Journal of Chemical and Petroleum Engineering
Volume:54 Issue: 1, Jun 2020

In the present study, in order to predict the activity coefficient of inorganic ions, 12 cases of aqueous chloride solution were considered (AClx=1,2; A=Li, Na, K, Rb, Mg, Ca, Ba, Mn, Fe, Co, Ni). For this study, the UNIQUAC thermodynamic model is desired and its adjustable parameters are optimized with the Genetic + PSO algorithm. The optimization of the UNIQUAC model with PSO+ genetic algorithms has good results. So that the minimum and maximum electrolyte error of the whole system are 0.00044 and 0.0091, respectively. For this study, a temperature of 298.15 and a pressure of 1 is considered. Also, in this study for the electrolyte system, the Artificial bee colony (ABC) algorithm, and Imperialist competitive algorithm (ICA) has been studied. The results showed that the Artificial bee colony algorithm has a lower accuracy than the Genetic+ Particle swarm optimization (PSO) algorithm. The minimum concentration was 0.1 Molality and the maximum concentration was 3 Molality. Based on the results, the activity coefficient of LiCl, NaCl, KCl, RbCl + H2O, MgCl2, CaCl2, BaCl2, MnCl2, FeCl2, CoCl2 NiCl2 depends on the ionic strength of the electrolyte system.

Upgrading of cracked PFO (Pyrolysis fuel oil) for production of fuels, such as gasoline and light gasoil, was carried out in a semi batch reactor. Two different kinds of mesoporous and microporous catalysts, MCM-41 and ZSM-5, were used. Modification methods, such as ion exchange and impregnation with Fe and Ti, were done for tuning the acidity of the catalyst. XRD, FT-IR, and XRF analyzes were used to identify the structure and composition of the catalysts. Among the catalysts used in low temperature catalytic cracking of cracked PFO in a moderate temperature (380 °C), 3%Ti/H-MCM-41 showed the best catalytic performance. After choosing the best catalyst, an experimental design was carried out using response surface method with a five-level central composite design model. The effect of 3 main parameters, i.e. reaction temperature (360-400 °C), catalyst to feed ratio (0.04-0.1), and loading of Ti (0-5%) were investigated on liquid productivity and light olefin production. Design Expert software was used to maximize the sum of liquid yield and olefins in the gas. The best catalyst is 2.5%Ti/H-MCM-41. In optimum, 380 °C with the ratio of 0.1 g/g catalyst to feed over 2.5%Ti/H-MCM-41, the wt.% of liquid, gas, and solid products are 80 wt. %, 10 wt. %, and 10 wt. %, respectively. At this condition, 26 wt. % of liquid product was in the range of gasoline (C5-C10) and the rest (i.e. C11+) was considered in the range of light gas oil. Light olefins of the obtained gas products were about 2.74 wt. %.

This study investigated the reduction of the chemical oxygen demand from the Kermanshah oil refinery wastewater using Fenton and Photo-Fenton processes. The study investigated the effects of operating variables such as ultraviolet light intensity in values of 0, 15, and 30 W, ferrous ion concentration in values of 10, 50, and 90 mg/l, hydrogen peroxide concentration in values of 100, 500 and, 900 mg/l, and treatment time in values of 30, 90, and 150 min on the reduction of the chemical oxygen demand percentage from this industrial wastewater. All the experiments were carried out in a glass reactor at a temperature of 25 ℃ under a stirrer speed of 350 rpm. Using the Box-Behnken experimental method, 27 experiments were performed randomly. Then, using the least-squares error method, an experimental quadratic model was achieved. Analysis of variance of the model showed that the proposed model had a high level of accuracy. Finally, we obtained the optimum conditions for maximizing the percentage of the chemical oxygen demand removal from the wastewater. The optimal conditions were Fe2+ concentration of 89.2 mg/l, H2O2 concentration of 119.9 mg/l, and treatment time of 135.9 min for the Fenton process. Also, these conditions were Fe2+ concentration of 35.1 mg/l, H2O2 concentration of 110.2 mg/l, treatment time of 92.8 min, and ultraviolet light intensity of 30 W, for the photo-Fenton process. Under these conditions, the percentage of chemical oxygen demand removal using the proposed model was 96.61 % and 92.82 % for the Fenton and photo-Fenton processes, respectively.

