Comparison between multi-linear- and radial-basis-function-neural-network-based QSPR Models for the prediction of the critical temperature, critical pressure and acentric factor of organic compounds

Critical properties and acentric factor are widely used in phase equilibrium calculations but are difficult to evaluate with high accuracy for many organic compounds. Quantitative Structure-Property Relationship (QSPR) models are a powerful tool to establish accurate correlation between molecular properties and chemical structure. QSPR multi-linear (MLR) and radial basis-function-neural-network (RBFNN) models have been developed to predict the critical temperature, critical pressure and acentric factor of a database of 306 organic compounds. RBFNN models provided better data correlation and higher predictive capability (an AAD% of 0.92–2.0% for training and 1.7–4.8% for validation sets) than MLR models (an AAD% of 3.2–8.7% for training and 6.2–12.2% for validation sets). The RMSE of the RBFNN models was 20–30% of the MLR ones. The correlation and predictive performances of the models for critical temperature were higher than those for critical pressure and acentric factor, which was the most difficult property to predict. However, the RBFNN model for the acentric factor resulted in the lowest RMSE with respect to previous literature. The close relationship between the three properties resulted from the selected molecular descriptors, which are mostly related to molecular electronic charge distribution or polar interactions between molecules. QSPR correlations were compared with the most frequently used group-contribution methods over the same database of compounds: although the MLR models provided comparable results, the RBFNN ones resulted in significantly higher performance

QSPR calculation of normal boiling points of organic molecules based on the use of correlation weighting of atomic orbitals with extended connectivity of zero- and first-order graphs of atomic orbitals

We report the results of a calculation of the normal boiling points of a representative set of 200 organic molecules through the application of QSPR theory. For this purpose we have used a particular set of flexible molecular descriptors, the so called Correlation Weighting of Atomic Orbitals with Extended Connectivity of Zero- and First-Order Graphs of Atomic Orbitals. Although in general the results show suitable behavior to predict this physical chemistry property, the existence of some deviant behaviors points to a need to complement this index with some other sort of molecular descriptors. Some possible extensions of this study are discussed.SCOPU

Development of Quantitative Structure-Property Relationships (QSPR) using calculated descriptors for the prediction of the physico-chemical properties (nD, r, bp, e and h) of a series of organic solvents.

Quantitative structure-property relationship (QSPR) models were derived for predicting boiling point (at 760 mmHg), density (at 25 \ub0C), viscosity (at 25 \ub0C), static dielectric constant (at 25 \ub0C), and refractive index (at 20 \ub0C) of a series of pure organic solvents of structural formula X-CH2CH2-Y. A very large number of calculated molecular descriptors were derived by quantum chemical methods, molecular topology, and molecular geometry by using the CODESSA software package. A comparative analysis of the multiple linear regression techniques (heuristic and best multilinear regression) implemented in CODESSA, with the multivariate PLS/GOLPE method, has been carried out. The performance of the different regression models has been evaluated by the standard deviation of prediction errors, calculated for the compounds of both the training set (internal validation) and the test set (external validation). Satisfactory QSPR models, from both predictive and interpretative point of views, have been obtained for all the studied properties

Prediction of the physical properties of pure chemical compounds through different computational methods.

