A Comparison Between GA and PSO Algorithms in Training ANN to Predict the Refractive Index of Binary Liquid Solutions

A total of 1099 data points consisting of alcohol-alcohol, alcohol-alkane, alkane-alkane, alcohol-amine and acid-acid binary solutions were collected from scientific literature to develop an appropriate artificial neural network (ANN) model. Temperature, molecular weight of the pure components, mole fraction of one component and the structural groups of the components were used as input parameters of the network while the refractive index was selected as its output. The ANN was optimized once by genetic algorithm (GA) and once again by particle swarm optimization algorithm (PSO) in order to predict the refractive index of binary solutions. The optimal topology of the ANN-GA consisted of 13 neurons in the hidden layer and the optimal topology of the ANN-PSO consisted of 16 neurons in the hidden layer. The results revealed that the ANN optimized by PSO had a better accuracy (MSE=0.003441 for test data) compared to the ANN optimized with GA (MSE=0.005117 for test data)

Using Intelligent Methods and Optimization of the Existing Empirical Correlations for Iranian Dead Oil Viscosity

Numerous empirical correlations exist for the estimation of crude oil viscosities. Most of these correlations are not based on the experimental and field data from Iranian geological zone. In this study several well-known empirical correlations including Beal, Beggs, Glasso, Labedi, Schmidt, Alikhan and Naseri were optimized and refitted with the Iranian oil field data. The results showed that the Beal and the Labedi methods were not suitable for estimation of the viscosity of the Iranian crudes, while the Beggs, Glasso and Schmidt methods gave reasonable results. The Naseri’s correlation and their present method proved to be the best classical methods investigated in this study. Two new intelligent methods to predict the viscosity of Iranian crudes have also been introduced. The study also showed that the neural network and SVM give much better results comparing to classical correlations. It is claimed that this study may provide more exact results for the prediction of Iranian oil viscosity

The Effect of the Drying Method on the Quality of Dried Kiwi Slices

Abstract— Drying is known as a food preservation method which increases the food’s storage time by water reduction. Traditional drying consisted of open sun-drying, but different industrial dryers have been widely used in recent times. The new dryers consist of convective, infrared, ultrasound, freeze fluidized bed and freeze dryers. All of these dryers reduce the water content but under different mechanisms which leads to the end products with different qualities. In this study we aim to compare the difference in quality of kiwi fruit slices dried by three different dryers: 1. Convective tray dryer, 2. Microwave dryer and 3. Freeze dryer. The tray dryer experiments were conducted in two air temperatures of 60 and 80oC in the constant air velocity of 0.8 m/s. The microwave dryer operated in 3 output powers of 180, 270 and 360 W. The condenser temperature and pressure in the freeze dryer reduced to -50oC and 0.1 mbar, respectively. The operating conditions and time were regulated so that the moisture content of all dried samples reduced to nearly 10% in the wet basis. The three parameters of color change, ascorbic acid and antioxidant reduction were selected as the measuring criteria for the comparison of the product qualities. The experiments show that the freeze drying caused the minimum color change while the microwave drying in the maximum power of 360W caused the maximum amount of color change. The concentration of ascorbic acid was measured in the fresh fruits and dried samples by standard methods. The measurements proved that the ascorbic acid content of the freeze dried samples was 80% of the fresh fruits. The ascorbic acid content of other samples was much lower. The antioxidant activity of the dried samples and the fresh fruits was also measured by standard methods and the experimental data also showed that the freeze drying causes the minimum reduction in the antioxidant activity

A Predictive Correlation for Vapor-Liquid Equilibrium of CO2 + n-Alkane Ternary Systems Based on Cubic Mixing Rules

The accurate description of the phase equilibria of CO2 and n-alkane multicomponent mixtures over a wide range of temperature, pressure, and n-alkane molecular weight, requires the models that are both consistent and mathematically flexible for such highly non-ideal systems. In this study, a predictive correlation was proposed for the vapor-liquid equilibrium data (VLE) of CO2 and n-alkane ternary systems, based on the Peng-Robinson equation of state (PR EOS), coupled to cubic mixing rules (CMRs). The ternary interaction parameters (TIP) correlation is developed using binary VLE data and tested for CO2 + n-alkane+ n-alkane ternary systems. For this purpose, binary VLE data of CO2 + n-alkane and n-alkane + n-alkane systems for n-alkane from C3 to C24, covering a total of about 70 references, used to correlate TIP in the ranges of 0.5-31 MPa and 230-663 K. Two temperature-dependent TIP correlations, based on acentric factor ratio, have been tuned with more than 2000 data points of the CO2 + n-alkane and the n-alkane + n-alkane binary systems with AARD of 3.13% and 6.71%, respectively. The generalized predictive correlation was proposed based on the proper three-body interaction contributions and successfully tested for VLE data of the CO2 + n-alkane + n-alkane ternary systems

Bubble Pressure Prediction of Reservoir Fluids using Artificial Neural Network and Support Vector Machine

