Coke deposition detection through the analysis of catalyst images

Coke deposition on catalyst will not only reduce catalytic activity and selectivity, but also affect the product yield, the reaction residence time, the regenerator temperature and so on. As a result, it is necessary to measure the amount of coke deposition on catalyst. This paper proposes a new method based on image analysis. An image acquisition system consisting of a flatbed scanner and an opaque cover is used to obtain catalyst images. After imaging processing and analysis, the gray layer is selected to be the most effective colour layer for colour features extraction based on a discriminability index, D. Eight colour features (mean, variance, skewness, entropy, energy, H, S, V) are extracted from images with a good ability to classify the catalysts with different coke amount. Furthermore, the results show that there is a significant linear correlation between the H value and amount of coke deposition on catalyst, which could reflect the coke deposited state and coking progress effectively.