Ph. D. University of KwaZulu-Natal, Durban 2014.Liquid thermal conductivities, viscosities, thermal decomposition temperatures, electrical conductivities, normal boiling point temperatures, sublimation and vaporization enthalpies, saturated liquid speeds of sound, standard molar chemical exergies, refractive indices, and freezing point temperatures of pure organic compounds and ionic liquids are important thermophysical properties needed for the design and optimization of products and chemical processes. Since sufficiently purification of pure compounds as well as experimentally measuring their thermophysical properties are costly and time consuming, predictive models are of great importance in engineering. The liquid thermal conductivity of pure organic compounds was the first investigated property, in this study, for which, a general model, a quantitative structure property relationship, and a group contribution method were developed. The novel gene expression programming mathematical strategy [1, 2], firstly introduced by our group, for development of non-linear models for thermophysical properties, was successfully implemented to develop an explicit model for determination of the thermal conductivity of approximately 1600 liquids at different temperatures but atmospheric pressure. The statistical parameters of the obtained correlation show about 9% absolute average relative deviation of the results from the corresponding DIPPR 801 data [3]. It should be mentioned that the gene expression programing technique is a complicated mathematical algorithm and needs a significant computer power and this is the largest databases of thermophysical property that has been successfully managed by this strategy. The quantitative structure property relationship was developed using the sequential search algorithm and the same database used in previous step. The model shows the average absolute relative deviation (AARD %), standard deviation error, and root mean square error of 7.4%, 0.01, and 0.01 over the training, validation and test sets, respectively. The database used in previous sections was used to develop a group contribution model for liquid thermal conductivity. The statistical analysis of the performance of the obtained model shows approximately a 7.1% absolute average relative deviation of the results from the corresponding DIPPR 801 [4] data. In the next stage, an extensive database of viscosities of 443 ionic liquids was initially compiled from literature (more than 200 articles). Then, it was employed to develop a group contribution model. Using this model, a training set composed of 1336 experimental data was correlated with a low AARD% of about 6.3. A test set consists of 336 data point was used to validate this model. It shows an AARD% of 6.8 for the test set. In the next part of this study, an extensive database of thermal decomposition temperature of 586 ionic liquids was compiled from literature. Then, it was used to develop a quantitative structure property relationship. The proposed quantitative structure property relationship produces an acceptable average absolute relative deviation (AARD) of less than 5.2 % taking into consideration all 586 experimental data values. The updated database of thermal decomposition temperature including 613 ionic liquids was subsequently used to develop a group contribution model. Using this model, a training set comprised of 489 data points was correlated with a low AARD of 4.5 %. A test set consisting of 124 data points was employed to test its capability. The model shows an AARD of 4.3 % for the test set. Electrical conductivity of ionic liquids was the next property investigated in this study. Initially, a database of electrical conductivities of 54 ionic liquids was collected from literature. Then, it was used to develop two models; a quantitative structure property relationship and a group contribution model. Since the electrical conductivities of ionic liquids has a complicated temperature- and chemical structure- dependency, the least square support vector machines strategy was used as a non-linear regression tool to correlate the electrical conductivity of ionic liquids. The deviation of the quantitative structure property relationship from the 783 experimental data used in its development (training set) is 1.8%. The validity of the model was then evaluated using another experimental data set comprising 97 experimental data (deviation: 2.5%). Finally, the reproducibility and reliability of the model was successfully assessed using the last experimental dataset of 97 experimental data (deviation: 2.7%). Using the group contribution model, a training set composed of 863 experimental data was correlated with a low AARD of about 3.1% from the corresponding experimental data. Then, the model was validated using a data set composed of 107 experimental data points with a low AARD of 3.6%. Finally, a test set consists of 107 data points was used for its validation. It shows an AARD of 4.9% for the test set. In the next stage, the most comprehensive database of normal boiling point temperatures of approximately 18000 pure organic compounds was provided and used to develop a quantitative structure property relationship. In order to develop the model, the sequential search algorithm was initially used to select the best subset of molecular descriptors. In the next step, a three-layer feed forward artificial neural network was used as a regression tool to develop the final model. It seems that this is the first time that the quantitative structure property relationship technique has successfully been used to handle a large database as large as the one used for normal boiling point temperatures of pure organic compounds. Generally, handling large databases of compounds has always been a challenge in quantitative structure property relationship world due to the handling large number of chemical structures (particularly, the optimization of the chemical structures), the high demand of computational power and very high percentage of failures of the software packages. As a result, this study is regarded as a long step forward in quantitative structure property relationship world. A comprehensive database of sublimation enthalpies of 1269 pure organic compounds at 298.15 K was successfully compiled from literature and used to develop an accurate group contribution. The model is capable of predicting the sublimation enthalpies of organic compounds at 298.15 K with an acceptable average absolute relative deviation between predicted and experimental values of 6.4%. Vaporization enthalpies of organic compounds at 298.15 K were also studied in this study. An extensive database of 2530 pure organic compounds was used to develop a comprehensive group contribution model. It demonstrates an acceptable %AARD of 3.7% from experimental data. Speeds of sound in saturated liquid phase was the next property investigated in this study. Initially, A collection of 1667 experimental data for 74 pure chemical compounds were extracted from the ThermoData Engine of National Institute of Standards and Technology [5]. Then, a least square support vector machines-group contribution model was developed. The model shows a low AARD% of 0.5% from the corresponding experimental data. In the next part of this study, a simple group contribution model was presented for the prediction of the standard molar chemical exergy of pure organic compounds. It is capable of predicting the standard chemical exergy of pure organic compounds with an acceptable average absolute relative deviation of 1.6% from the literature data of 133 organic compounds. The largest ever reported databank for refractive indices of approximately 12 000 pure organic compounds was initially provided. A novel computational scheme based on coupling the sequential search strategy with the genetic function approximation (GFA) strategy was used to develop a model for refractive indices of pure organic compounds. It was determined that the strategy can have both the capabilities of handling large databases (the advantage of sequential search algorithm over other subset variable selection methods) and choosing most accurate subset of variables (the advantages of genetic algorithm-based subset variable selection methods such as GFA). The model shows a promising average absolute relative deviation of 0.9 % from the corresponding literature values. Subsequently, a group contribution model was developed based on the same database. The model shows an average absolute relative deviation of 0.83% from corresponding literature values. Freezing Point temperature of organic compounds was the last property investigated. Initially, the largest ever reported databank in open literature for freezing points of more than 16 500 pure organic compounds was provided. Then, the sequential search algorithm was successfully applied to derive a model. The model shows an average absolute relative deviations of 12.6% from the corresponding literature values. The same database was used to develop a group contribution model. The model demonstrated an average absolute relative deviation of 10.76%, which is of adequate accuracy for many practical applications

