Average Relative Deviation Values Research Articles

Thermodynamic functions of 1-methyl-4-(methylsulfonyl)benzene solubility in nine organic solvents from T = (278.15 to 318.15) K

The solid-liquid equilibrium for 1-methyl-4-(methylsulfonyl)benzene in nine solvents (methanol, ethanol, acetonitrile, ethyl acetate, acetone, N,N-dimethylformamide, n-butanol, n-propanol, isopropanol) were determined at temperatures from 278.15K to 318.15K by using the isothermal saturation method under atmosphere pressure, and the solubility of 1-methyl-4-(methylsulfonyl)benzene in the solvents were analysed by the high-performance liquid chromatography (HPLC). On the whole, the solubility obeyed the following order from high to low for the selected solvents: N,N-dimethylformamide>acetone>acetonitrile>ethyl acetate>methanol>ethanol>n-propanol>n-butanol>isopropanol. The four thermodynamic models, modified Apelblat equation, λh equation, Wilson model and NRTL model were used to describe the solubility results obtained. The largest values of relative average deviation (RAD) and root-mean-square deviations (RMSD) were 1.48% and 56.85×10−4, respectively. The calculated solubility with the four models agreed well with the experimental values. Furthermore, the mixing properties, including mixing Gibbs energy, mixing enthalpy, mixing entropy, activity coefficient at infinitesimal concentration (γ1∞) and reduced excess enthalpy (H1E,∞) were calculated for the dissolution process of 1-methyl-4-(methylsulfonyl)benzene in these solvents. The mixing process of 1-methyl-4-(methylsulfonyl)benzene was spontaneous and favourable in the selected solvents. The solubility of 1-methyl-4-(methylsulfonyl)benzene in different solvents and thermodynamic relations would be invoked as basic data and models for the purification process of 1-methyl-4-(methylsulfonyl)benzene.

Solubility determination and thermodynamic modelling of 2,4-dihydro-5-methyl-2-(4-methylphenyl)-3H-pyrazol-3-one in twelve organic solvents from T = (278.15 to 313.15) K and mixing properties of solutions

The solubility of 2,4-dihydro-5-methyl-2-(4-methylphenyl)-3H-pyrazol-3-one in twelve organic solvents including methanol, ethanol, n-propanol, isopropanol, ethylbenzene, toluene, n-butanol, acetonitrile, ethyl acetate, 1,4-dioxane, cyclohexane and isopentanol were determined experimentally by using the isothermal saturation method over a temperature range from (278.15 to 313.15)K under 101.3kPa. For the temperature range studied, the solubility of 2,4-dihydro-5-methyl-2-(4-methylphenyl)-3H-pyrazol-3-one in the solvents increased with a rise of temperature. The mole fraction solubility obeyed the following order from high to low in different solvents: isopentanol>1,4-dioxane>n-butanol>n-propanol>ethyl acetate>methanol>ethanol>isopropanol>ethylbenzene>toluene>acetonitrile>cyclohexane. The obtained solubility data of 2,4-dihydro-5-methyl-2-(4-methylphenyl)-3H-pyrazol-3-one in the studied solvents were correlated with the modified Apelblat equation, λh equation, Wilson model and NRTL model. The largest values of root-mean-square deviation (RMSD) and relative average deviation (RAD) were 1.35×10−4 and 0.32%, respectively. The RAD values obtained with the modified Apelblat equation were smaller than those with the other three models for a given solvent. So the four thermodynamic models were all acceptable for the systems of 2,4-dihydro-5-methyl-2-(4-methylphenyl)-3H-pyrazol-3-one in these solvents. Furthermore, the mixing Gibbs energy, mixing enthalpy, mixing entropy, activity coefficient (γ1∞) and reduced excess enthalpy (H1E,∞) at infinitesimal concentration were calculated. The obtained solubility and thermodynamic studies would be very helpful for optimizing the purification process of 2,4-dihydro-5-methyl-2-(4-methylphenyl)-3H-pyrazol-3-one.

Solubility determination and thermodynamic modelling of terephthaldialdehyde in ten organic solvents from T = (273.15 to 318.15) K and mixing properties of solutions

Knowledge of solubility for terephthaldialdehyde in different solvents is essential for its purification and further theoretical investigations. In this work, the solubility of terephthaldialdehyde in ten solvents of ethanol, n-propanol, 1-butanol, isopropanol, benzyl alcohol, 2-butanone, acetonitrile, isopentanol, N,N-dimethylformamide, and acetone were determined by an isothermal saturation method at temperatures ranging from (273.15 to 318.15)K under atmospheric pressure. By and large, the solubility of terephthaldialdehyde decrease according to the following order in different solvents: N,N-dimethylformamide>benzyl alcohol>2-butanone>acetone>acetonitrile>isopentanol>1-butanol>n-propanol>ethanol>isopropanol. In addition, values of the measured solubility were correlated with the Buchowski–Książczak λh equation, modified Apelblat equation, Wilson model and NRTL model. The largest values of relative average deviation and root-mean-square deviation were 1.2% and 8.7×10−4, respectively. The correlated values with the four models agreed well with the experimental values and the Apelblat equation model provided better correlation results than the other three models. Furthermore, the mixing properties, including mixing Gibbs energy, mixing enthalpy, mixing entropy, activity coefficient at infinitesimal concentration (γ1∞) and reduced excess enthalpy (H1E,∞) were computed. The solution process of terephthaldialdehyde was spontaneous and favourable in these solvents. Knowledge of the solubility and thermodynamic investigations would be very important for optimizing the purification process of terephthaldialdehyde.

