Iranian Journal of Chemical Engineering (IJChE)

Abstract The purpose of this research is CFD modeling of the fluid flow inside an industrial valve in order to discover the areas with high shear stress and to determine the effect of hydrodynamic on the erosion rate. CFD results are compared with the existing experimental data in a valid reference and the model is verified with high accuracy. The impact of the pressure at inlet and the disc angle on the erosion is investigated. By increasing inlet pressure, maximum velocity, turbulence intensity, wall shear stress and particle erosion increased. However, the wall shear stress, turbulence intensity, and particle erosion are clearly reduced as the disc angle decreases. When the disc angle is less than 50o, the range of dependent parameters changes has a small value. Reducing the disc angle or increasing the inlet pressure led to an increase in cavitation. Therefore, to prevent the erosion of the butterfly valve, it is necessary to increase the disc angle or reduce the pressure at inlet. Erosion of the butterfly valve significantly occurred at the front and rear of the disc. Depending on the disc angle, the shear stress of wall for the modified configuration is 10 to 80 times lower than the original butterfly valve. Therefore, it can be stated that the modified geometry can reduce the wall shear stress and consequently the erosive for all the disc angles of the studied butterfly valve.

Abstract In this paper, CFD modeling of ferrofluid convection heat transfer in a micromixer with static magnetic field (SMF) and rotating magnetic field (RMF) is investigated. Applying a magnetic field and the existence of magnetic nanoparticles lead to the creation of transverse vortices in the micromixers by movement of nanoparticles, that improves heat transfer. There is a cylindrical pit in the microcmixer with heat source that is applied to its bottom wall. Top wall of the pit is adjacent to a fixed permanent magnet, which creates the SMF. CFD modeling first is done for heat transfer process in the micromixer in the absence of the magnetic field. Secondly, simultaneous effect of the SMF and magnetic nanoparticles on the flow pattern and heat transfer rate of ferrofluid is evaluated. Results showed that ferrofluid leads to the improvement of the heat transfer rate compared to pure water. The secondary flows induced by nanoparticles’ motion toward SMF decreases the velocity in the area of application of the magnetic field, so the heat transfer coefficient decreases. But, in the case of RMF, applying the magnetic field causes the nanoparticles to rotate inside the pit, which leads to an increase in the heat transfer coefficient. CFD results of heat transfer coefficient are compared with experimental results in a reliable reference and acceptable agreement between them is observed.

Abstract In this work, the separation of carbon monoxide (CO) from a synthesis gas (syngas) mixture was modeled. It was considered a copper-based adsorbent consisting of cuprous chloride (CuCl) on an activated carbon (AC) support (CuCl/AC) in a pressure swing adsorption (PSA) process. First, the adsorption of syngas components on the CuCl/AC adsorbent at 303.15 K was simulated to determine the required data. Next, the PSA process to separate CO from syngas using CuCl/AC absorbent at ambient temperature and pressure of 1000 kPa was evaluated by computational fluid dynamics simulation. The simulation results showed that with an adsorption bed of 2 m in height and 1 m in diameter, CO with appropriate purity (~ 99.5%) is separated from syngas by CuCl/AC. In addition, reducing the inlet feed pressure, or in other words, its velocity or flow can increase the efficiency of the operation (e.g, with a shorter bed height of 0.5 m, a CO purity of more than 99.8% can be achieved at 700 kPa, but with a significant increase in operating cost).

Abstract Ejectors offer a cost-effective and practical solution for recovering flare gases, thereby reducing greenhouse gases. Improving the entrainment rate of the secondary fluid can enhance ejector performance. The objective of this research is to identify the optimal ejector geometry to maximize the absorption rate of the secondary fluid. Computational fluid dynamics is used to evaluate a two-phase ejector. Geometric parameters such as throat diameter and length, nozzle diameter, and converging and diverging angles impact the absorption rate of the secondary fluid. Using a multi-objective genetic algorithm, the optimal values for each parameter are obtained. The results show that reducing the throat length and angle of the converging section, as well as nozzle diameter, leads to increased absorption. In contrast, the throat and angle of the divergent section increase absorption. Additionally, energy efficiency is investigated under basic and optimized geometries. The findings reveal that increasing the soak range does not necessarily enhance energy efficiency.