QSPR calculation of normal boiling points of organic molecules based on the use of correlation weighting of atomic orbitals with extended connectivity of zero- and first-order graphs of atomic orbitals

We report the results of a calculation of the normal boiling points of a representative set of 200 organic molecules through the application of QSPR theory. For this purpose we have used a particular set of flexible molecular descriptors, the so called Correlation Weighting of Atomic Orbitals with Extended Connectivity of Zero- and First-Order Graphs of Atomic Orbitals. Although in general the results show suitable behavior to predict this physical chemistry property, the existence of some deviant behaviors points to a need to complement this index with some other sort of molecular descriptors. Some possible extensions of this study are discussed.SCOPU

Properties of potential eco-friendly gas replacements for particle detectors in high-energy physics

Gas detectors for elementary particles require F-based gases for optimal performance. Recent regulations demand the use of environmentally unfriendly F-based gases to be limited or banned. This work studies properties of potential eco-friendly gas replacements by computing the physical and chemical parameters relevant for use as detector media, and suggests candidates to be considered for experimental investigation

Industrially Pre-Treated Potatoes (Solanum tuberosum L.) Served in Large-Scale Food Service Systems

In Swedish schools, a warm lunch is served every day. The lunch is prepared in a largescale food service system and includes steam-cooked potatoes several times per week. The potatoes are often industrially pre-treated to facilitate the handling and logistics required to cook hundreds of meals in a couple of hours. During the industrial pretreatment, the potato tubers are usually peeled by abrasion, knife peeling, or a combination of both, followed by preservative actions and packaging. When arriving at the large-scale food service systems, the tubers are usually cooked by steam-cooking (SC) in a combi-steamer (an oven with saturated steam as the medium for heat transfer). To manage logistical issues, the potato tubers might have to be held warm until serving. These processing steps are often very rough, causing stress and mechanical damage to the tubers, followed by a poorly controlled and understood cooking process. Unfortunately, this contributes to an unpleasant product being served with reduced eating quality. Preservative actions are required to prolong the shelf-life and include reduction of enzymatic browning and reduction of microbiological growth. These actions can be performed by chemical treatment with organic acids (OA) and/or sodium metabisulfite (SMS). Textural analyses by puncture of the samples show that tubers treated with OA, SMS, and OA+SMS might develop a tough surface compared to untreated samples, referred to as Ref. Preservation with both OA and SMS prolongs the shelf-life but has shown to reduce the eating quality by contributing to the creation of an unpleasant, tough surface. Textural analysis revealed that the hardness of the surface depends on the chemical pre-treatment, with hardness order of Ref<OA<SMS<OA+SMS. Visual inspection showed that SMS and OA+SMS contributed to the most pronounced tough surface, while analysis by light microscopy revealed that OA developed brick-like cells at the surface, indicating that the different preservative treatments contribute to a tough surface by two different mechanisms. SC is used to cook the samples due to decreased cooking time and easier handling compared to conventional boiling (CB) in a cooking vessel. The cooking degree has shown to mainly depend on the core temperature of the tuber at the end of the cooking process, but differences were also found depending on the cooking method (CB compared to SC) and potato variety. Warm-holding (WH) is often conducted in the combi-steamer with a relative humidity (RH) of 100% at a minimum temperature of 72°C for a maximum of 2 h since this used to be the recommendation in Sweden. During WH, the cooking process continues, where temperature has a larger impact than time. WH can easily cause overcooking, with an unpleasant watery core as a result. However, if tubers that will be warm-held are slightly undercooked, the continuation of the cooking process can easily be adapted to achieve a good eating quality. Control of relative humidity (RH) has shown to be crucial, where too low RH contributes to a tough surface due to evaporation of water, which is a third mechanism causing a tough surface