Bubble point pressure is an important parameter in equilibrium calculations of reservoir fluids and having other applications in reservoir engineering. In this work, an artificial neural network (ANN) and a least square support vector machine (LS-SVM) have been used to predict the bubble point pressure of reservoir fluids. Also, the accuracy of the models have been compared to two-equation state-based models, i.e. SRK-EOS and PR-EOS and four empirical equations, i.e. Whitson, Standing, Wilson and Ghafoori et al. Compared to the experimental data, the average relative deviations (ARD) of bubble pressure prediction for these equations were obtained to be 14%, 29%, 66%, 30%, 38%, and 11%, respectively. The best semi-empirical equation has an ARD of about 11% while, the ANN and LS-SVM models have an ARD of 8% and 4.68%, respectively. Thus, it can be concluded that generally, these soft computing models appear to be more accurate than the empirical and EOS based methods for prediction of bubble point pressure of reservoir fluids

Experimental Study and neural network modeling for prediction of refractive index of pure and binary alcohol mixtures

In this study, the refractive index of pure alcohols and binary alcohol mixtures were investigated experimentally and theoretically. In the experimental approach, a refractometer was used to measure the refractive index of the samples. Aa artificial neural network in form of a multilayer perceptron was also used to model and predict the measured refractive data. The input parameters of the network included temperature, molecular weight, the group contributions of CH3, CH2 and OH for the pure materials and for the binary mixtures the additional parameters of mole fractions, molecular weight and group contributions of both components have to be considered. The refractive index of the pure or binary alcohol mixture consist the only output parameter of the network. 70% of the experimental data were considered for train, 15% for test and 15% for the validation of the neural network. The optimum neural architecture for the pure compounds consisted of 10 neurons in the hidden layer with 0.08457 mean absolute relative error and the optimum network for binary mixtures consisted of 12 neurons in the hidden layer with 0.07121 absolute relative error. Comparison of the results showed a good agreement between the experimental data and the neural network outputs and the high accuracy of the model

Estimation of the normal boiling point of organic compounds via group contributions

The normal boiling point is a fundamental thermo-physical property, which is important in describing the transition between the vapor and liquid phases. Reliable method which can predict it is of great importance, especially for compounds where there are no experimental data available. In this work, an improved group contribution method, which is second order method, for determination of the normal boiling point of organic compounds based on the Joback functional first order groups with some changes and added some other functional groups was developed by using experimental data for 632 organic components. It could distinguish most of structural isomerism and stereoisomerism, which including the structural, cis- and trans- isomers of organic compounds. First and second order contributions for hydrocarbons and hydrocarbon derivatives containing carbon, hydrogen, oxygen, nitrogen, sulfur, fluorine, chlorine and bromine atoms, are given. The fminsearch mathematical approach from MATLAB software is used in this study to select an optimal collection of functional groups (65 functional groups) and subsequently to develop the model. This is a direct search method that uses the simplex search method of Lagarias et al. The results of the new method are compared to the several currently used methods and are shown to be far more accurate and reliable. The average absolute deviation of normal boiling point predictions for 632 organic compounds is 4.4350 K; and the average absolute relative deviation is 1.1047 %, which is of adequate accuracy for many practical applications

Comparison of the dried properties of Ganoderma lucidum produced by the convective dryer and infrared dryer

Abstract Ganoderma lucidum is a promising medicine with a high amount of antioxidants and calcium. The selection of appropriate drying process methods in food science has a chief role to reach the best final characteristics. This study aimed to investigate the effects of air velocity and temperature in the convective dryer, sample distance, and infrared power in infrared dryers on the drying kinetics and quality of Ganoderma lucidum slices. In addition, Response Surface Methodology based on central composition design was used to optimize and analyze drying conditions. The ranges of temperature and air velocity were 40–60 °C and 0.5–1.5 m/s, respectively in the convective drying process while the range of distance and infrared power was 4–16 cm and 500–1500 W, respectively in the infrared drying process. It is worth mentioning that antioxidant and calcium contents were greatly enhanced during the drying procedures. Moreover, the values of the total color difference ranged between 8.21 and 19.66 for the convective dryer and 8.14 and 28.85 for the infrared dryer. A kinetic study indicated that dried samples by the infrared dryer could rapidly reach equilibrium moisture content due to exposure to IR radiation. Consequently, the results indicated that the infrared dryer has better performance than the convective dryer regarding drying time, energy consumption, and amount of calcium and antioxidant

Optimization and quality evaluation of infrared-dried kiwifruit slices

Infrared drying characteristics of kiwifruits under natural and forced drying air convection with different conditions were investigated. An experimental study along with statistical analysis aimed to evaluate quality characteristics of infrared-dried kiwifruit slices, in terms of drying time, rehydration ratio and shrinkage as a function of infrared power levels, slice thicknesses, slice distance from the infrared lamps, and air velocity. Response surface methodology was used for optimization of drying parameters with employing desirability function. Minimum drying time, shrinkage, and maximum rehydration ratio assumed as criteria for optimizing drying conditions of kiwifruit slices were strongly dependent on the drying conditions. All operating variables had a significant effect on total responses, but slice thickness almost was the most prominent factor. The slices dried at the highest power level, the lowest distance from the Infrared lamp, the least thickness, and air velocity showed a higher rehydration capacity than slices dried at the other conditions