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Predicting the amount of coke deposition on catalyst through image analysis and soft computing

Zhang, Jingqiong, Zhang, Wenbiao, He, Yuting, Yan, Yong (2016) Predicting the amount of coke deposition on catalyst through image analysis and soft computing. Measurement Science & Technology, 27 (11). ISSN 0957-0233. (doi:10.1088/0957-0233/27/11/114006) (KAR id:55605)

Abstract

The amount of coke deposition on catalyst pellets is one of the most important indexes of catalytic property and service life. As a result, it is essential to measure this and analyze the active state of the catalysts during a continuous production process. This paper proposes a new method to predict the amount of coke deposition on catalyst pellets based on image analysis and soft computing. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and feature extraction, twelve effective features are selected and two best feature sets are determined by the prediction tests. A neural network optimized by a particle swarm optimization algorithm is used to establish the prediction model of the coke amount based on various datasets. The root mean square error of the prediction values are all below 0.021 and the coefficient of determination R 2, for the model, are all above 78.71%. Therefore, a feasible, effective and precise method is demonstrated, which may be applied to realize the real-time measurement of coke deposition based on on-line sampling and fast image analysis.

Item Type: Article
DOI/Identification number: 10.1088/0957-0233/27/11/114006
Subjects: T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Tina Thompson
Date Deposited: 26 May 2016 08:09 UTC
Last Modified: 08 Dec 2022 21:26 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/55605 (The current URI for this page, for reference purposes)

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