Iranian Journal of Chemical Engineering (IJChE)

Abstract Mass transfer coefficients of rising drops in spray and packed columns with random and structured packing in a liquid-liquid extraction operation were experimentally measured and the results compared. In this work, a high interfacial tension chemical system of toluene-acetic acid-water in a structured packed column is investigated. Results of random and structured packing show that random types are effective only for a drop size less than 9 mm, while the structured ones are shown to have a positive effect on mass transfer coefficient in a wide drop size range. Furthermore, structured packing proved to be slightly more effective than random packing in improving the mass transfer coefficient. The effect of drop size on mass transfer coefficient has also been studied in this work and the results showed that when the drop diameter increases, the mass transfer coefficient increases too. Finally, new correlations for the prediction of the mass transfer coefficient in both a random and structured packed column have been introduced which are in better agreement with the experimental data in comparison with those resulted from Newman, Kronig-Brink and Handlos-Baron models.

Abstract In this work, the equation of state-excess Gibbs energy (EoS-G E ) model has been employed to predict Liquid-Liquid Equilibria (LLE) of some binary mixtures at 0.1 MPa. Three binary systems containing Decane+Methanol, Cyclohexane+Methanol and Cyclohexane+2,2,2-Trifluoroethanol (TFE) have been tested using Peng–Robinson EoS modified by Stryjek and Vera (PRSV), along with the excess Gibbs mixing rules MHV1 and MHV2 and two excess Gibbs models: Wilson equation modified by Tsuboka and Katayama (T–K–Wilson) and UNIQUAC equation. The interaction parameters of T-K-Wilson and UNIQUAC G E models for three binary systems have been determined from LLE data points at 0.1 MPa. The prediction ability of the models has been evaluated by comparison of the results with experimental data. Average Absolute Error (AAE) of 0.047 for MHV1 and T–K–Wilson model, 0.0117 for MHV2 and T–K–Wilson model, 0.0317 for MHV1 and UNIQUAC model and 0.0109 for MHV2 and UNIQUAC model have been obtained. As it is clear, the combination of MHV2 excess Gibbs mixing rule with UNIQUAC equation shows a satisfactory agreement with experimental data.

Abstract The separation of immiscible liquids is an important process in oil and petrochemical industries. In the outlet stream of a catalytic reactor of dehydrogenation of ethyl benzene to styrene monomer, water is present because it is used as a high pressure steam to provide reaction heat. Therefore, aqueous and immiscible organic phases should be separated in a horizontal separator before fractionation. The aim of this work is to study the batch separation of ethyl benzene and styrene from water. Different mixtures of water, styrene and ethyl benzene were prepared using different amounts of organic compounds and various mixing rates. The experiments show that the separation time of ethyl benzene and water mixtures are more than that for styrene and water mixtures. Furthermore, increasing the mixing rate increases the separation time because the dispersity of the system increases, but it has more effects on water/ethyl benzene mixtures.

Abstract Stuck pipe is one of the most serious drilling problems, estimated to cost the petroleum industry hundreds of millions of dollars annually. One way to avoid stuck pipe risks is to predict the stuck pipe with the available drilling parameters which can be employed to modify drilling variables. In this work, Artificial Neural Network (ANN) was used for stuck pipe prediction according to the fact that this method is applicable when relationships of parameters are too complicated. Based on the drilling fluid condition from one of the Iranian oil fields, stuck pipe instances were divided into static and dynamic types. The results of this study show more than 90% accuracy for stuck pipe prediction in the investigated oilfield. The methodology presented in this paper enables the Iranian drilling industry to estimate the risk of stuck pipe occurrenc during the well planning procedure.

Abstract Ibuprofen solubility in supercritical carbon dioxide was measured using a dynamic apparatus at a pressure between 80 and 140 bars at three different temperatures, 308.15, 313.15 and 318.15 K. The mole fraction of Ibuprofen in fluid phase was in the range of 0.015 × 10 -3 - 3.261 × 10 -3 at the mentioned operational condition. Modified Mendez-Santiago and Teja equation were used to check the consistency of the experimental data. Results were correlated using the Stryjek and Vera equation of state with the van der Waals 1-parameter (vdW1) and 2-parameters (vdW2) mixing and combining rules. Interaction parameters along with the percentage of the average absolute relative deviation (%AARD) were displayed. Also, the Lydersen group contribution methods were used for predicting the physicochemical and critical properties of the Ibuprofen.

Abstract In this contribution Artificial Neural Network (ANN) modeling of the hydrocracking process is presented. The input–output data for the training and simulation phases of the network were obtained from the Tehran refinery ISOMAX unit. Different network designs were developed and their abilities were compared. Backpropagation, Elman and RBF networks were used for modeling and simulation of the hydrocracking unit. The residual error (root mean squared difference), correlation coefficient and run time were used as the criteria for judging the best network. The Backpropagation model proved to be the best amongst the models considered. The trained networks predicted the yields of products of the ISOMAX unit (diesel, kerosene, light naphtha and heavy naphtha) with good accuracy. The residual error (root mean squared difference) between the model predictions and plant data indicated that the validated model could be reliably used to simulate the ISOMAX unit. A four-lumped kinetic model was also developed and the kinetic parameters were optimized utilizing the plant data. The result of the best ANN model was compared to the result of the kinetic model. The root mean square values for the kinetic model were slightly better than the ANN model but the ANN models are more versatile and more practical tools in such applications as fault diagnosis and pattern recognition.

Abstract This paper reports a study to find optimum conditions for oil extraction from vetiver root. For this purpose, the influence of temperature and pressure on the extraction yield is investigated. In addition, the effects of supercritical fluid flow rate and particle diameter on optimum temperature and pressure have been studied. The results show that optimum pressure is a strong function of particle diameter and solvent flow rate. However, the results reveal that the optimum temperature is independent from particle diameter and solvent flow rate.