Model-Based Quality, Exergy, and Economic Analysis of Fluidized Bed Membrane Reactors

In petroleum refineries, naphtha reforming units produce reformate streams and as a by-product, hydrogen (H2). Naphtha reforming units traditionally deployed are designed as packed bed reactors (PBR). However, they are restrained by a high-pressure drop, diffusion limitations in the catalyst, and radial and axial gradients of temperature and concentration. A new design using the fluidized bed reactor (FBR) surpasses the issues of the PBR, whereby the incorporation of the membrane can improve the yield of products by selectively removing hydrogen from the reaction side. In this work, a sequential modular simulation (SMS) approach is adopted to simulate the hydrodynamics of a fluidized bed membrane reactor (FBMR) for catalytic reforming of naphtha in Aspen Plus. The reformer reactor is divided into five sections of plug flow reactors and a continuous stirrer tank reactor with the membrane module to simulate the overall FBMR process. Similarly, a fluidized bed reactor (FBR), without membrane permeation phenomenon, is also modelled in the Aspen Plus environment for a comparative study with FBMR. In FBMR, the continuous elimination of permeated hydrogen enhanced the production of aromatics compound in the reformate stream. Moreover, the exergy and economic analyses were carried out for both FBR and FBMR.

Comparison between Artificial Neural Network and Rigorous Mathematical Model in Simulation of Industrial Heavy Naphtha Reforming Process

In this study, an artificial neural network (ANN) model was developed and compared with a rigorous mathematical model (RMM) to estimate the performance of an industrial heavy naphtha reforming process. The ANN model, represented by a multilayer feed forward neural network (MFFNN), had (36-10-10-10-34) topology, while the RMM involved solving 34 ordinary differential equations (ODEs) (32 mass balance, 1 heat balance and 1 momentum balance) to predict compositions, temperature, and pressure distributions within the reforming process. All computations and predictions were performed using MATLAB® software version 2015a. The ANN topology had minimum MSE when the number of hidden layers, number of neurons in the hidden layer, and the number of training epochs were 3, 10, and 100,000, respectively. Extensive error analysis between the experimental data and the predicted values were conducted using the following error functions: coefficient of determination (R2), mean absolute error (MAE), mean relative error (MRE), and mean square error (MSE). The results revealed that the ANN (R2 = 0.9403, MAE = 0.0062) simulated the industrial heavy naphtha reforming process slightly better than the rigorous mathematical model (R2 = 0.9318, MAE = 0.007). Moreover, the computational time was obviously reduced from 120 s for the RMM to 18.3 s for the ANN. However, one disadvantage of the ANN model is that it cannot be used to predict the process performance in the internal points of reactors, while the RMM predicted the internal temperatures, pressures and weight fractions very well.

Mechanistic modeling and numerical simulation of axial flow catalytic reactor for naphtha reforming unit

Naphtha catalytic reforming (NCR) process has been of tremendous attention all over the world owing to the significant requirement for high-quality gasoline. Industrialized naphtha reforming unit at oil refineries applies a series of fixed bed reactors (FBRs) to improve the quality of the low-octane hydrocarbons and convert them to more valuable products. The prominent purpose of this research is to understand the catalytic reactor of naphtha reforming unit. For this aim, an appropriate mechanistic modeling and its related CFD-based computational simulation is presented to predict the behavior of the system when the reactors are of the axial flow type. Also, the triangular meshing technique (TMT) is performed in this paper due to its brilliant ability to analyze the results of model’s predictions along with improving the computational accuracy. Additionally, mesh independence analysis is done to find the optimum number of meshes needed for reaching the results convergence. Moreover, suitable kinetic and thermodynamic equations are derived based on Smith model to describe the NCR process. The results proved that the proceeding of NCR process inside the reactor significantly increased the concentration amount of aromatic materials, lighter ends and hydrogen, while deteriorated the concentration amount of naphthene and paraffin. Moreover, the pressure drop along the reactor length was achieved very low, which can be considered as one of the momentous advantages of NCR process.