Abstract The optimization of the ammonia synthesis plant to increase the production of ammonia is studied in this line of research. In this paper, the steady-state ammonia synthesis is simulated using the Aspen HysysV.11 software. By comparing the simulation results with the industrial information, a mean relative error of 7.71 % was obtained, which indicated the high accuracy of the simulation. Then, four effective variables were selected from among 11 independent variables by the Plackett-Burman method. The effects of the Hydrogen flow in the feed stream, Recycle stream pressure, Feed stream temperature, and input temperature of the third reactor were investigated, and the response surface design method of the central composite design was performed to plant optimize. It is obtained that the Hydrogen flow in the feed stream is equal to 6255 , the feed stream pressure is equal to 205 bar, the temperature of the excess stream inlet in the first reactor is equal to 663 K, and the temperature of the stream inlet of the second reactor is 677.5 K which increased the ammonia production by 7.5 %.

Abstract An adaptive neuro-fuzzy inference system (ANFIS) was applied to simulate the batch adsorption of triglyceride (TG) from the human blood serum using the cinnamon powder, which has appeared as a potential biosorbent for serum purification, in our previous work. The obtained experimental results were used to train and evaluate the ANFIS model. Temperature (°C), the adsorption time (h), the stirring rate (rpm), the dose of adsorbent (g) and the adsorbent milling time (min) (or the particle sizes of the powder) were considered as the model inputs and TG removal (%) was chosen as the model response. The ANFIS model was trained with 75 % of the available data while 25 % of the remaining data was used to verify the validity of the obtained model. Sobol sensitivity analysis results indicated that the cinnamon dose with 71 % and the adsorbent milling time (or the particle size of the powder) with 15 % impact share were the most influential variables on the TG removal. Furthermore, the specific surface area and the number of reactive adsorption sites were found to be the most important characteristics of the adsorbent. Generally, the results of this study confirmed the advantages of applying the ANFIS and Sobol approaches for the data-based modeling of the bioprocesses.

Abstract MXene membranes perform well in biofuel separation due to their excellent hydrophilicity, flexibility, and mechanical strength. For the first time, computational fluid dynamics was used to model the dehydration of ethanol through the pervaporation system by the MXene membrane. We discretized the momentum and continuity equations using finite element methods and predicted the mass transport. Experimental results and model data were in good agreement (less than 10 %). The feed velocity, feed concentration, and membrane thickness all had positive effects on the separation factors while the temperature had a decreasing effect. This model's efficiency has decreased by 35 % after increasing the feed flow rate by 10 times. In addition, the separation factor increases tenfold when temperature is raised from 25 to 70 °C.

Abstract The removal of Acrylonitrile (AN) from waste gas streams using biological methods, due to their better performance, has recently gained more attraction. The purpose of this research is modeling the AN removal by a bio-filter. The validation of the model is done by using the experimental data of a bench-scale bio-filter bed column including yard waste compost and shredded hard plastics and thickened municipal activated sludge. In this work the kinetics of the biodegradation of Acrylonitrile is first investigated. Then equations of the biofilm and air are obtained at a steady state and constant temperature. The unknown parameters of the model are determined by the least square optimization method along with solving the model equations using MATLAB. For inlet concentrations less than 1 g/m³ the model results show reasonable similarities to the experimental data. The effect of various parameters on the bio-filter performance is evaluated. The Peclet number, biofilm thickness and biomass concentration are the most important parameters. The proposed model can be useful for design purposes.

