Solvent Selection for Extractive Distillation by the Universal Quasichemical Functional Group Activity Coefficient(UNIFAC) Group Contribution Method

A method to select solvent for extractive distillation is proposed by UNIFAC group contribution. Solventselectivity can be divided into two parts: the partial combinatorial solvent selectivity and the partial residual solventselectivity. The properties of partial combinatorial and residual solvent selectivity are demonstrated. In most cases,the partial residual solvent selectivity is predominant. The candidate groups of solvent can be selected by groupinteraction parameter using UNIFAC group interaction parameter table as a guide.

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).

Molecular reconstruction model based on structure oriented lumping and group contribution methods

Molecular management is a promising technology to face challenges in the refining industry, such as more stringent requirements for product oil and heavier crude oil, and to maximize the value of every molecule in petroleum fractions. To achieve molecular management in refining processes, a novel model that is based on structure oriented lumping(SOL) and group contribution(GC) methods was proposed in this study. SOL method was applied to describe a petroleum fraction with structural increments, and GC method aimed to estimate molecular properties. The latter was achieved by associating rules between SOL structural increments and GC structures. A three-step reconstruction algorithm was developed to build a representative set of molecules from partial analytical data. First, structural distribution parameters were optimized with several properties. Then, a molecular library was created by using the optimized parameters. In the final step, maximum information entropy(MIE) method was applied to obtain a molecular fraction. Two industrial samples were used to validate the method, and the simulation results of the feedstock properties agreed well with the experimental data.

Position Group Contribution Method for Predicting the Normal Boiling Point of Organic Compounds

A new position group contribution model is proposed for the estimation of normal boiling data of organic compounds involving a carbon chain from C2 to C18.The characteristic of this method is the use of position distribution function.It could distinguish most of isomers that include cis-or trans-structure from organic compounds.Contributions for hydrocarbons and hydrocarbon derivatives containing oxygen,nitrogen,chlorine,bromine and sulfur,are given.Compared with the predictions,results made use of the most common existing group contribution methods,the overall average absolute difference of boiling point predictions of 417 organic compounds is 4.2 K;and the average absolute percent derivation is 1.0%,which is compared with 12.3 K and 3.2% with the method of Joback,12.1 K and 3.1% with the method of Constantinou-Gani.This new position contribution groups method is not only much more accurate but also has the advantages of simplicity and stability.

Estimation of Acentric Factor of Organic Compounds with Corresponding States Group Contribution Method

A new estimation method was proposed by combining the corresponding state principle with the group contribution method through introducing the concept of assumed-critical properties. Combining it with the Reidel equation, a new acentric factor correlation equation (CSGC-Reidel) was developed. Contribution values of 70 groups were obtained by correlating acentric factor data of 228 organic compounds of 14 type substances including saturated hydrocarbons, unsaturated hydrocarbons, cyclanes, aromatics, oxygen compounds, nitrogen compounds,halohydrocarbons, etc. The average error of acentric factor is 3.52% between the literature data and the predicated with the new estimation method.

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.

Estimation of Octanol-Water Partition Coefficient of Chloride Hydrocarbon by Group Contribution Method

A novel method named two-level group contribution （GC-K） method for the estimation of octanol-water partition coefficient （Kow） of chloride hydrocarbon is presented. The equation includes only normal boiling points and molecular weight of compounds. Group contribution parameters of 12 first-level groups and 7 second-level groups for Kow are obtained by correlating experimental data of three types including 57 compounds. By comparing the estimation results of the first-level with that of the two-level groups, it was observed that the latter is better with the addition of the modification of proximity effects. When compared with Marrero＇s three-level group contribution approach and atom-fragment contribution method （AFC）, the accuracy of the average relative error of GC-K by first-level groups is 7.20% and is preferred to other methods.

Position Group Contribution Method for Estimation of Melting Point of Organic Compounds

A new method is proposed based on the position group contribution additivity for the prediction of melting points of covalent compounds. The characteristics of this method are the use of position distribution func-tion, which could distinguish between most isomers including cis or trans structure of organic compounds. Contri-butions for hydrocarbons and hydrocarbon derivatives containing oxygen, nitrogen, chlorine, bromine and sulfur, are given. Results are compared with those by the most commonly used estimating methods. The average derivation for prediction of normal melting temperature of 730 compounds is 14.46 K, compared to 29.33 K with the method of Joback, and 27.81 K with the method of Constantinou-Gani. The present method is not only more accurate, but also much simpler and more stable.

