Journal of Chemical and Petroleum Engineering
Volume:49 Issue: 2, Dec 2015

Hot spots are among the most serious operational issues in furnaces as they may result in the destruction of tubes. Hence, it is essential to locate such hot spots precisely on the tube surfaces inside the industrial furnaces inorder to secure a safe design and operation. In the current study, we have extended the model proposed by Talmor in order to precisely locate the combustion hot spots on the surface of the tubes inside process furnaces with arbor coils. These furnaces are mostly used in catalytic reforming units. The Talmor model is one of the best models for analyzing combustion hot spots on the tube surfaces of industrial furnaces. This model is developed by considering the furnace geometry and arrangement (tube arrangements and burner positions). In the current paper, we have derived and extended the equations formulated by Talmor for furnaces with arbor coil. As the second step, a specific furnace already installed in a catalytic conversion unit of a refinery has been selected for the sake of modeling. The modeling of the preceding furnace was completed using the code prepared for this purpose. The results have been used not only for analyzing the hot spots but also to model the heat flux profile inside the furnace. Upon modeling the furnace, the location of the hot spot on the 20th coil was predicted which was consistent with the experimental results.

This paper discusses the use of Box Behnken design (BBD) approach to plan the experiments for turning the yield of CVD, thickness and layer number of graphene sheets with an overall objective of optimizing the process to provide higher graphene production volume, fewer layers and thinness structure of graphene. BBD is having the maximum efficiency for an experiment involving four factors such as total gas flow, gas ratio (H2/CH4), temperature, and reaction time in three levels. The proposed BBD requires 25 runs of experiment for data acquisition and modeling the response surface. Three regression models were developed and their adequacies were verified to predict the output values at nearly all conditions. Further, the models were validated by performing experiments, taking three sets of random input values. The output parameters measured through experiments (actual) are in good consistency with the predicted values using the models. This work resulted in identifying the optimized set of turning parameters for CVD process to achieve high yield value and good structure of graphene. In the best condition, yield of process is 6.1%.

Gas hydrate formation in production and transmission pipelines and consequent plugging of these lines have been a major flow-assurance concern of the oil and gas industry for the last 75 years. Gas hydrate formation rate is one of the most important topics related to the kinetics of the process of gas hydrate crystallization. The main purpose of this study is investigating phenomenon of gas hydrate formation with the Presence of kinetic Inhibitors in operation gas transmission, and prediction of gas hydrate formation rate in the pipeline. In this regard, by using experimental data and Intelligent Systems (Artificial neural networks and adaptive neural–fuzzy system), two different high efficient and accurate models were designed to predict hydrate formation rate of CO2, C1, C3, and i-C4. It was found that such models can be used as powerful tools, for prediction of gas hydrate formation rate with total average of absolute deviation less than 6%.

Liquid membrane processes have attracted many interests in recent years for removal of heavy metals such as cadmium from industrial wastewaters. In this study, a modified hybrid liquid membrane system is introduced. The setup is worked by applying the water-insoluble dioctyl phthalate as the organic solvent. N-octanol and tetra butyl ammonium bromide are added to the organic phase to increase the system efficiency. The effects of different parameters such as pHs of the feed and stripping phases, the complexing agent concentration, the organic film thickness, initial concentration of cadmium, and the carrier concentration on the cadmium removal are studied. The results demonstrate the increase in the removal rate and capacity comparing to those of previously studied hybrid liquid membrane system. An electrical potential is then applied to the hybrid liquid membrane system. The results show higher removal rate and capacity compared to the corresponded values in the system without applying electrical potential.

Sieve trays are widely used in the gas- liquid contactors such as distillation and absorption towers. In this article, a three-dimensional, two phase CFD model using Euler-Euler framework was developed to simulate a distillation tower with two sieve trays. Hydrodynamic simulation of air and water system in different rates of gas phase was carried out and velocity distribution parameters, clear liquid height and froth height were calculated and compared to the experimental data and the literature simulation result. Liquid velocity distributions on the two trays were found to be in relatively good agreement with experimental data. It was found that heights of accumulated liquid in the down-comers are not equal.

In this study, a three–layer \ artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of the network while corresponding pressure gradient was selected as its output. A tansig and a linear function were chosen as transfer functions for hidden and output layers, respectively and Levenberg–Marquardt back–propagation algorithm were applied to train the ANN. The optimal topology of the ANN was achieved with 16 neurons in hidden layer, which made it possible to estimate the pressure gradient with a good accuracy (R2=0.996 & AAPE=7.54%). In addition, the results of the developed ANN model were compared to Al–Wahaibi correlation results (with R2=0.884 & AAPE=17.17%) and it is found that the proposed ANN model has higher accuracy. Finally, a sensitivity analysis was carried out to investigate the relative importance of each input parameter on the ANN output. The results revealed that the pipe diameter (D) has the most relative importance (24.43%) on the ANN output, while the importance of the other parameters is nearly the same.

Due to the high price of virgin vegetable oils and the drawbacks of the homogeneous catalytic transesterification, in this work an economically profitable alternative process was proposed for biodiesel synthesis in which transesterification of the low-cost waste cooking oil (WCO) with methanol in a heterogeneous system was done. Alumina impregnated with sodium hydroxide was utilized as a solid base catalyst along with a high yield and less waste streams. The optimum combination for transesterification reaction was determined as methanol-to-oil molar ratio 7:1, catalyst amount 1.5%, reaction time 4 hr and reaction temperature 70oC. At this optimum condition, the fatty acid methyl ester (FAME) yield was over 92%. Fourier Transform Infrared spectroscopy (FT-IR) was the assessing technique for detection of biodiesel and glycerol in this work and biodiesel’s physical properties including flash point, fire point, density, kinematic viscosity, cloud point, pour point and acid value have also been measured which satisfied the requirement of the international standards.

In recent years, biodiesel has been considered as a good alternative of diesel fuels. Density and viscosity are two important properties of these fuels. In this study, density and kinematic viscosity of biodiesel-diesel blends were estimated by using artificial neural network (ANN). A three-layer feed forward neural network with Levenberg-Marquard (LM) algorithm was used for learning empirical data (previous studies data and this study empirical data). Input data for estimating density and kinematic viscosity includes components volume fraction, temperature and pure component properties (pure density at 293.15 K and pure kinematic viscosity at 313.15 K). Results of neural network simulation for density and kinematic viscosity showed a high accuracy (mean relative error for density and kinematic viscosity are 0.021% and 0.73%, respectively).