Abstract The fully mixed continuous stirred tank reactor is an important type of industrial reactors mainly used to produce high volume products such as petrochemicals, detergents, sanitary products and products that are in demand in the market. Knowing the dynamic behavior of chemical reactors is of great importance in setting up, designing, controlling and stopping reactors. In this paper, the effect of non-dimensional numbers Damkohler and Stanton on the stability of a continuous stirred tank reactor in which a first-order exothermic reaction takes place is investigated. First, a mathematical model of the system's dynamic behavior was presented. Then, by simultaneous solving of the equations of mass and energy around the fixed point in MATLAB software, the effect of the mentioned numbers was investigated. The results show that the continuous stirred tank reactor shows different behaviors in different ranges of Damkohler and Stanton numbers. This reactor behaves unstable in small and large ranges of Damkohler and Stanton numbers due to the presence of mixed or positive and negative eigenvalues. The best range for Damkohler and Stanton numbers is close to 1, because in this range the reactor shows stable behavior due to having two negative eigenvalues. In this range, in addition to the stability, the conversion is also 100%. Finaly the ratio of Stanton to DamKohler was investigated as St / Da˃1 and St / Da = 1. If St / Da = 1, the system is in steady state, but in St / Da˃1, the system moves away from steady state.

Abstract Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehensive search method is used in neural network modeling, and a particle swram optimization algorithm for SVM modeling. To compare the accuracy of the models, various criteria such as ARD, MAE, MSE, RAE and R2 are used. The simulation results show that the ARD for the prediction of the true critical temperature and pressure of the binary hydrocarbon mixtures for the final optimized ANN-based model is equal to 0.0161 and 0.0387, respectively. The corressponding ARD value for the SVM-based model is equal to 0.0086 and 0.0091 for critical temperature and pressure, respectively. Simulation results show that although both models have a very high predictive accuracy, the SVM has higher learning speed and accuracy than ANN.

Abstract In this study, an Eulerian-Eulerian multi-fluid model (MFM) was used to simulate the segregation pattern of a conical fluidized bed containing ternary mixtures of equidensity TiO2‌ particles. Experimental 'freeze–sieving' method was employed to determine the axial mass fraction profiles of the different-sized particles, and validate the simulation results. The profiles of mass fraction for large, medium and small sized particles along the bed height during the simulation time indicated that the particles’ segregation can be predicted by CFD model. Effect of superficial gas velocity on segregation pattern was also investigated. It was shown that for U0=1.2Umf, partial segregation of large particles occurred, while for U0=1.6Umf, small and medium size particles also segregated and full segregation was achieved. By increasing U0 to 2Umf, mixing of different sized particles was increased and particles segregation was reduced. Therefore, it can be concluded that there was a critical velocity below which particles would segregate while above which their mixing increased.

Abstract In the present study, Adaptive Neuro–Fuzzy Inference System (ANFIS) approach was applied for predicting the heat transfer and air flow pressure drop on flat and discontinuous fins. The heat transfer and friction characteristics were experimentally investigated in four flat and discontinuous fins with different geometric parameters including; fin length (r), fin interruption (s), fin pitch (p), and fin thickness (t). Two ANFIS models were developed using the Computational Fluid Dynamic (CFD) results which validated by the experimental data. The ANFIS models were applied for prediction of Nusselt number (Nu) and friction factor (f) as functions of Reynolds number (Re), and fin geometric parameters including, spanwise spacing ratio (p/t), and streamwise spacing ratio (s/r). The low error values for testing data set, which were not employed in the training of the ANFIS, proved the precise and validity of the model. The root mean square error (RMSE) of 0.7343 and mean relative error (MRE) of 1.33% were resulted for prediction Nu. In addition, these values for estimation of the f were resulted 0.0158, 3.32%, respectively.

Abstract Computational fluid dynamics (CFD) is a powerful numerical tool that is becoming widely used to simulate many processes in the industry. In this work study of the stirred tank with 7 types of concave blade with CFD was presented. In the modeling of the impeller rotation, sliding mesh (SM) technique was used and RNG-k-ε model was selected for turbulence. Power consumption in various speeds in the single phase, mean tangential, radial and axial velocities in various points, effects of disc diameter and thickness and mixing time were investigated. The optimum concave impeller was selected and the effect of tracer feed position and probe location was investigated on it. Results suggested that power consumption is exactly depending on impellers scale and geometry, was in a good agreement with the experimental data and in turbulent flow is relatively independent of Reynolds number. Power number increases with increasing disc diameter for both concave and Rushton and concave´s power is relatively independent on disc thickness but increasing it decreases Rushton´s power. The data revealed that the power number was 2.3±0.3 for blade angle 40° whereas for blade 25°, 50° and 55° respectively 43% lower and 57% and 43% higher.

