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Based on a slit model, a pellet scale model has been developed for calculation of drag force imposed on trilobe catalyst particles in a packed bed reactor. The drag coefficient for single gas phase flow in a porous media has been calculated by CFD simulation and the results compared to the Ergun equation. The results show that the drag coefficient predicted by Ergun equation should be modified for various bed porosities, particle aspect ratio and gas densities. Therefore, a correction factor has been proposed to correct the Ergun equation constants in various conditions for trilobe particles. Comparison between the proposed corrected Ergun equation results and experimental data indicates considerable agreement.

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In this study, a hybrid model is presented for estimating the activity of a commercial Pt/zeolite catalyst in an industrial‐scale light naphtha isomerization unit. This model is also capable of predicting the research octane number (RON) and Reid vapor pressure (RVP) of gasoline, the flow rate of product, and the temperature profile of the reactor. In the proposed model, the decay function of heterogeneous catalysts is combined with a recurrent‐layer artificial neural network. To identify the activity of catalyst, a set of 52 data points during 272 days were gathered from the target isomerization plant. From these data, 31 points were selected for training (60 %), 11 data points for testing (20 %) and the remained ones for validating the developed hybrid network (20 %). Results show that the presented hybrid model can acceptably estimate the activity of the catalyst during its life in consideration of all process variables. Moreover, it is confirmed that for unseen (validating) data, the proposed model is capable of predicting RON, RVP, flow rate of gasoline, and the temperatures of the middle and end sections of the isomerization reactor with an average absolute deviations (AAD %) of 0.0769 %, 0.118 %, 0.6945 %, 0.042 %, and 0.1137 %, respectively.

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