Hybrid Modeling of Modified Mathematical Model for Gas Phase Olefine in Fluidized Bed Catalyst Reactors Using Artificial Neural Networks

Abstract

The complex flow patterns induced in fluidized bed catalytic reactors and the competing parameters affecting the mass and heat transfer characteristics makes the design of such reactors a challenging task to accomplish. The models of such a process rely heavily on predictive empirical correlations for mass and heat transfer coefficients. Unfortunately, published empirical based correlations have the common shortcoming of low-prediction efficiency compared with experimental data. In this work, an artificial neural network approach is used to capture the reactor characteristics in terms of heat and mass transfer based on published experimental data. The developed ANN-based heat and mass transfer coefficients relations were used in a conventional FCR model and simulated under industrial operating conditions. The hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. This modeling approach can be used as an alternative to conventional modeling methods.