Abstract In this work, zeolite 13X with porosity structure has been used as an adsorbent for adsorption of CO2 flue gas. The effect of operating conditions including pressure and time on adsorption capacity were investigated. The experiments conditions are constant temperature, the range of pressure 1 - 9 bar and the registration of adsorption capacity with passing of time. Experimental data were adjusted with adsorption isotherm models including two and three parameters isotherm. Also the process was studied in terms of kinetic models and after the implementation of the experimental data with kinetic models, the speed of this process equations were obtained. The best kinetic model for this process was selected first order equation. The results showed that adsorption capacity of 13X is 71.5 mg/g at pressure of 8 bars. Also the result indicate that 13x has high capacity at low pressures. With regard to achieved results for adsorption isotherm modeling, the adsorption isotherm followed of the three-parameter and among three-parameter models, Toth isotherm can be interpreted the process. Also the results of the fixed bed indicate a very high adsorbent selectivity to carbon dioxide adsorption and there was little oxygen and nitrogen adsorption.

Abstract Solubility data of solid in aqueous and different organic solvents are very important physicochemical properties considered in the design of the industrial processes and the theoretical studies. In this study, experimental solubility data of 666 pharmaceutical compounds in water and 712 pharmaceutical compounds in organic solvents were collected from different sources. Three different artificial neural networks including multilayer perceptron, radial basis function and support vector machine were constructed to predict the solubility of these different pharmaceutical compounds in water and different solvents. Molecular weight, melting point, temperature and the number of each functional group in the pharmaceutical compound and organic solvents were selected as the input variables of these three different neural network models. The neural network predictions were compared with the experimental data and the SVR-PSO model with the Average Absolute Relative Deviation equal to 0.0166 for the solubility in water and 0.0707 for solubility in organic compounds was selected as the most accurate model.

Abstract Reverse osmosis is a commonly used process in water desalination. Due to the scarcity of freshwater resources and wastewater problems, a lot of theory and experimental studies have been conducted to optimize this process. In the present study, the performance of reverse osmosis membrane module of salt–water separation was simulated based on computational fluid dynamics technique and solution-diffusion theory. Eight geometries of membrane modules four flat sheets, and four tubular membranes were investigated. It was found that if the membrane surface area and inlet flow rate were kept constant for the eight modules, the pressure drop and permeated flow rate would be approximately similar for some geometries (such as the performance of primary flat sheet channel is same as 3 tubular membranes with R=1/3 Rref). The results also showed that because of the phenomenon of concentration polarization, if it is possible to use more membranes with a smaller length, it can reduce the pressure drop and increase the permeation flux of water. Furthermore, the results showed that in similar conditions between the tubular and the plate membranes; the tubular one is more suitable for the water permeation due to its ease of construction and its ability to withstand ECP.

Abstract Abstract
The effect of the solid–wall boundary condition on the segregation behavior of a sand ternary mixture differing in size but having the same proportion has been investigated in a gas–solid bubbling fluidized bed. A multi-fluid computational fluid dynamics model incorporating the kinetic theory of granular flow has been used. The mass fraction profiles of the different-sized particles along the bed height have been experimentally measured by 'freeze–sieving' method. The simulation results of mass fraction distribution and segregation index have been compared against our experimental data in order to evaluate solid–wall boundary conditions in terms of specularity and particle-wall restitution coefficients. The analysis indicates that, the specularity coefficients in range 0.5 to 0.9 lead to the satisfactory results and the best agreement is obtained for =0.9 which corresponds to partial–slip wall boundary condition while the particle–wall restitution coefficient has only a negligible effect on the results. Also maximum segregation index occurs at specularity coefficient of 0.9 at which the segregation pattern may be affected by simultaneous mechanisms of particles circulation and bubbles rising. The effects of superficial gas velocity on the segregation behavior in bubbling regime have also been studied and a significant reduction in segregation index has been observed with increasing gas velocity from 1.1Umf to 1.3Umf.