Solubility determination and thermodynamic modeling of 2,4-dinitroaniline in nine organic solvents from T = (278.15 to 318.15) K and mixing properties of solutions

The solubility of 2,4-dinitroaniline in nine organic solvents of methanol, ethanol, acetone, acetonitrile, n-propanol, toluene, isopropanol, ethyl acetate and 1-butanol was determined experimentally by using the isothermal saturation method over a temperature range from (278.15 to 318.15)K under 101.1kPa. For the temperature range studied, the solubility of 2,4-dinitroaniline in the solvents increased with a rise of temperature. At a specific temperature, the mole fraction solubility is lowest in toluene, and largest in acetone. The solubility order of 2,4-dinitroaniline is acetone>ethyl acetate>acetonitrile>1-butanol>ethanol>methanol>(n-propanol, isopropanol)>toluene. The modified Apelblat equation, λh equation, Wilson model and NRTL model were employed to correlate the solubility of 2,4-dinitroaniline in the solvents studied. The maximum values of root-mean-square deviation (RMSD) and relative average deviation (RAD) were 3.76×10−4 and 1.44%, respectively. The values of RAD obtained with the modified Apelblat equation were smaller than those with the other three models for a certain solvent. Generally, the four thermodynamic models were all acceptable for the systems of 2,4-dinitroaniline in these solvents. Furthermore, the mixing Gibbs energy, mixing enthalpy, mixing entropy, activity coefficient at infinitesimal concentration (γ1∞) and reduced excess enthalpy (H1E,∞) were computed. The solution process of 2,4-dinitroaniline was spontaneous and endothermic in the solvents. The solubility determined and thermodynamic studies would be very helpful for optimizing the purification process of 2,4-dinitroaniline.

Solubility determination and thermodynamic modeling of 5-nitro-8-hydroxyquinoline in ten organic solvents from T = (278.15 to 313.15) K and mixing properties of solutions

The solubility of 5-nitro-8-hydroxyquinoline in ten organic solvents including methanol, ethanol, acetone, acetonitrile, acetic acid, n-propanol, toluene, isopropanol, ethyl acetate and 1-butanol was determined experimentally by using the isothermal saturation method over a temperature range from (278.15 to 313.15)K under 101.3kPa. For the temperature range studied, the solubility of 5-nitro-8-hydroxyquinoline in the solvents increased with a rise of temperature. In general, they obeyed the following order from high to low in different solvents: acetone>ethyl acetate>toluene>acetic acid>acetonitrile>1-butanol>(n-propanol, ethanol)>isopropanol>methanol. The acquired solubility data of 5-nitro-8-hydroxyquinoline in the solvents studied were correlated by using the modified Apelblat equation, λh equation, Wilson model and NRTL model. The maximum values of root-mean-square deviation (RMSD) and relative average deviation (RAD) were 1.10×10−4 and 1.39%, respectively. Generally, the RAD values acquired with the modified Apelblat equation were smaller than those with the other three models for a certain solvent. Generally, the four thermodynamic models were all acceptable for the systems of 5-nitro-8-hydroxyquinoline in these solvents. Furthermore, the mixing Gibbs energy, mixing enthalpy, mixing entropy, activity coefficient at infinitesimal concentration (γ1∞) and reduced excess enthalpy (H1E,∞) were obtained. The solution process of 5-nitro-8-hydroxyquinoline was spontaneous and endothermic in the studied solvents. The obtained solubility and thermodynamic studies would be very helpful for optimizing the purification process of 5-nitro-8-hydroxyquinoline.

Viscosities and densities of systems involved in the deterpenation of essential oils by liquid-liquid extraction: New UNIFAC-VISCO parameters

This work reports viscosities and densities, at T=298.15K, of the phases formed after deterpenation of bergamot, lemon and mint essential oils, by (liquid+liquid) extraction (LLE). Samples of mixtures containing the main components of each essential oil (terpenes and oxygenated compounds), plus ethanol and water, were obtained from studies of phase equilibrium performed previously by our research group. Experimental viscosities were also correlated to the UNIFAC VISCO model, based on the group contribution method. Correlations were accomplished using two approaches: in the first one, functional groups already described by previous studies in the literature and new ones obtained in this work were considered, providing a global average relative deviation (ARD) equal to 1.70%; in a second approach, all functional groups were fitted to our experimental data, which provided a global average relative deviation equal to 0.68%. The predictive capability of the UNIFAC-VISCO were tested for systems involved in the deterpenation of eucalyptus essential oil, giving ARD values of (3.56 and 1.83)%, for parameters from first and second approach, respectively. These results indicate that, for more accurate calculation of viscosities, it is important to consider the particularities and the complexity of each system.

