Volume 17, Issue 3

In this study, the viscosity of hydrocarbon binary mixtures has been predicted with an artificial neural network and a group contribution method (ANN-GCM) by utilizing various training algorithm including Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Resilient back Propagation (RP), and Gradient Descent with variable learning rate back propagation (GDX). Moreover, different transfer functions such as Tan-sigmoid (tansig), Log-sigmoid (logsig), and purelin were investigated in hidden and output layer and their effects on network precision were estimated. Accordingly, 796 experimental data points of viscosity of hydrocarbon binary mixture were collected from the literature for a wide range of operating parameters. The temperature, pressure, mole fraction, molecular weight, and structural group of the system were selected as the independent input parameters. The statistical analysis results with R 2  = 0.99 revealed a small value for Average absolute relative deviation (AARD) of 1.288 and Mean square error (MSE) of 0.001018 by comparing the ANN predicted data with experimental data. Neural network configuration was also optimized. Based on the results, the network with one hidden layer and 27 neurons with the Levenberg-Marquardt training algorithm and tansig transfer function for hidden layer along with purelin transfer function for output layer constituted the best network structure. Further, the weights and bias were optimized to minimize the error. Then, the obtained results of the present study were compared with the data from some previous methods. The results suggested that this work can predict the viscosity of hydrocarbon binary mixture with better AARD. In general, the results indicated that combining ANN and GCM model is capable to predict the viscosity of hydrocarbon binary mixtures with a good accuracy.

In this paper, oxidative desulfurization (ODS) process is studied for the purpose of removing the sulfur components from light gas oil (LGO) via experimentation and process modeling. A recently developed (by the authors) copper and nickel oxide based composite nano-catalyst is used in the process. The ODS experiments are conducted in a batch reactor and air is used as an oxidizer under moderate operation conditions. Determination of the kinetic parameters with high accuracy is necessary of the related chemical reactions to develop a helpful model for the ODS operation giving a perfect design of the reactor and process with high confidence. High conversion of 92% LGO was obtained under a reaction temperature of 413 K and reaction time of 90 min for synthesized Cu Ni /HY nano-catalyst. Here model based optimization technique incorporating experimental data is used to estimate such parameters. Two approaches (linear and non-linear) are utilized to estimate the best kinematic parameters with an absolute error of less than 5% between the predicted and the experimental results. An environmentally friendly fuel is regarded the main goal of this study, therefore the optimization process is then employed utilizing the validated model of the prepared composite nano-catalyst to get the optimal operating conditions achieving maximum conversion of such process. The results show that the process is effective in removing more than 99% of the sulfur from the LGO resulting in a cleaner fuel.

The present study reports numerical results of coupled heat transfer by natural convection and surface radiation in an open air-filled cavity. Two heating modes are considered; in the first mode called LH (lateral heating), the cavity is heated via its lateral wall, while in the second mode named CH (corner heating), the cavity is heated via its bottom corner (the lower half of the vertical wall and the left half of the bottom wall). The rest of the walls are assumed to be perfectly adiabatic. The conservation equations were solved using the Finite Volume Method (FVM) combined with the SIMPLE algorithm (Semi-Implicit Method for Pressure Linked Equations). The radiation heat transfer between the different surfaces of the cavity was treated by the radiosity-irradiation method. Results are presented in terms of isotherms, streamlines, and Nusselt numbers. The effect of the Rayleigh number Ra on the flow structure, the distribution of temperature gradients, the local and mean Nusselt numbers is discussed. Also, a comparison between results of the two heating modes is conducted.

This study aims to ascertain the influence of the activity coefficient model and equation of state for predicting the vapor–liquid equilibrium (VLE) of the multi-electrolytic system H 2 O–NH 3 –CO 2 . The non-idealities of the liquid phase are described by the eUNIQUAC and eNRTL models. The vapor phase is modeled with the Nakamura equation, which is compared with the ideal gas assumption. The models are validated with experimental data from literature on total pressure and ammonia partial pressure. Results show that the models UNIQUAC and NRTL without dissociation can only reproduce the experimental conditions in the absence of CO 2 . When the electrolytic term is considered, the eUNIQUAC model is able to reproduce the experimental data with greater accuracy than the eNRTL. The equation of state which describes the vapor phase plays no major role in the accuracy of the VLE prediction in the operational conditions evaluated here. Indeed, the accuracy relies on the activity coefficient, therefore the ideal gas equation can be considered if the non-idealities of the liquid phase are described by a well-tuned model. These findings could be useful for equipment design, flowsheet simulations and large-scale simultaneous optimization problems.

The spinning cone columns (SCC) are one of the distillation columns with increasing applications in food industries. The geometrical complexity and different flow regimes, besides the presence of moving parts, make the design and analysis of these columns challenging. Computational fluid dynamics analysis of SCC columns has shown promising results in analyzing the performance of these towers. The majority of previous works were pertinent to the air/water systems. Therefore, the application of these results to real systems is not very clear. In this study, the liquid film thickness, mass transfer coefficients, HETP, and Murphree vapor efficiency for the water/ethanol system have been predicted in a pilot-scale column. The results show that by increasing the radial distance from the axis, the thickness of the liquid film gradually decreases. This finding is also in consistent with the experimental results. The maximum thickness of the liquid film is <1 mm and is near the axis. Mass transfer coefficients in the liquid phase and in the gas phase increase slightly with increasing flow velocity and remain almost unchanged. The average values of these coefficients in the liquid and gas phases are 0.023 (s −1 ) and 1.21 (s −1 ), respectively. HETP increased with increasing gas velocity, the range of which varies between 0.092 and 0.375 m. Also, Murphree vapor efficiency at three rotational speeds of 550, 750, and 1000 rpm are predicted and compared with the experimental data. The results show that the efficiency has been decreased by increasing the strip ratio and increased by increasing the rotational speed. Minimum and maximum efficiencies obtained are 3.48 and 24.56% corresponding to strip ratio = 27.1% and RPM = 550 plus strip ratio = 9.15% and RPM = 1000, respectively. The predicted efficiencies are in a reasonable agreement (within 10.3%) with experimental data.

Natural gas dehydration is essential in gas processing to avoid serious problems. As a pretreatment in a cryogenic Natural Gas Liquid (NGL) recovery process, it typically uses triethylene glycol (TEG) and followed by a Molecular Sieve dehydration to achieve 1 mg/Sm 3 of water moisture in the dehydrated gas. This work studied the retrofitting of the existing dehydration unit to improve its performance in satisfying the gas moisture qualities. The retrofitted process uses recycled stripping gas schemes to achieve high purity TEG while minimizing the use of fresh stripping gas. The results revealed that the recycled stripping gas has provided sufficiently high purity TEG (>99.99%-wt), significantly reduced the heating and cooling duty by 80%, and reduced the electrical duty by 29% compared to the base case. The TAC was reduced by 38.1% from $ 725,245/year to $ 448,670/year. Through this study, the evaluated cases provide similar dehydration results with less equipment, simpler process, more energy-efficient, and better economic numbers. Therefore, a better process was obtained.