Abstract A three-layer artificial neural network (ANN) model was developed to predict the remained DO (deoxygenation) in water after DO removal with an enzymatic granular biocatalyst (GB), based on the experimental data obtained in a laboratory stirring batch study. The effects of operational parameters such as initial pH, initial glucose concentration and temperature on DO removal were investigated. On the basis of batch reactor test results, the optimum value of operating temperature, glucose concentration and pH were found to be 30oC, 80 mM and 7, respectively. After back-propagation training, the ANN model was able to predict the remained DO with a tangent sigmoid function (tansig) at hidden layer with 7 neurons and a linear transfer function (purelin) at the output layer. The linear regression between the network outputs and the corresponding targets were proven to be satisfactory with a correlation coefficient of 0.995 for three model variables used in this study.

Abstract The high silica Mn-substituted MFI metallosilicate catalyst with Si/Al molar ratio of 220 and Si/Mn molar ratio of 50 was successfully synthesized by hydrothermal method. The catalyst sample was appropriately characterized by XRD, FE-SEM, EDX and BET techniques. The Mn-substituted MFI metallosilicate has not been reported as the potential catalyst for the methanol to propylene (MTP) reaction. The prepared catalyst was examined in the MTP reaction at the optimal operating conditions. Furthermore, for elucidating the flow field of the MTP fixed bed reactor, a three-dimensional (3D) reactor model was developed. A detailed reaction mechanism which was proposed for the MTP reaction over the Mn-impregnated MFI zeolite (Mn/H-ZSM-5) was properly employed. The reaction mechanism was integrated to a computational fluid dynamics (CFD) for simulating the kinetic, the energy equation and the hydrodynamics of the MTP process, simultaneously. The component distribution during proceeding of the MTP reaction was also simulated as a function of time on stream. The CFD modeling results were validated by the actual data which were obtained over the Mn-substituted MFI metallosilicate catalyst. With regard to the findings, the experimental data were in good agreement with the predicted values of the CFD modeling.

Abstract In the present research, three different architectures were investigated to predict the coefficients of the Daubert and Danner equation for calculation of saturated liquid density. The first architecture with 4 network input parameters including critical temperature, critical pressure, critical volume and molecular weight, the second architecture with 6 network input parameters including the ones in the first architecture with acentric factor and compressibility factor. The third architecture contains 12 network input parameters including 6 input parameters of the second architecture and 6 structural functional groups of different hydrocarbons. The three different architectures were trained and tested with the 160 sets of Daubert and Danner coefficients gathered from the literature. The trained neural networks were also applied to 15 un-known hydrocarbons and the outputs (Daubert and Danner coefficients) were used to predict the saturated liquid densities. The calculated liquid densities were compared with the experimental values. The Results indicated that the coefficients obtained from the second architecture produced more precise values for the liquid densities of the 15 selected hydrocarbons.

Abstract Abstract
Gas to liquid (GTL) process involves heterogeneous catalytic chemical reactions that convert synthesis gas to hydrocarbons and water vapor. A three phase reactor, called Low temperature Fischer-Tropsch (LTFT) is commonly applied for GTL process. In this reactor the gaseous phase includes the synthesis gas, light hydrocarbons and water vapor, the liquid phase is a mixture of the heavy hydrocarbons, and the solid phase is composed of the catalyst and the waxy products. The presence of the liquid phase in LTFT reactor causes mass transfer restriction, affecting the reaction conversion. In this work a numerical simulation of the LTFT fixed bed reactor in trickle flow regime has been accomplished to understand the impact of the liquid phase on the reactor performance. For this purpose, we have developed an axisymmetric two-dimensional multiphase heterogeneous model, where contain carbon monoxide and hydrogen, are transferred into the liquid phase. The reactor consisted of a shell and a tube that was filled with the spherical cobalt catalyst. The reaction conditions were as follows: the wall temperature was 473 K, pressure was 20 bars and a gas hour space velocity (GHSV) was 111 Nml.g_cat^(-1).h^(-1). The numerical simulation results proved the negative impact of the liquid phase on the reaction conversion. The model predictions were evaluated against the reported experimental data and also compared with the result of a numerical pseudo-homogeneous model. It was found that applying the heterogeneous model instead of the pseudo-homogeneous model clearly decreases the deviation of the numerical results.