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

AbstractTo 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 basis 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), respectively. 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.

Prediction of gas solubility in polymers by back propagation artificial neural network based on self-adaptive particle swarm optimization algorithm and chaos theory

A novel prediction method based on chaos theory, self-adaptive particle swarm optimization (PSO) algorithm, and back propagation artificial neural network (BP ANN) is proposed to predict gas solubility in polymers, hereafter called CSPSO BP ANN. The premature convergence problem of CSPSO BP ANN is overcome by modifying the conventional PSO algorithm using chaos theory and self-adaptive inertia weight factor. Modified PSO algorithm is used to optimize the BP ANN connection weights. Then, the proposed CSPSO BP ANN (two input nodes consisting of temperature and pressure; one output node consisting of gas solubility in polymers) is used to investigate solubility of CO2 in polystyrene, N2 in polystyrene, and CO2 in polypropylene, respectively. Results indicate that CSPSO BP ANN is an effective prediction method for gas solubility in polymers. Moreover, compared with conventional BP ANN and PSO ANN, CSPSO BP ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R2) and standard deviation (SD) are 0.1275, 0.9963, and 0.0116, respectively. Statistical data demonstrate that CSPSO BP ANN has excellent prediction capability and high accuracy, and the correlation between predicted and experimental data is good.

Solubility prediction of gases in polymers using fuzzy neural network based on particle swarm optimization algorithm and clustering method

AbstractA four‐layer fuzzy neural network (FNN) model combining particle swarm optimization (PSO) algorithm and clustering method is proposed to predict the solubility of gases in polymers, hereafter called the CPSO‐FNN, which combined fuzzy theory's better adaptive ability, neural network's capability of nonlinear and PSO algorithm's global search ability. In this article, the CPSO‐FNN model has been employed to investigate solubility of CO2 in polystyrene, N2 in polystyrene, and CO2 in polypropylene, respectively. Results obtained in this work indicate that the proposed CPSO‐FNN is an effective method for the prediction of gases solubility in polymers. Meanwhile, compared with traditional FNN, this method shows a better performance on predicting gases solubility in polymers. The values of average relative deviation, squared correlation coefficient (R2) and standard deviation are 0.135, 0.9936, and 0.0302, respectively. The statistical data demonstrate that the CPSO‐FNN has an outstanding prediction accuracy and an excellent correlation between prediction values and experimental data. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013

Comparative Study between Neural Network Model and Mathematical Models for Prediction of Glucose Concentration during Enzymatic Hydrolysis

atic hydrolysis of cellulose is a complex process because of a number of inhibition and enzyme inactivation reactions which happen during hydrolysis. Artificial Neural Networks (ANNs) are very effective in developing predictive models for processes involving complex reaction kinetics that would otherwise be difficult to be modeled by more traditional deterministic approaches. The present investigation was carried out to study the application of Artificial Neural Network as a tool for predicting glucose production by enzymatic hydrolysis of pure cellulose and comparison with mathematical models and experimental results. A feed forward neural network with one hidden layer was trained and used to predict the glucose production. Comparing the R 2 (coefficient of determination), MSE (mean square error) and ARD (average relative deviation) values of the neural network model with the mathematical model, it was concluded that the neural network is more accurate than the mathematical models. The obtained results show that the ANN can be a useful method for the design of the enzymatic hydrolysis.

A new impedimetric biosensor utilizing VEGF receptor-1 (Flt-1): Early diagnosis of vascular endothelial growth factor in breast cancer

A new impedimetric biosensor, based on the use of vascular endothelial growth factor receptor-1 (VEGF-R1), was developed for the determination of vascular endothelial growth factor (VEGF). VEGF-R1 was immobilized through covalent coupling with 3-mercaptopropionic acid which formed a self-assembled monolayer on gold electrodes. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy techniques were employed to characterize the immobilization process and to detect VEGF. To successfully construct the biosensor current, experimental parameters were optimized. Kramers–Kronig Transform was performed on the experimental impedance data. The obtained results provided a linear response range from 10 to 70 pg/mL human VEGF. The applicability of the developed biosensor in the determination of VEGF in a spiked artificial human serum sample was experienced, yielding average recovery of 101%, in that order, with an average relative deviation value less than 5%.

The roller compaction of different types of lactose

The influence of the roller compaction process on the granule quality of four different types of lactose showing different particle size, bulk density, crystallinity and morphology was compared by modeling. For each type of lactose the influence of the parameter settings on the friability was modeled by a second order polynomial having different average relative deviation values. Pressure, roll speed, vertical and horizontal screw speed were important parameters for all types of lactose but pressure was the most important parameter followed by the roll speed and the horizontal screw speed. Type, particle size and density had an influence on the usable roller compactor parameter settings. The best quality was obtained if at a high pressure a low horizontal screw speed was chosen for each roll speed. Roller compaction of spray-dried lactose was difficult. A gradual decrease of granule quality was seen for α-lactose monohydrate 200M, anhydrous β-lactose, α-lactose monohydrate 90M and spray-dried α-lactose, respectively. The roller compaction process using the different types of lactose was reproducible.