Estimation of Formation Enthalpies of Organic Pollutants from a New Structural Group Contribution Method

Chemical stability and reactivity of organic pollutants is dependent to their formation enthalpies. The main objective of this study is to provide simple straightforward strategy for prediction of the formation enthalpies of wide range organic pollutants only from their structural functional groups. Using such an extended dataset cornprising 1694 organic chemicals from 77 diverse material classes benefits the generalizability and reliability of the study. The new suggested collection of 12 functional groups and a simple linear regression lead to promising statis- tics of R2= 0.958, Q2 =0.956, and AEE= 57 kJ.mo1-1 for the whole dataset. Moreover, unknown experimental formation enthalpies for 27 organic pollutants are estimated by the presented approach. The resultant model needs no technical software/calculations, and thus can be easily applied by a non-specialist user.

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.

A new group contribution-based method for estimation of flash point temperature of alkanes

Flash point is a primary property used to determine the fire and explosion hazards of a liquid. New group contribution-based models were presented for estimation of the flash point of alkanes by the use of multiple linear regression(MLR)and artificial neural network(ANN). This simple linear model shows a low average relative deviation(AARD) of 2.8% for a data set including 50(40 for training set and 10 for validation set) flash points. Furthermore, the predictive ability of the model was evaluated using LOO cross validation. The results demonstrate ANN model is clearly superior both in fitness and in prediction performance.ANN model has only the average absolute deviation of 2.9 K and the average relative deviation of 0.72%.

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.

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.

Group Contribution based Flammability Limit Estimation of Hydrocarbon-Inert Gas Mixture

As group contribution method is easy to apply and has a wide application range,current study has developed this model to predict flammability limit of hydrocarbons mixed with inert gas using the Marrero/Gani group contribution method,which is significative to the safe application of hydrocarbons in the ORC system.The whole modeling process is divided into two parts:pure compound prediction and mixture prediction.The contribution factors of inert gases and dilute concentration were first introduced in the group contribution method.Moreover,the respective 95%-confidence interval of the mixture based on linear superposition method has been proposed in the developed group contribution model to improve the safety coefficient.For CO2 as inert gas,the average relative error and correlation coefficient are 5.34%and 0.88 for lower flammability limit while 6.99%and 0.95 for upper flammability limit.For N2 as inert gas,the average relative error and correlation coefficient are 7.47%and 0.84 for lower flammability limit while 6.68%and 0.97 for upper flammability limit.Most importantly,this group contribution method has extended the application range to make up the shortcomings of other flammability limit prediction methods aiming at hydrocarbon and inert gas mixtures and proposed the uncertainty analysis to provide reliable prediction range.

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.

An Improved Machine Learning Model for Pure Component Property Estimation

Information on the physicochemical properties of chemical species is an important prerequisite when performing tasks such as process design and product design.However,the lack of extensive data and high experimental costs hinder the development of prediction techniques for these properties.Moreover,accuracy and predictive capabilities still limit the scope and applicability of most property estimation methods.This paper proposes a new Gaussian process-based modeling framework that aims to manage a discrete and high-dimensional input space related to molecular structure representation with the group-contribution approach.A warping function is used to map discrete input into a continuous domain in order to adjust the correlation between different compounds.Prior selection techniques,including prior elicitation and prior predictive checking,are also applied during the building procedure to provide the model with more information from previous research findings.The framework is assessed using datasets of varying sizes for 20 pure component properties.For 18 out of the 20 pure component properties,the new models are found to give improved accuracy and predictive power in comparison with other published models,with and without machine learning.

Studies on Partial Molar Volumes of Some Amino Acids and Their Groups in Aqueous Solutions from 293.15K to 333.15K

Densities of aqueous solutions of eight amino acids, glycine, L-alanine, L-valine, L-isoleucine, L-serine, L-threonine, L-arginine and L-phenylalanine, are measured as a function of amino acid concentration from 293.15K to 333.15K. These data are used to calculate the apparent molar volume Vφ and infinite dilution apparent molar volume Vφo (partial molar volume). Data of five amino acids are used to correlate partial molar volume Vφo usinggroup contribution method to estimate the contributions of the zwitterionic end groups (NH3+,COO-) and CH2 group, OH group, CNHNHNH2 group and C6H5(phenyl) group of amino acids. The results show that Vφo values for all kinds of groups of amino acids studied increase with increase of temperature except those for CH2 group, which are almost constant within the studied temperature range. Data of other amino acids, L-valine, L-isoleucine and L-threonine, are chosen for comparison with the predicted partial molar volume Vφo using the group additivity parameters obtained. The results confirm that this group additivity method has excellent predictive utility.

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.