Kinetic Parameter Estimation of Ammonia Synthesis Using Hybrid Dynamic Global and Local Combined Particle Swarm Optimization

In this paper an industrial ammonia synthesis reactor has been modelled. The reactor under study is a fixed-bed reactor. The model is developed based on the fractional conversion of the nitrogen in the reaction. Calculation on material balance is then performed across the bed of iron catalysts befor...

Full description

Saved in:
Bibliographic Details
Main Author: Jun Yi, Thian
Format: Final Year Project
Language:English
Published: Universiti Teknologi PETRONAS 2013
Subjects:
Online Access:http://utpedia.utp.edu.my/8491/1/Thian%20Jun%20Yi_12884.pdf
http://utpedia.utp.edu.my/8491/
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper an industrial ammonia synthesis reactor has been modelled. The reactor under study is a fixed-bed reactor. The model is developed based on the fractional conversion of the nitrogen in the reaction. Calculation on material balance is then performed across the bed of iron catalysts before the model equation of the reaction is obtained in the form of an initial value problem. The second part of this project is the kinetic parameter estimation of the ammonia synthesis reaction. The expression for the rate of ammonia formation at pressures ranging from 150 atm. to 300 atm. derived here is the simplest yet available for a modern catalyst. It is suitable for design, optimization, and control studies, and is believed to be as accurate as the most complex expression in the composition, temperature, and pressure regions of commercial importance. The rate expression is based on the Temkin and Pyzhev expression corrected for high pressures and fitted to recently reported kinetic measurements of Nielsen, Kjaer, and Hansen for an industrially used catalyst. The pre-exponential factor of the rate expression is calculated using transitional state theory and thermo dynamical statistics. Activation energy of the reaction is the only kinetic parameter estimated through Hybrid Dynamic Global and Local Combined Particle Swarm Optimization method.