Iranian journal of chemical engineering
Volume:19 Issue: 3, Summer 2022

The Mixed Matrix Membrane (MMM) concept consists of incorporating suitable polymers with inorganic or organic fillers. The majority of polymeric membranes maintain a trade-off between permeation and selectivity, which restricts their development in separation applications. In this paper, less reviewed challenges on development of MMMs, such as the preparation of mix-matrix resistant membranes for industrial gas separation applications, as well as the use of appropriate and compatible fillers for different types of polymers were discussed. The MMMs comprising Metal Organic Framework (MOF) fillers were extensively studied. The importance of MOFs includes finely tunable structures, excellent compatibility with polymer matrices, and molecular sieve action. MMMs are considered promising structures that combines the advantages of polymeric and inorganic membranes. They exhibit the potential to upgrade the separation performance of pure polymer membranes using filler materials, whereas the cost remains relatively lower than that of pure inorganic membranes. The development of novel filler materials makes a substantial contribution in terms of role-playing.

The impact of bed loading on minimum spouting velocity (ums) of polydispersed TiO2 particles was studied in a conical fluidized bed. The experiments were performed at different bed loadings according to Gaussian and narrow-cut particle size distribution (PSD). The bed consisted of simple-agglomerates in size range of 30-90 µm belonging to Geldarts’ group A classification. The effect of PSD and interparticle force (IPF) on the predicted ums and hysteresis in the pressure profiles were studied through a combination of computational fluid dynamics and discrete element method (CFD-DEM). The experimental data showed that the choice of bed with Gaussian PSD-type led to more accurately predicting ums than the narrow-cut particle PSD. The impact of IPF on the expected ums became more critical than the PSD type because of an increase in bed loadings. The lowest deviations the results were obtained in the low bed loadings, which is confirmed the accuracy of simulation results. The simultaneous effects of PSD-type and IPF led to a change in the fluidization behavior of the bed. The bed with narrow-cut PSD has a hydrodynamic behavior similar to spouting and slugging regimes, while the fluidization quality of the bed improves by fine particles.

Optimization of the homogeneous rhodium-catalyzed methanol carbonylation reactor to reduce CO2 emissions is studied in this line of research. In this paper, the steady-state homogeneous rhodium-catalyzed methanol carbonylation reactor is simulated using Aspen HysysV.9 software, by comparing the simulation results with industrial information, a mean relative error (excluding methanol) of 4.8% was obtained, which indicates the high accuracy of the simulation. The central composite design (CCD) and genetic algorithm (GA) with the aid of a simplified process simulation were used to estimate the effect of individual variables (liquid level, the temperature of the catalyst-rich recycle stream, the mole ratio of CO to methanol (MeOH) in the feed, and flow rate of dilute acid stream) and their mutual interactions to reduce CO2 emissions. It is obtained that the liquid level percentage of 46%, the catalyst-rich recycle stream temperature of 120 °C, CO: MeOH molar ratio equal to 1.13:1, and the dilute acid flow rate of 513.14 kmol/hr lead to CO2 reduction by 34%.

Most industrial operating units are basically in contact with two gas and liquid phases. Bubble characteristics over the last years have been determined through different methods. In this project a mass transfer system has been designed for absorbing gas bubbles by liquid phase. The mass transfer and hydrodynamic behavior in the wake of single rising air bubbles were investigated by using an image analysis method and empirical relations. By considering these methods, the overall bubble properties including the size of single bubble, shape, path, rising velocity and mass transfer coefficient were studied and measured. The investigation was developed with 0.15×0.15×0.35 m3 bubble column and nozzle diameter (0.5, 1, 1.5, 2, 2.5 mm) in different liquids considering viscose changes. Moreover, from the results obtained, it can be concluded that the increase of nozzle diameter increases the bubble diameter which results in reduction of velocity and mass transfer coefficient. This is a fact that, by raising the viscosity of liquid phase the bubble diameter stands at the highest level and on the contrary velocity and mass transfer coefficient stand at the lowest level. So according to these outcomes we can conclude that, the diameter of bubble depends on physical properties of fluids and has a direct relation with nozzle diameter.

Biodiesel, as a renewable and environmentally friendly fuel, is a feasible alternative to fossil diesel, which has gained great popularity in recent years. However, due to some undesirable properties such as higher viscosity, biodiesel must be blended with diesel in order to be utilizable in a diesel engine. Therefore, a reasonable approach is required for predicting the diesel-biodiesel blend properties. This study tries to estimate two substantial properties of blend, i.e. kinemattic viscosity (KV) and cetane number (CN), through neural network (NN) and empirical models which use pure properties of biodiesel (kinematic viscosity, boiling point, evaporation point, flash point, pour point, heat of combustion, cloud point, and specific gravity) as independent variables. In this regard, a three-layer feed-forward network with varying input parameters, training algorithms, transfer functions, and hidden neurons has been examined to predict the KV and CN of the diesel-biodiesel blend. Besides, the prediction capability of thirty empirical equations is investigated to determine the top equations describing blend properties. The result reveals that an ANN with three input parameters of biodiesel concentration (%), the CN of biodiesel, and biodiesel cloud point has the best prediction quality of CN with an R-value of 0.9961. Moreover, NN estimates the KV of blend with the highest correlation coefficient of 0.9985. The results corresponding to empirical equations also indicate that fractional-exponential equations are the best describer of the CN and KV of blend with R-values of 0.9947 and 0.9980, respectively.

Methanol is an important industrial chemical, and its synthesis and purification units are among the most widely used processes in the field of energy. The two-column separation unit of methanol has been analyzed from the thermodynamic and energy points of view in the present study. The simulation has been done by Aspen Hysys V11 and the SRK equation has been regarded as the most appropriate equation of state (EOS) for this simulation with the mean relative error (MRE) of 2 %. Then, the design of the heat exchanger network (HEN) has been calculated using the Aspen Energy Analyzer V11. Both distillation towers have been analyzed using pinch technology. As a result, the amount of hot and cold utilities used has been LP=1.482×〖10〗^8, MP=1.57×〖10〗^4, and Air =1.423×〖10〗^8, respectively. Besides, the total heating and cooling target of the process has been 1.482×〖10〗^8 and 1.423×〖10〗^8, accordingly. Then, the 〖∆T〗_min (minimum allowable temperature difference between hot and cold currents) and its effect on the annual cost have been investigated. The optimum value 〖∆T〗_min is determined to have better-operating conditions and to meet the design of the HEN economically. Reducing 〖∆T〗_min increases operating costs and reduces energy costs.