Estimating Hansen solubility parameters of organic pigments by group contribution methods

The Hansen solubility parameters(HSP)are frequently used for solvent selection and characterization of polymers,and are directly related to the suspension behavior of pigments in solvent mixtures.The performance of currently available group contribution(GC)methods for HSP were evaluated and found to be insufficient for computer-aided product design(CAPD)of paints and coatings.A revised and,for this purpose,improved GC method is presented for estimating HSP of organic compounds,intended for organic pigments.Due to the significant limitations of GC methods,an uncertainty analysis and parameter confidence intervals are provided in order to better quantify the estimation accuracy of the proposed approach.Compared to other applicable GC methods,the prediction error is reduced significantly with average absolute errors of 0.45 MPa^(1/2),1.35 MPa^(1/2),and 1.09 MPa^(1/2) for the partial dispersion(δD),polar(δP)and hydrogen-bonding(δH)solubility parameters respectively for a database of 1106 compounds.The performance for organic pigments is comparable to the overall method performance,with higher average errors forδD and lower average errors forδP andδH.

A Group Contribution Method for the Correlation of Static Dielectric Constant of Ionic Liquids

Static dielectric constant is a key parameter to estimate the electro-viscous effect which plays important roles in the flow and convective heat transfer of fluids with ions in microfluidic devices such as micro reactors and heat exchangers.A group contribution method based on 27 groups is developed for the correlation of static dielectric constant of ionic liquids in this paper.The ionic liquids considered include imidazolium,pyridinium,pyrrolidinium,alkylammonium,alkylsulfonium,morpholinium and piperidinium cations and various anions.The data collected cover the temperature ranges of 278.15-343.15 K and static dielectric constant ranges of 9.4-85.6.The results of the method show a satisfactory agreement with the literature data with an average absolute relative deviation of 7.41%,which is generally of the same order of the experimental data accuracy.The method proposed in this paper provides a simple but reliable approach for the prediction of static dielectric constant of ionic liquids at different temperatures.

Group Contribution Method Supervised Neural Network for Precise Design of Organic Nonlinear Optical Materials

To rationalize the design of D-π-A type organic small-molecule nonlinear optical materials,a theory guided machine learning framework is constructed.Such an approach is based on the recognition that the optical property of the molecule is predictable upon accumulating the contribution of each component,which is in line with the concept of group contribution method in thermodynamics.To realize this,a Lewis-mode group contribution method(LGC)has been developed in this work,which is combined with the multistage Bayesian neural network and the evolutionary algorithm to constitute an interactive framework(LGC-msBNN-EA).Thus,different optical properties of molecules are afforded accurately and efficientlyby using only a small data set for training.Moreover,by employing the EA model designed specifically for LGC,structural search is well achievable.The origins of the satisfying performance of the framework are discussed in detail.Considering that such a framework combines chemical principles and data-driven tools,most likely,it will be proven to be rational and efficient to complete mission regarding structure design in related fields.

Prediction of Flash Point Temperature of Organic Compounds Using a Hybrid Method of Group Contribution ＋ Neural Network ＋ Particle Swarm Optimization

The flash points of organic compounds were estimated using a hybrid method that includes a simple group contribution method (GCM) implemented in an artificial neural network (ANN) with particle swarm optimization (PSO). Different topologies of a multilayer neural network were studied and the optimum architecture was determined. Property data of 350 compounds were used for training the network. To discriminate different substances the molecular structures defined by the concept of the classical group contribution method were given as input variables. The capabilities of the network were tested with 155 substances not considered in the training step. The study shows that the proposed GCM+ANN+PSO method represent an excellent alternative for the estimation of flash points of organic compounds with acceptable accuracy (AARD = 1.8%; AAE = 6.2 K).

