is the key to the VOC absorption process.In the present study,a rapid solvent screening tool,Conductor-like Screening Model for Real Solvents(COSMO-RS),was used to predict the solubility of toluene in 816 ILs.The effects of four structure characters,namely,the type and alkyl chain length of the cations and anions on the solubility of toluene were discussed.The following conclusions were drawn from the results:(1)ILs with pyrrolidinium-based cations showed better solubility than pyridinium-and imidazoliumbased ones.(2)The solubility of toluene in PF6-based ILs increased with the increasing alkyl chain length,while its solubility in Ac-based ILs exhibited the opposite trend.(3)Toluene showed greater solubility in Cl-based ILs than those based on other anions.(4)The solubility of toluene increased with the anion alkyl chain length.Ac-based ILs were chosen as the most promising potential solvents,and further studied to determine the relationship between various interaction energy parameters and toluene solubility.The results showed that the misfit energy played a dominant role during the absorption process.Furthermore,several ILs were selected for experimental verification of the predicted solubility behavior using liquid and gaseous toluene.The results demonstrated that COSMO-RS could be used to semi-quantitatively and qualitatively predict the solubility of toluene,and this model had promising prospects in screening ILs for VOCs absorption.In summary,this study provided a fundamental basis and practical data for the control and treatment of VOCs.

Prediction of CO2 Solubility in Polymers by Radial Basis Function Artificial Neural Network Based on Chaotic Self-adaptive Particle Swarm Optimization and Fuzzy Clustering Method

To replace costly and time-consuming experimentation in laboratory, a novel solubility prediction model based on chaos theory, self-adaptive particle swarm optimization （PSO）, fuzzy c-means clustering method, and radial ba- sis function artificial neural network （RBF ANN） is proposed to predict CO2 solubility in polymers, hereafter called CSPSO-FC RBF ANN. The premature convergence problem is overcome by modifying the conventional PSO using chaos theory and self-adaptive inertia weight factor. Fuzzy c-means clustering method is used to tune the hidden centers and radial basis function spreads. The modified PSO algorithm is employed to optimize the RBF ANN connection weights. Then, the proposed CSPSO-FC RBF ANN is used to investigate solubility of CO2 in polystyrene （PS）, polypropylene （PP）, poly（butylene succinate） （PBS） and poly（butylene succinate-co-adipate） （PBSA）, respec- tively. Results indicate that CSPSO-FC RBF ANN is an effective method for gas solubility in polymers. In addition, compared with conventional RBF ANN and PSO ANN, CSPSO-FC RBF ANN shows better performance. The values of average relative deviation （ARD）, squared correlation coefficient （R2） and standard deviation （SD） are 0.1071, 0.9973 and 0.0108, respectively. Statistical data demonstrate that CSPSO-FC RBF ANN has excellent prediction capability and high-accuracy, and the correlation between prediction values and experimental data is good.

Predictive phase equilibria for the aqueous ternary system(Li_2SO_4 + K_2SO_4 + H_2O) from 273.15 to 373.15 K

1 Introduction Many variable temperature chemical models were developed to predict mineral solubility in the natural waters(Na+,K+,Ca2+,Mg2+//Cl-,SO42-–H2O)in the temperature range below 298.15 K(to near 213.15 K)and(Na+,K+,

Prediction of CO_(2) solubility in deep eutectic solvents using random forest model based on COSMO-RS-derived descriptors

This work presents the development of molecular-based mathematical model for the prediction of CO_(2) solubility in deep eutectic solvents(DESs).First,a comprehensive database containing 1011 CO_(2) solubility data in various DESs at different temperatures and pressures is established,and the COSMO-RS-derived descriptors of involved hydrogen bond acceptors and hydrogen bond donors of DESs are calculated.Afterwards,the efficiency of the input variables,i.e.,temperature,pressure,COSMO-RS-derived descriptors of HBA and HBD as well as their molar ratio,is explored by a qualitative analysis of CO_(2) solubility in DESs using a simple multiple linear regression model.A machine learning method namely random forest is then employed to develop more accurate nonlinear quantitative structure-property relationship(QSPR)model.Combining the QSPR validation and comparisons with literature-reported models(i.e.,COSMO-RS model,traditional thermodynamic models and equations of state methods),the developed QSPR model with COSMO-RS-derived parameters as molecular descriptors is suggested to be able to give reliable predictions of CO_(2) solubility in DESs and could be used as a useful tool in selecting DESs for CO_(2) capture processes.