Iranian journal of chemical engineering
Volume:3 Issue: 3, Summer 2006

Analytical gas-permeation models for predicting the separation process across membranes (exit compositions and area requirement) constitutes an important and necessary step in understanding the overall performance of membrane modules. But, the exact (numerical) solution methods suffer from the complexity of the solution. Therefore, solutions of nonlinear ordinary differential equations that govern the performance of the membrane modules for gas separations by approximate and asymptotic methods are useful in the design and comparison of processes. In this work, the asymptotic methods were applied for predicting the performance of nanometric tubular ceramic membranes in the separation of binary gas mixtures with cocurrent and countercurrent flow patterns. Also, the exact (numerical) solutions of the governing equations using the fourth order Rung-Kutta technique were proposed. The comparison of the results showed a good agreement between the exact solution and asymptotic analysis methods over the whole range of selectivities ( ). Because, the asymptotic curves into the former ( ) and latter ( ) boundaries had a suitable overlap with each other to cover the whole range of selectivities. The accuracy of this method was verified by a comparison of the predicted results with different literature experimental data and mathematical models. This result suggests the use of the asymptotic analysis method to provide excellent shortcut, preliminary design information.

In this paper a two impinging streams cyclone contacting system suitable for handling of solid-liquid systems has been studied. Certain pertinent parameters such as: solid holdup, mean residence time and Residence Time Distribution (RTD) of solid particles have been investigated. A stochastic model based on Markov chains processes has been applied which describe the behavior of solid particles in the contacting system. From this model the RTD data were estimated and compared with the experimental results. The RTD data were obtained at different t and compared with those estimated from the model. At t = 0.362 s a good correlation has been observed between the predicted and experimental data. The RTD data may be used to determine certain pertinent characteristic parameters of physical and chemical apparatuses such as conversion in chemical reactors.

In this study we developed LaNixAl1-xO3 perovskite systems using a sol-gel method (with propionic acid as solvent) to use in methane-reforming reactions for producing synthesis gas. To understand the roles of the nature of the precursor and calcination conditions on the formation of LaNixAl1-xO3, we carried out identifications using NMR, FT-IR, XRD, SEM, and TEM. The precursor structure is a function of raw materials and calcination conditions. Nitrate salts of nickel, aluminium, and lanthanum, and calcinations at 750ºC for 4 h gave pure LaNixAl1-xO3 perovskite with good homogeneity, even at nanoscopic scales. These systems are highly efficient catalysts in steam and the dry reforming of methane. Various ratios of hydrogen to carbon monoxide in synthesis gas can be achieved by changing the feed type. We also investigated stabilization of these systems by studying the perovskite structure after reactivity tests. The optimum mixed perovskite for steam and dry reforming of methane is LaNi0.3Al0.7O3. The total conversion of CH4 is rapidly obtained at 750°C in steam reforming with a H2O/CH4 ratio = 3, the selectivity of CO is lower (55%) and the yield of hydrogen (98%) is higher compared to the ratio H2O/CH4 = 1. After 170 h of reaction, no deactivation had occurred, methane conversion remained higher than 90% at 750°C and in dry reforming, methane conversion and CO yield are about 98% and 95% respectively.

Surfactants are one of the major components (10- 18%) of detergents and household cleaning products and are used in high volumes in developed countries. In the present research work, the ability of rice husk as a low cost adsorbent for anionic and nonionic surfactants in wastewater has been studied.
The maximum removal efficiency for anionic surfactants was 97%, in an aqueous solution that contains 10mg/lit sodium linear alkyl benzene sulphonate in pH 2 and for nonionic surfactants was up to 75% in an aqueous solution that contains 20mg/lit nonyl phenol ethoxylate in pH 6-7.
The mechanism of surfactant removal by rice husk was attributed to the physicochemical characteristics of rice husk. In addition, the size of micelles and critical micelle concentration are two important factors in the removal yield.

In this research, Fischer Tropsch synthesis (FTS) has been modeled in the fixed bed chromatographic reactor for the first time by applying a rather complex dispersed plug flow model for fluid phase and linear driving force (LDF) model for adsorbent. Model equations are dynamic, multi-component, non-linear and heterogeneous including reaction and adsorption simultaneously Complex kinetics for FTS and water-gas shift (WGS) reaction and the multicomponent Langmuir adsorption isotherm is used in the model. A set of partial differential and ordinary differential equations with algebraic equations have been converted into a set of ordinary differential equations by using the orthogonal collocation technique. Then this set of equations has been solved by multi-step methods of Numerical Differentiation Formulae (NDF) or Backward Dif-ferentiation Formulae (BDF) Known as the Gear’s method. Consequently, results for dynamic model and effects of modeling parameters have been analyzed. Through this fixed bed chromatographic reactor model, one may develop a suitable configuration of simulated moving bed chromatographic reactors.

The aim of this study was to investigate the particulate dispersion from Kerman Cement Plant. The upwind – downwind method was used to measure particle concentration and a cascade impactor was applied to determine particle size distribution. An Eulerian model, Gaussian plume model and an artificial neural network have been used to compute and predict concentration of PM10 from Kerman Cement Plant. Eulerian model incorporates source related factors, meteorological factors, surface roughness and particle settling to estimate pollutant concentration from continuous sources. The measured data have been used to create an artificial neural network for predicting suspended particle concentration from Kerman Cement Plant. The data includes particle concentration, distance from source, mixing height, lateral and vertical dispersion parameters and 10 meters wind speed. The performance of these models has been compared with the measured data. The AAPD (Average Absolute Percent Deviation) parameter for the results of the Eulerian model, Gaussian model and ANNs was 25.53%, 15.38% and 5.91% respectively.