Evaluation of working fluids for organic Rankine cycles using group-contribution methods and second-law-based models

The group-contribution (GC) methods suffer from a limitation concerning to the prediction of process-related indexes, e.g., thermal efficiency. Recently developed analytical models for thermal efficiency of organic Rankine cycles (ORCs) provide a possibility of overcoming the limitation of the GC methods because these models formulate thermal efficiency as functions of key thermal properties. Using these analytical relations together with GC methods, more than 60 organic fluids are screened for medium-low temperature ORCs. The results indicate that the GC methods can estimate thermal properties with acceptable accuracy (mean relative errors are 4.45%-11.50%);the precision, however, is low because the relative errors can vary from less than 0.1% to 45.0%. By contrast, the GC-based estimation of thermal efficiency has better accuracy and precision. The relative errors in thermal efficiency have an arithmetic mean of about 2.9% and fall within the range of 0-24.0%. These findings suggest that the analytical equations provide not only a direct way of estimating thermal efficiency but an accurate and precise approach to evaluating working fluids and guiding computer-aided molecular design of new fluids for ORCs using GC methods.

Determination and Correlation for Solubility of Aromatic Acids in Solvents

Solubility of benzoic acid, terephthalic acid and 2,6-naphthalene dicarboxylic acid in water, acetic acid, N.N-dimethylformamide, N.N-dimethylacetamide, dimethyl sulphoxide and Ar-methyl-2-ketopyrrolidine were measured by dynamic method. The solubilities were calculated by UNIFAC group contribution method, in which new groups, BCCOOH and NCCOOH, were introduced to express the activity coefficients of aromatic acids and new interaction parameters of the new groups were expressed as the function of temperature, which were determined from the experimental data. The new interaction parameters provided good calculated result. The experimental data were also correlated with Wilson and y-h models, and results were compared with present UNIFAC model.

Measurement and Correlation for Solubility of Dimethyl-2,6-naphthalene Dicarboxylate in Organic Solvents

Solubility of dimethyl-2,6-naphthalene dicarboxylate in acetic acid, N,N-dimethylfonnamide, N,N-dimethyl acetamide, dimethyl sulphoxide, and N-methyl-2-ketopyrrolidine were determined using a dynamic method. The measured systems were correlated by UNIFAC group contribution method. A new main group （aromatic ester, ACCOO） was defined to express the activity coefficients of the aromatic ester. New interaction parameters of the ACCOO group were expressed as the first-order function of temperature and were determined from the experimental data. The calculated results for the new interaction parameters were satisfactory. The measured systems were also correlated with the Wilson and 2-h models, and the results were compared with those of the UNIFAC model.

A stochastic reconstruction strategy based on a stratified library of structural descriptors and its application in the molecular reconstruction of naphtha

Molecular reconstruction is a rapid and reliable way to provide molecular detail of petroleum fractions,which is required in the kinetic modeling of petroleum conversation processes at the molecular level.In the typical stochastic reconstruction method,the estimation of properties of pseudo molecules that are generated by Monte Carlo sampling depends on the building of predefined molecular libraries,which is expensive and inaccessible for certain petroleum fractions.In this paper,a novel stochastic reconstruction strategy is proposed,which is based on a stratified library of structural descriptors.Properties of pseudo molecules generated in the novel strategy can be directly estimated by group contribution method in the condition of lacking predefined molecular libraries.In this strategy,the molecular building diagram comprises two steps.First,the ring structure is configured by determining the number of rings.Different from the length of chain adopted in the traditional stochastic reconstruction method,in the second step,number of structural descriptors(SDs)for binding site and chain were determined sequentially for the configuration of binding site and saturated acyclic hydrocarbon chain.These structural descriptors for binding site and chain were selected from group contribution methods.To count the number of partial overlapping sections between structural descriptors for chain,two supplementary structural descriptors were created.All possible saturated structures of hydrocarbon chains can be represented by structural descriptors at the scale of property estimation.This strategy separates the building of a predefined molecule library from the stochastic reconstruction process.The exact structures of pseudo molecules represented by structural descriptors in this work can be determined with sufficient chemical knowledge.Fifty naphtha samples are tested independently to demonstrate the performance of the proposed strategy and the results show that the estimated properties were close enough to the experimental values.This strategy will benefit the molecular management of petrochemical industries and therefore improve economic and environmental efficiencies.

Effects of Phenyl Content and Molecular Structure on the Refractive Index of Organic Silicon Polymer

The effects of phenyl content and group distribution on the refractive index of phenyl silicone oil were investigated by synthesis and characterization of silicone oils of different molecular structures.A group contribution function model was established to predict the refractive index. The results showed that refractive index of phenyl silicone oil increased as its phenyl content increased. A linear equation had been established to describe the quantitative relationship between the refractive index and phenyl content.Refractive index values from the group contribution function model showed good consistency with experimental results.

