Mixing–segregation phenomena of binary system in a fluidized bed

Computational analysis of the particle size effect on the pressure profiles and type of flow regimes of TiO2 microparticles in a fluidized bed

The effect of particle size on the pressure profiles and flow regimes of the bed containing TiO2 microparticles (MPs) was investigated in a fluidized bed. The fluidization behavior of particles with mean diameters, d p , of 170, 200, 225, and 300 μm at different gas velocities, U g , was investigated both experimental and computational viewpoints. A computational fluid dynamic (CFD) model was developed by the Eulerian–Eulerian approach to evaluate the sensitivity of the Syamlal–O’Brien, and Gidaspow drag models on the predicted results of the bed pressure profiles. The results showed that with increasing particle size, the amplitude of pressure fluctuations increases and the type of flow regime in the bed tended from bubbling to slugging flow regime. The error analysis showed that the use of the Gidaspow model led to more accurate results than the Syamlal–O’Brien model in predicting the bed pressure drop and pressure fluctuations in the slugging flow regime. However, the Syamlal–O’Brien model was more suitable for predicting the pressure profiles in the bubbling flow regime. The results were more suitable for the bed containing particles of 300 μm than the beds with d p  ≤ 225 μm. The highest and lowest deviations between the experimental data and simulation outputs were obtained at U g of 0.295 and 0.650 m/s, respectively. The findings confirmed that the mutual effects existed between the d p pressure profiles, and the type of flow regimes in the bed.

Systematic Study of Pressure Fluctuation in the Riser of a Dual Inter-Connected Circulating Fluidized Bed: Using Single and Binary Particle Species

This study systematically investigates the pressure fluctuation in the riser of a dual interconnected circulating fluidized bed (CFB) representing a 10 kWth cold-flow model (CFM) of a chemical-looping combustion (CLC) system. Specifically, a single-species system (SSS) and a binary-mixtures system (BMS) of particles with different sizes and densities were utilized. The pressure fluctuation was analyzed using the fast Fourier transform (FFT) method. The effect of introducing a second particle, changing the inventory, composition (i.e., 5, 10 to 20 wt.%), particle size ratio, and fluidization velocity were investigated. For typical SSS experiments, the results were similar to those scarcely reported in the literature, where the pressure fluctuation intensity was influenced by varying the initial operating conditions. The pressure fluctuations of BMS were investigated in detail and compared with those obtained from SSS experiments. BMS exhibited different behaviour; it had intense pressure fluctuation in the air reactor and in the riser when compared to SSS experiments. The standard deviation (SD) of the pressure fluctuation was found to be influenced by the fluidization regime and initial operating conditions, while the power spectrum density (PSD) values were more sensitive to the presence of the particles with the higher terminal velocity in the binary mixture.

Developed Hybrid Model for Propylene Polymerisation at Optimum Reaction Conditions

A statistical model combined with CFD (computational fluid dynamic) method was used to explain the detailed phenomena of the process parameters, and a series of experiments were carried out for propylene polymerisation by varying the feed gas composition, reaction initiation temperature, and system pressure, in a fluidised bed catalytic reactor. The propylene polymerisation rate per pass was considered the response to the analysis. Response surface methodology (RSM), with a full factorial central composite experimental design, was applied to develop the model. In this study, analysis of variance (ANOVA) indicated an acceptable value for the coefficient of determination and a suitable estimation of a second-order regression model. For better justification, results were also described through a three-dimensional (3D) response surface and a related two-dimensional (2D) contour plot. These 3D and 2D response analyses provided significant and easy to understand findings on the effect of all the considered process variables on expected findings. To diagnose the model adequacy, the mathematical relationship between the process variables and the extent of polymer conversion was established through the combination of CFD with statistical tools. All the tests showed that the model is an excellent fit with the experimental validation. The maximum extent of polymer conversion per pass was 5.98% at the set time period and with consistent catalyst and co-catalyst feed rates. The optimum conditions for maximum polymerisation was found at reaction temperature (RT) 75 °C, system pressure (SP) 25 bar, and 75% monomer concentration (MC). The hydrogen percentage was kept fixed at all times. The coefficient of correlation for reaction temperature, system pressure, and monomer concentration ratio, was found to be 0.932. Thus, the experimental results and model predicted values were a reliable fit at optimum process conditions. Detailed and adaptable CFD results were capable of giving a clear idea of the bed dynamics at optimum process conditions.

Polypropylene Production Optimization in Fluidized Bed Catalytic Reactor (FBCR): Statistical Modeling and Pilot Scale Experimental Validation

Propylene is one type of plastic that is widely used in our everyday life. This study focuses on the identification and justification of the optimum process parameters for polypropylene production in a novel pilot plant based fluidized bed reactor. This first-of-its-kind statistical modeling with experimental validation for the process parameters of polypropylene production was conducted by applying ANNOVA (Analysis of variance) method to Response Surface Methodology (RSM). Three important process variables i.e., reaction temperature, system pressure and hydrogen percentage were considered as the important input factors for the polypropylene production in the analysis performed. In order to examine the effect of process parameters and their interactions, the ANOVA method was utilized among a range of other statistical diagnostic tools such as the correlation between actual and predicted values, the residuals and predicted response, outlier t plot, 3D response surface and contour analysis plots. The statistical analysis showed that the proposed quadratic model had a good fit with the experimental results. At optimum conditions with temperature of 75°C, system pressure of 25 bar and hydrogen percentage of 2%, the highest polypropylene production obtained is 5.82% per pass. Hence it is concluded that the developed experimental design and proposed model can be successfully employed with over a 95% confidence level for optimum polypropylene production in a fluidized bed catalytic reactor (FBCR).