Affiliation:
1. Chemical Engineering Department, Urmia University, Urmia,Iran
2. Department of Civil Engineering, University of Bojnord, Bojnord,Iran
Abstract
Objectives:
A four-lump dynamic kinetic model on the hierarchical SAPO-34 catalyst in
the methanol to light olefins (MTO) process has been presented using the power law models. Since
decreased catalyst activity in the MTO process is common, for the applicability of the proposed
model, the function of catalyst activity was computed as a function of the coke percentage
deposited on the catalyst.
Materials and Methods:
The reactant and products were divided into four lumps, including
methanol and dimethyl ether (DME), light olefins (ethylene and propylene), light paraffin
(methane, ethane, and propane) and heavier hydrocarbons from C4. The one-dimensional ideal plug
reactor was used for the simulation of the MTO reactor. The kinetic parameters and the catalyst
activity function were predicted using the particle swarm optimization (PSO) algorithm.
Results:
The comparison of product distribution in the experimental model and the results of the
kinetic model indicated the high accuracy of the presented model. The effect of operational
parameters such as temperature and weight hourly space velocity (WHSV) on the mole percent of
light olefins was investigated using the proposed kinetic model. The optimized value of
temperature and WHSV to reach the maximum yield of light olefins was respectively 460 ˚C and
4.2 h-1.
Conclusion:
The passive kinetic coefficients were estimated in the reaction rate constant and
catalyst activity function with the help of the PSO optimization algorithm. The mole fraction of
different products and the reactant arising from modeling at the reactor outlet was compared with
experimental results, which indicated the high accuracy of the presented kinetic model. The results
also revealed that the selection of high and low temperatures and WHSV decreases the yield of
light olefins and the lifetime of the catalyst.
Publisher
Bentham Science Publishers Ltd.
Subject
Organic Chemistry,Computer Science Applications,Drug Discovery,General Medicine