Predictive modeling reforming process

The reforming process model is designed to predict the performance of many reactor configurations. The model can be run in four modes, combining adiabatic or isothermal reactors with recycle or single-pass (no recycle)... 

Naphtha catalytic reforming process Maximization of tiie aromatic yield and minimization of the yield of heavy aromatics. Neighborhood and Archived GA (NAGA) The process model is based on 20 lumps and 31 reactions. The frequency factors of 31 reactions were estimated by matching the predictions with the operating data. Hou et al. (2007)... 

No exemplary simulation results are presented here. Anyway, these would only be applicable for a certain MCFC system and under certain conditions, and they would not be representative for the broad range of available models. Nevertheless, MCFC models have been applied for various purposes Toshiba et al. [5] compared different flow configurations, Koh and Kang [10] predicted the impact of pressurized operation on fuel-cell performance, Park et al. [14] and Heidebrecht and Sundmacher [56] applied MCFC models to evaluate the effect of the reforming process on the fuel cell and to optimize it, and Bosio et al. [8] studied the application of nonuniform gas distributions with regard to the temperature distribution in MCFCs. 

Elnashaie and Abashar [34] developed a mathematical model to investigate the phenomena of diffusion and chemical reactions in porous catalyst pellets for steam reforming. The rigorous dusty gas model was compared to the simpler Wilke-Bosanquet model under the assumptions of steady-state, negligible viscous flow and isothermal conditions. It was found that at low steam to methane ratios the simplified diffusion model is adequate for simulating the reforming process, while at high steam to methane ratios the implementation of the dusty gas model is essential for accurate prediction of the behavior for this gas-solid system. 

Predictive Modeling of the Continuous Catalyst R eneration (CCR) Reforming Process... 

I 5 Predictive Modeling of the Continuous Catalyst Regeneration (CCR) Reforming Process Table 5.3 Behavior summary key reaction classes (adapted from [6, 7,8]). 

The second consideration is the model for the interstage heaters, product separators and compressors. In order to model these units meaningfully, we must have reasonable estimates for the key thermophysical properties of the lumps. In the case of the reformer, we must make reasonable prediction of reactant concentration (at system pressure), fC-values (for the product separator) and heat capacity (to correctly model the reactor temperature drop and product temperatures). The reforming process generally operates at temperatures and pressures where the ideal gas law applies for hydrocarbon species in the reactor section. Ancheyta-Juarez et al. [1, 2] use the ideal gas assumption to calculate the concentration of reactant species. In addition, they use the polynomial heat capacity correlations for pure components to approximate the heat capacity of the mixture. Work by Bommannan et al. [30] and Padmavathi et al. [31] uses a fixed value for the heat capacity and fC-value correlation to predict compositions in the primary product separator. 

Since we wish to use this model to model BTX production as well, we need to predict the composition of the all the relevant isomers of A8 (ethylbenzene, orthoxylene, para-xylene, meta-xylene). In our model, we assume that these isomers take on fixed equilibrium ratios as a function of temperature. Figure 5.10 shows the equilibrium distribution of these isomers at various temperatures [40,41]. The distributions correspond to expected temperatures in the reforming process. 

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