Abstract The Lattice Boltzmann Method is used to simulate the dynamics of droplet deformation in a channel flow under various conditions. The droplet behavior has been investigated under transient conditions. For cases where the droplet remains attached to the surface, the shape deformation of the droplet during crawling is captured. It has been shown that there is a limiting value for the droplet volume beyond which the critical shear rate remains almost constant and does not demonstrate much correlation with the size of the droplet. The predicted shapes of the droplet at various stages of deformation in the course of the flow by the current LBM code demonstrates more resemblance to the reported experiments than those obtained by a traditional CFD code. The effect of the droplet's initial volume and Reynolds number on the detachment and crawling processes are also investigated. The results are presented at various time steps to better demonstrate the droplet separation. Under the flow conditions investigated, wherever the Aniline droplet detaches, the entire droplet separates from the surface. For an Isoquinoline droplet however, once the main body is detached, a small part of the droplet remains attached to the surface in flows with low Reynolds numbers.

Abstract In this research, photocatalytic degradation method has been introduced to clean up Spent Caustic of Olefin units of petrochemical industries (neutralized Spent Caustic by means of sulfuric acid) in the next step, adaptable method and effective parameters in the process performance have been investigated. Chemical oxygen demand (COD) was measured by the commercial zinc oxide that synthesized with precipitation synthesis method in a two-shell photoreactor. The percent of reduction of COD in the photocatalytic process was modeled using Box–Behnken design and artificial neural network techniques. It was concluded that the ANN was a more accurate method than the design of experiment. The effect of important parameters including oxidant dosage, aeration rate, pH, and catalyst loading was investigated. The results showed that all of the parameters, except pH, had positive effects on increasing COD removal. According to the obtained results, adsorption and photolysis phenomena had a negligible effect on COD removal.

Abstract Temperature distribution is a key function for analyzing and optimizing the thermal behavior of various process equipments. Moving bed reactor (MBR) is one of the high-tech process equipment which tries to improve the process performance and its energy consumption by fluidizing solid particles in a base fluid. In the present study, thermal behavior of MBR has been analyzed through mathematical simulation. Good agreement between the obtained results and both experimental data and analytical solution by self-adjoint method is observed. Mathematical results confirm that the average particle temperature linearly increases across the reactor length. Fluid temperature changes in a parabolic manner, and then it changes linearly. Increasing the Biot number ( ) results in increasing the temperature gradient inside the particle to a maximum value, and thereafter a decreasing pattern is observed. The numerical results confirmed that the finite difference method can be used for thermal analysis of the moving bed reactor.

Abstract Joule-Thomson cooling systems are used in refrigeration and liquefaction processes. There are extensive studies on Joule-Thomson cryogenic systems, but most of them coverage steady state conditions or lack from experimental data. In the present work, transient thermal behavior of Joule-Thomson cooling system including counter current helically coiled tube in tube heat exchanger, expansion valve, and collector was studied by experimental tests and simulations. The experiments were carried out by small gas liquefier and nitrogen gas as working fluid. The recuperative heat exchanger was thermally analyzed by experimental data obtained from gas liquefier. In addition, the simulations were performed by an innovative method using experimental data as variable boundary conditions. A comparison was done between presented and conventional methods. The effect of collector mass and convection heat transfer coefficient was also studied using temperature profiles along the heat exchanger. The higher convection heat transfer coefficient in low-pressure gas leads to increase in exchanging energy between two streams and faster cooling of heat exchanger materials, but the higher convection heat transfer coefficient in high-pressure gas does not influence on cool-down process.