Enthalpy of phase transition of isonicotinic acid

In this work, the group contribution method of Chickos et al. was applied to estimate the fusion enthalpy of isonicotinic acid, and the obtained result(29.2 k J·mol^(-1)) showed a large difference with the value(135 k J·mol^(-1)) as reported from literatures and as determined by differential scanning calorimetry(DSC). The results of DSC/TG measurement showed that the phase transition of isonicotinic acid from 187.27 °C to277.47 °C underwent a sublimation process, with a sublimation enthalpy of 128.03 k J·mol^(-1). An efficient analytical technique combining pyrolysis and gas chromatography/mass spectrometry(Py-GC/MS) was used to prove this conclusion.

Estimation of Enthalpy of Formation Using Benson’s Group Addition and Functional Group Correction

The enthalpies of formation of solid organic compounds containing carbon,nitrogen,oxygen,and hydrogen were estimated using two suggested descriptor sets,separately,by machine learning methods.The two descriptor sets are both composed of descriptors of Benson’s groups and corrected groups.The main differences between them are that one is generated based on atoms and the other is based on bonds.An in-house program was specially written in Java to extract all the descriptors with a function to ensure that each atom(or bond)of a molecule is represented by Benson’s groups once for an atom-based(or bond-based)descriptor set.Multiple linear regression and partial least squares were used,separately,to build models to predict the enthalpy of formation for two descriptor sets.The combination of the models constructed by two descriptor sets based on the atoms and the bonds achieved the best-predicted results in this paper,and the corresponding results of the test set are better than that in the literature,from which the original data were retrieved.Further,a small data set of fluorinated molecules was collected,and satisfactory results were also obtained for these molecules containing fluorine with the assistance of the former data set.

Temperature Dependence of Density,Viscosity,Thermal Conductivity and Heat Capacity of Vegetable Oils for Their Use as Biofuel in Internal Combustion Engines

This work gives tools to overcome the difficulty to determine experimentally physical properties for vegetable oils within the range of temperature typically observed during the injection phase in a diesel engine. Knowing vegetable oils’ physical properties to these ranges of temperature is of fundamental importance when modeling their combustion in diesel engine. However, vegetable oils’ experimental physical properties data are rare in the literature for temperature above 523 K. This paper describes experimental measurements and estimation methods for density, dynamic viscosity, thermal conductivity and heat capacity of vegetable oils for this particular range of temperature. The methodology uses several correlative methods using group contribution approach for each property and compares experimental data with predicted one to select the more accurate model. This work has shown the rapeseed and jatropha oils’ physical properties can be satisfactorily predicted as a function of temperature using group contribution approach.

Machine learning models for the density and heat capacity of ionic liquid-water binary mixtures

Ionic liquids(ILs),because of the advantages of low volatility,good thermal stability,high gas solubility and easy recovery,can be regarded as the green substitute for traditional solvent.However,the high viscosity and synthesis cost limits their application,the hybrid solvent which combining ILs together with others especially water can solve this problem.Compared with the pure IL systems,the study of the ILs-H_(2)O binary system is rare,and the experimental data of corresponding thermodynamic properties(such as density,heat capacity,etc.)are less.Moreover,it is also difficult to obtain all the data through experiments.Therefore,this work establishes a predicted model on ILs-water binary systems based on the group contribution(GC)method.Three different machine learning algorithms(ANN,XGBoost,LightBGM)are applied to fit the density and heat capacity of ILs-water binary systems.And then the three models are compared by two index of MAE and R^(2).The results show that the ANN-GC model has the best prediction effect on the density and heat capacity of ionic liquid-water mixed system.Furthermore,the Shapley additive explanations(SHAP)method is harnessed to scrutinize the significance of each structure and parameter within the ANN-GC model in relation to prediction outcomes.The results reveal that system components(XIL)within the ILs-H_(2)O binary system exert the most substantial influence on density,while for the heat capacity,the substituents on the cation exhibit the greatest impact.This study not only introduces a robust prediction model for the density and heat capacity properties of IL-H_(2)O binary mixtures but also provides insight into the influence of mixture features on its density and heat capacity.