A dynamic numerical model was developed to predict the biomass concentration, pH, and carbon dioxide fixation rate in the continuous culture of cyanobacteria in a photobioreactor. The model is based on the growth rate equation of microalgae combined with mass transfer equations for gas and liquid phases in the photobioreactor as well as thermodynamic equilibrium of inorganic carbon ions in the culture media. The model was validated by comparing its predictions with experimental results obtained from turbidostat cultivation of Synechocystis in a flat-plate photobioreactor. Optical density, pH, and CO2 concentration in outlet gas were measured continuously in this photobioreactor. The model was used to simulate this system at the same conditions that the experiments were performed at two light intensities of 75 mE/m2/s and 150 mE/m2/s. Although the growth rate and outlet gas CO2 concentration were quite different at these two light intensities, the model predicted the system behavior accurately. The average error in the prediction of biomass concentration, pH, and outlet gas CO2 concentration was 0.40%, 0.61%, and 0.34%, respectively.

In this study, the solubility of CO2 in DEA in the presence and absence of choline chloride was reported at different temperatures of 276.15, 298.15, 313.15, and 333.15 K, and pressure range of 4-15 bar. The solubility of CO2 was evaluated using the pressure decay method in a batch isochoric stirred absorption cell. Also, the design of experiments performed with Qualitek-4 software using the Taguchi method. Henry’s law constants at three different temperatures were calculated from the correlation of solubility data. Results showed that increasing the initial pressure and DEA concentration and also decreasing the temperature, increases the solubility of CO2. Optimum operating conditions to maximize the amount of CO2 absorption including the temperature of 276.15 K (minimum level), the initial pressure of 15 bar (maximum level), the concentration of DEA of 40 wt.% (maximum level), and the concentration of choline chloride of 5 wt.% (middle level). Also, Qualitek-4 software predicted the amount of solubility at the optimum conditions which was 7.5% different from the measured value.

Given that most of the gaseous constituents of industrial chimneys are usually carbon dioxide which is one of the most important greenhouse gases. It seems that the hydration process is one of the newest methods for the separation of this gas from gaseous mixtures. In the gas hydrate formation industry, in addition to disadvantages, there are some advantages such as gas separation, transmission, and storage. Therefore, it is important to determine the appropriate promoter for the formation of gaseous hydrates as well as to find the inhibitor. In this study, the effect of tetra-n-butyl ammonium chloride (TBAC) (which is a thermodynamics promoter) and alkyl poly glucoside (APG) as a nonionic surfactant on the surface tension of carbon dioxide hydrate formation process have been studied. The experiments were carried out in a 218 cm3 batch reactor. The surface tension of CO2 hydrate has been determined at different concentrations and different temperatures and pressures. The nucleation classical theory has been used for this purpose. Designing the experiments performed by Design-Expert software. The results show that increasing the APG and temperature leads to decreasing the surface tension and in contrast, induction time decreases, and the experimental model of the effect of these parameters on surface tension presented as R2 = 0.9898.

Permeability is arguably the most important property in evaluating fluid flow in the reservoir. It is also one of the most difficult parameters to measure in field. One of the main techniques for determining permeability is the application of Nuclear Magnetic Resonance (NMR) logging across the borehole. However, available correlations in literature for estimating permeability from NMR data do not usually give acceptable accuracy in carbonate rocks. In this research, two new empirical models are introduced for quantifying NMR extracted permeability in carbonate formations. These models are validated for three carbonate formations, namely, Yamama, Gadvan, and Daryan in one of Iranian offshore reservoirs in the Persian Gulf. The first empirical model applies the pore-related NMR data such as free and bound fluid parameters. The second model, however, is a novel approach that uses the geometric features of the occurring humps in T2 distribution. For assessing the performance of the proposed models, statistical parameters as well as graphical tools are utilized. It is found that the for the examined case studies, geometric approach gives more accurate and reliable estimates compared to the available models in the literature including Timur-Coates and SDR methods.

In this article, the improved space-time conservation element and solution element (CESE) method are used to simulate the dynamic treatment of the hydrocracking reactor. The dynamic model consists of four lumps: vacuum gas oil (VGO), middle distillate, naphtha, and gas which is dissolved by this method. The offered method can solve the partial differential equations caused by the reactions inside the hydrocracking reactor. In this study, both temperature and mole fraction variables are solved explicitly and simultaneously. In the CESE method, to obtain a suitable answer to the dynamic model, a CFL insensitive scheme was used which, for the CESE method to be stable, the CFL number should be less than 1. In this work, obtained results from the CESE method, in good agreement with the data industry. Outcomes illustrate that AAD% of the yield forecast for the middle distillate, naphtha, and gas are 3.33%, 2.56 %, and 6.47%, respectively. This method unlike other contractual numerical methods treats with space and time coordinates Similar.

