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Prediction of industrial catalysts deactivation rate using first principle model and operating data Ramalan kadar penyahaktifan mangkin industri menggunakan model prinsip pertama dan data operasi

Azarpour, A. and Alwi, S.R.W. (2017) Prediction of industrial catalysts deactivation rate using first principle model and operating data Ramalan kadar penyahaktifan mangkin industri menggunakan model prinsip pertama dan data operasi. Malaysian Journal of Analytical Sciences, 21 (1). pp. 204-212.

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Abstract

Catalyst deactivation is the loss of catalytic activity and/or selectivity over the course of time. Catalyst deactivation is a considerable and enduring problem in the operation of industrial catalytic processes. It is very costly in terms of catalyst replacement and process shutdown. The deactivation phenomenon not only affects the final product quality but also negatively influences the efficiency of the downstream processes. Therefore, a practical method which can accurately predict the deactivation rate can be a quite advantage to the industrial processes. In this paper, the deactivation rate of the industrial catalyst is predicted using operating data and catalyst specifications. The first principle model (FPM) is employed to predict the catalysts deactivation rate. The devised model is implemented into an industrial catalyst, which is palladium supported on carbon (Pd/C) utilized for the purification process of terephthalic acid, to show its applicability. The whole programs to obtain the rate of catalyst deactivation have been coded into Matlab R2013a environment. The model validated against industrial data. For the proposed catalyst, the catalyst sintering order is calculated with less that 3 percent error, and the pre-exponential values and the activation energy for the deactivation were calculated 0.00092 h-1 and 5279 J mol-1. Moreover, the catalyst is deactivated after around 360 days of operation. The methods, which are devised in this study, can be applied to any industrial catalyst to calculate the rate of deactivation. © 2017, Malaysian Society of Analytical Sciences. All rights reserved.

Item Type:Article
Impact Factor:cited By 0
Departments / MOR / COE:Division > Academic > Faculty of Engineering > Chemical Engineering
ID Code:19633
Deposited By: Ahmad Suhairi
Deposited On:20 Apr 2018 07:19
Last Modified:20 Apr 2018 07:19

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