Modeling of naphtha reforming unit applying detailed description of kinetic in continuous catalytic regeneration process

Thermal Integration of Sulfuric Acid and Continuous Catalyst Regeneration of Naphtha Reforming Plants

“…In fact, the researchers tried to find the best reaction network for the naphtha reforming process that can justify the observed data and can be used for the prediction of results from different reactor configurations. One of the most accurate, comprehensive reaction networks was proposed by Iranshahi et al , in which a lumped reaction network based on 32 pseudo‐components and 84 reactions was suggested and the results were satisfactory. Indeed, one of its outstanding benefits is the fact that 8‐carbon aromatics are not taken into account as one lumped component; in other words, different isomers of 8‐carbon aromatics, including para ‐xylene, meta ‐xylene, ortho ‐xylene, and ethylbenzene, are considered individually.…”

“…In 1959, Smith provided the first model for the naphtha reforming process, in which he considered three compounds: paraffins, naphthenes, and aromatics (PNA), involving four reactions. Afterwards, much attention was paid to find a reliable kinetic model for the naphtha reforming process , among which the reaction network proposed by Iranshahi et al resulted in reliable, accurate predictions of reactor performance. Their kinetic model is based on 32 lumped components and 84 reactions.…”

Non Monotonous Product Distribution Dependence on Pt/γ‐Al2O3−Cl Catalysts Formulation in n‐Heptane Reforming.

“…These transformations preferably occur through bi‐functional mechanisms enabling proton and hydrogen transfers . The choice of the fresh catalyst formulation as well as its regeneration conditions are paramount process operating issues and depend on the targeted aromatics and isoparaffins selectivity . There is therefore a strong motivation for methods enabling the prediction of active phase formulation impact on catalyst performance.…”

A hybrid statistical approach for modeling and optimization of RON: A comparative study and combined application of response surface methodology (RSM) and artificial neural network (ANN) based on design of experiment (DOE)

“…Since the octane number measures the maximum compression ratio in engines, there has been a consistent increase in the demand for more efficient fuel of high octane number which contributes to the improve of engine antiknock property and improve the combustion M a n u s c r i p t 5 quality to take place perfectly [3,4]. However, the need to use of higher compression ratios in engines [5] and therefore production of motor fuel with an even greater octane number requires either severe process conditions such as temperature, pressure and space velocity or the use of novel effective catalysts that able to reform straight-long-chain alkanes into branched and cycled hydrocarbons with higher RON.…”