فهرست مطالب abbas naderifar

The performance of the ionic liquid (1-ethyl-3methylimidazolium bis (trifluoromethylsulfonyl)imide, [EMIM][NTF2]) as a green, non-aqueous and hydrophobic solvent and sulfolane as a conventional solvent for liquid-liquid extraction of toluene from its mixture with heptane was investigated. The LLE phase behavior was predicted with the help of the UNIFAC as a predictive thermodynamic model. The interfacial tension of the studied systems was measured to investigate the effect of the dissolved toluene concentration on interfacial tension. It was revealed that the average selectivity of the systems with IL (316.752) is much higher than the systems containing sulfolane (18.661) under similar experimental conditions. For low mass fractions of toluene less than 0.4, the performance of the IL is better, whereas, for high toluene mass fractions more than 0.4, sulfolane exhibits superior performance.

The presence of water in the oil often leads to problems reported in the literature from pressure flow limitations, reduced production, pipeline corrosion, pump malfunctions, poisoning of downstream catalysts in catalyst refineries, and other problems related to production equipment and distillation column. In this study, a coated filter with graphitic nitride carbon nanosheets (g-C3N4) was prepared and proposed as an alternative to demulcifiers to separate water from oil before entering the petroleum refinery. For this purpose, mesopore graphitic nitride carbon nanosheets are synthesized by thermal polycondensation method using hard-template approach and coated on the surface of stainless steel mesh using spray gun. The surface of the g-C3N4 coated mesh showed the hydrophilic/underwater superoleophobic wettability properties. The results of water-oil separation using the prepared filter showed that water permeated through the filter with a flux of 2200 L.m-2.h-1, while oil does not pass and remains on top of the filter with the separation efficiency more than 99%. The durability of the filter for water and oil separation was investigated and the results showed that the separation efficiency and flux after 25 cycles of water-oil separation have changed slightly.

The complexity of the fermentation processes is mainly due to the complex nature of the biological systems which follow the life in a non-linear manner. Joined performance of artificial neural network (ANN) and genetic algorithm (GA) in finding optimal solutions in experimentation has found to be superior compared to the statistical methods. Range of applications of β-cyclodextrin (β-CD) as an enzymatic derivative of starch is diverse, where the complex performance of cyclodextrin glucanotransferase (CGTase) as the involved enzyme is not well recognized.. The aim of the present work was to use ANN systems with different training algorithms and defined architectures joined with GA, in order to optimize β-CD production considering temperature of the reaction mixture, substrate concentration, and the inoculum’s pH as the input variables.. Commercially Neural Power, version 2.5 (CPC-X Software, 2004) was used for the numerical analysis according to the specifications provided in the software. β-CD concentration was determined spectrophotometrically according to phenolphthalein discoloration technique, described in the literature.. Randomly obtaining the experimental data for β-CD production in a fermentation process, could get explainable order using the ANN system coupled with GA. Changes of the β-CD as the function of each of the three selected input variables, were best quantified with use of the ANN system joined with the GA. The performance of the IBP learning algorithm was highly favorable (10300 epoch’s number within 5 second, with the lowest RMSE value) while the sensitivity analysis of the results which was carried out according to the weight method, were indicative of the importance of input variables as follows: substrate concentration < temperature < inoculum’s pH. For instance, small changes in the system’s pH are associated with the large variation in the β-CD production as has been described by the suggested model.. Production of β-CD (enzymatic derivative of starch) by B. licheniformis was satisfactorily described based on multivariate data analysis application of the ANN system and the experimental data were optimized by considering ANN plus the GA where the IBP was used as the training method and with use of three neurons as the constructed variables in the hidden layer of the test network..