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An investigation of the oxidative dehydrogenation of propane kinetics over a vanadium-graphene catalyst aiming at minimizing of the COx species

Fattahi, M ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1016/j.cej.2014.04.002
  3. Abstract:
  4. Application of the DOE with the ANN in kinetic study of the ODHP was investigated.•The catalyst of vanadium/graphene synthesized through the hydrothermal technique.•The ANN and RSM's simulations were utilized to generate the extra data points.•Power law models and corresponding parameters determined to describe the reactions.•The optimization conducted in order to minimize the COx production. In the current investigation, an application of the design of experiments (DOE) along with the artificial neural networks (ANN) in a kinetic study of oxidative dehydrogenation of propane (ODHP) reaction over a synthesized vanadium-graphene catalyst at 400-500. °C presented aiming at minimizing the CO. x production. In this venue, the main and side reactions' unknown and variable reaction kinetics network expressed through the power law equations and determined via non-linear regression analysis. The collected kinetic experimental data attributed to three operating factors including the temperature, feed molar ratio and total feed flowrate. The neural network-based optimum was compared with that of the experimental data. In addition, the predictions of the response surface methodology (RSM) and ANN models based upon the DOE information utilized to generate extra simulated data for further analysis. Then different data sets used to fit the power law kinetic rates for the main ODHP and side reactions. Propane to air ratio was found to be a critical parameter for optimization of the ODHP reaction. Distribution of the propane consumption rate as well as the propylene and ethylene formation rates to that of the CO. x investigated and optimizations were performed. It was revealed that, based on different number of data points utilized over the vanadium-graphene catalyst these ratios were higher than unity confirming that, the CO. x production minimized. Moreover, under such conditions, the ratio of ethylene to CO. x production rate was noticeably below unity indicating the CO. x formation was higher than that of the ethylene. Kinetic modeling results incorporating simulated data from the ANN and RSM models compared with those obtained from the experimental ones resulted in less than 10% deviations. Considering the complexity of the undertaken system, this comparison was rather satisfactory
  5. Keywords:
  6. Artificial neural network ; DOE ; Kinetic modeling ; Non-linear regression ; Oxidative dehydrogenation of propane ; RSM ; Catalysts ; Complex networks ; Computer simulation ; Dehydrogenation ; Design of experiments ; Ethylene ; Graphene ; Kinetic theory ; Neural networks ; Optimization ; Propane ; Propylene ; Reaction kinetics ; Regression analysis ; Vanadium ; Oxidative dehydrogenation of propanes ; Kinetics
  7. Source: Chemical Engineering Journal ; Vol. 250 , 2014 , Pages 14-24 ; ISSN: 13858947
  8. URL: http://www.sciencedirect.com/science/article/pii/S1385894714004227