Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors

Mjalli, Farouq Sabri and Al-Mfargi, A. (2010) Neural network-based heat and mass transfer coefficients for the hybrid modeling of fluidized reactors. Chemical Engineering Communications, 197 (3). pp. 318-342. ISSN 0098-6445, DOI

Full text not available from this repository.
Official URL:


The complex flow patterns induced in fluidized bed catalytic reactors and the competing parameters affecting the mass and heat transfer characteristics make the design of such reactors a challenging task to accomplish. The models of such processes rely heavily on predictive empirical correlations for the 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.

Item Type: Article
Uncontrolled Keywords: Catalytic reactor; Fluidized bed reactor; Heat transfer; Mass transfer; Neural networks; Three phase
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Engineering
Depositing User: MR Faizal II H
Date Deposited: 28 Dec 2015 12:44
Last Modified: 09 May 2019 04:44

Actions (login required)

View Item View Item