Review on Innovative Catalytic Reforming of Natural Gas to Syngas

Decreasing supplies of high quality crude oil and increasing demand for high quality distillates have motivated the interest in converting natural gas to liquid fuels, especially with the present boom in natural gas proven reserves. Nevertheless, one major issue is the curtailment of costs incurred in producing synthesis gas from natural gas, which account for approximately 60% of the costs used in producing liquid fuels. While there are three main routes to convert natural gas to syngas: steam reforming (SMR), partial Oxidation (POX) and auto-thermal reforming (ATR). Significant new developments and improvements in these technologies, established innovative processes to minimize greenhouse gases emission, minimize energy consumption, enhance syngas processes, adjust the desired H2/CO ratio and change the baseline economics. This article reviews the state of the art for the reforming of natural gas to synthesis gas taking into consideration all the new innovations in both processes and catalysis.

Response Surface Modeling of Fuel Rich and Fuel Lean Catalytic Combustion of the Stabilized Confined Turbulent Gaseous Diffusion Flames

The Response Surface Methodology (RSM) has been applied to explore the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/γAl2O3 and Pd/γAl2O3 disc burners were situated in the combustion domain and the experiments were performed under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (ø) of 0.75 and 0.25 respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners were inspected via measuring the mean temperature profiles in the radial direction at different discrete axial locations along the flames. The RSM considers the effect of the two operating parameters explicitly (r), the radial distance from the center line of the flame, and (x), axial distance along the flame over the disc, on the measured temperature of the flames and finds the predicted maximum temperature and the corresponding process variables. Also the RSM has been employed to elucidate such effects in the three and two dimensions and displays the location of the predicted maximum temperature.

Response Surface Methodology and Artificial Neural Network Methods Comparative Assessment for Fuel Rich and Fuel Lean Catalytic Combustion

Modeling, predictive and generalization capabilities of response surface methodology (RSM) and artificial neural network (ANN) have been performed to assess the thermal structure of the experimentally studied catalytic combustion of stabilized confined turbulent gaseous diffusion flames. The Pt/γAl2O3 and Pd/γAl2O3 disc burners were located in the combustion domain and the experiments were accomplished under both fuel-rich and fuel-lean conditions at a modified equivalence (fuel/air) ratio (ø) of 0.75 and 0.25, respectively. The thermal structure of these catalytic flames developed over the Pt and Pd disc burners was scrutinized via measuring the mean temperature profiles in the radial direction at different discrete axial locations along with the flames. The RSM and ANN methods investigated the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x), axial distance along with the flame over the disc, on the measured temperature of the flames and predicted the corresponding temperatures beside predicting the maximum temperature and the corresponding input process variables. A three-layered Feed Forward Neural Network was developed in conjugation with the hyperbolic tangent sigmoid (tansig) transfer function and an optimized topology of 2:10:1 (input neurons:hidden neurons:output neurons). Also the ANN method has been exploited to illustrate the effects of coded R and X input variables on the response in the three and two dimensions and to locate the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of & F_Ratio are 0.9181 - 0.9809 & 634.5 - 3528.8 for RSM method compared to 0.9857 - 0.9951 & 7636.4 - 24,028.4 for ANN method beside lower values for error analysis terms.

Comparative Appraisal of Response Surface Methodology and Artificial Neural Network Method for Stabilized Turbulent Confined Jet Diffusion Flames Using Bluff-Body Burners

The present study was conducted to present the comparative modeling, predictive and generalization abilities of response surface methodology (RSM) and artificial neural network (ANN) for the thermal structure of stabilized confined jet diffusion flames in the presence of different geometries of bluff-body burners. Two stabilizer disc burners tapered at 30° and 60° and another frustum cone of 60°/30° inclination angle were employed all having the same diameter of 80 (mm) acting as flame holders. The measured radial mean temperature profiles of the developed stabilized flames at different normalized axial distances (x/dj) were considered as the model example of the physical process. The RSM and ANN methods analyze the effect of the two operating parameters namely (r), the radial distance from the center line of the flame, and (x/dj) on the measured temperature of the flames, to find the predicted maximum temperature and the corresponding process variables. A three-layered Feed Forward Neural Network in conjugation with the hyperbolic tangent sigmoid (tansig) as transfer function and the optimized topology of 2:10:1 (input neurons: hidden neurons: output neurons) was developed. Also the ANN method has been employed to illustrate such effects in the three and two dimensions and shows the location of the predicted maximum temperature. The results indicated the superiority of ANN in the prediction capability as the ranges of R2 and F Ratio are 0.868 - 0.947 and 231.7 - 864.1 for RSM method compared to 0.964 - 0.987 and 2878.8 7580.7 for ANN method beside lower values for error analysis terms.

铂和HF掺杂的H-ZSM-5分子筛催化甲苯与甲醇烷基化反应(英文)

Toluene was alkylated with methanol in a flow type reactor at temperatures between 300 and 500 °C using H-ZSM-5 zeolite, 0.2%Pt/H-ZSM-5 and hydrofluorinated 0.2%Pt/H-ZSM-5 with HF concentrations of 1.0%, 2.0%, 3.0%, or 4.0%. Pt primarily enhances toluene conversion, total xylenes production, and p-xylene relative to its thermodynamic equilibrium. As the concentration of HF increases from 1.0% to 3.0%, the catalyst activity increases because of the increase in the number of acid sites and their strength. Additionally, the surface area and Pt dispersion also increases. An advantage of increased HF doping is that the formation of voluminous trimethylbenzene (TMB) byproducts is inhibited. However, at a HF concentration of 4.0%, Al and Si are partially leached and then deposited mostly in the wider catalytic pores. This was determined by evaluating the pore volume distribution and we determined that reactivity inhibition was ob-viously present and was due to diffusion restriction.