This study aims to develop an Adaptive Network-based Fuzzy Inference System technique (ANFIS) and using the parameters of a complex mathematical model in the RO membrane performances. The ANFIS was constructed by using a subtractive clustering method to generate initial fuzzy inference systems. The model trained by 70% of the data set and then its validity is examined by remained 30% data set. The result indicated that this method could predict the performance of the RO membrane faster and more accurately than previous numerical techniques. The squared correlation coefficient between real data and predicted data of this technique was 0.9973 for separation factor, 0.9916 for NP and 0.9975 NT, which are better in comparison with numerical methods, and previous Artificial Neural network used by the author to model these membranes. It was observed that the squash factor, reject ratio, and accept ratio has no significant effect on ANFIS performance. Results showed that for all cases better performances achieved when this parameter has a value of more than 0.5, as 0.86 for separation factor, 0.91 for net pre flux, and 0.83 for total flux. This technique just takes a few seconds to model RO membrane performance which is very faster than other numerical methods. So, this technique could be a powerful method to predict RO membranes.

In this research, the thermodynamics and mass transfer of CO2 absorption has been studied in a mixture of MDEA-MEA amines. A relation is presented for mass transfer flux in the reactive-absorption process. For this purpose, the effective parameters on the mass transfer flux were investigated in both liquid and gas phases. Then, using dimensional analysis with the Pi-Buckingham theorem, the effective variables were extracted as the dimensionless parameters. Also, the absorption process with MEA-MDEA is modeled according to four laws of chemical equilibrium, phase equilibrium, mass, and charge balance (considering the appropriate thermodynamic model for solvent). The experimental data of the previous research was used to calculate the dimensionless parameters. The constants of the mass flux equation are calculated with the fitting method. Also, the effects of operating parameters such as CO2 partial pressure, temperature, and dimensionless parameters such as the film parameter, enhancement factor, and loading have been investigated. The results showed that by increasing the loading and film parameter, the mass flux decreased, and the mean absolute error obtained from the proposed relationship was about 4.3%, which indicates the high accuracy of the predicted equation.

A review of field production data reveals that usually, it is very difficult to apply available decline models because of poor quality and often noisy character of initial data and also unrealistic assumptions of the models. This paper tries to introduce applicable procedures to correct initial data and reproduce missed data. Corrected data are analyzed and finally, the results for permeability and skin factor estimations are compared to results of transient well test analysis. Estimations for Initial Hydrocarbon In Place (IHIP) and reserve are also compared to the results of Material Balance Equation (MBE) and static model through field case studies. It is proved that the analysis can present acceptable estimations for radial permeability and skin values in naturally fractured reservoirs (NFR). Flowing material balance and Buba and Blasingame plotting function model give the best estimations for Original Gas In Place (OGIP) and gas reserve respectively. A simple but useful and applicable method is presented to determine reservoir fracture distribution mapping. It is also showed that the analysis can be used to distinguish water influx successfully.

Extraction of essential oils from medicinal plants has received researcher’s attention as it has a wide variety of applications in different industries. In this study, ultrasonic method has been used to facilitate the extraction of active ingredient anethole from fennel seeds. Effect of different parameters like extraction time (20, 40, and 60 min), power (80, 240, and 400 Watts) and solid particle size (0.3, 1, and 1.7 mm) on the anethole extraction yield have been studied. The box-Behnken design method has been used for the design of experiments to reduce the number of experiments. A second-degree polynomial was proposed to predict the relationship between independent variables and the dependent variable. An artificial neural network was trained with experimental data to provide another model for the system. Optimal results achieved when using the Levenberg-Marquardt back-propagation algorithm, Logsig, and Tansig transfer functions for hidden and output layers and the number of 10 neurons in the hidden layer. Coefficient of determination, sum of squared errors, root of mean square error, and absolute average deviation were found to be 0.9933, 0.0199, 0.0059, and 2.1944 for the ANN model and 0.9851, 0.0425, 0.0059 and 2.1944 for the design of experiment (DOE) model, respectively.