Coke deposition detection through the analysis of catalyst images

Abstract

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.

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

“…Orengo [15] proposed that the effective color features were the low moments of the colour components derived from a colour image. The first moment is the mean value of the image.…”

“…Some preliminary results of this method was reported at the 2015 IEEE International Conference on Imaging Systems & Techniques [15]. In this paper, we present in detail the image acquisition and processing system, extract the effective features from the images of the catalyst pellets and establish a model to predict the coke amount through soft computing.…”

“…On account of the limitations on all existing methods mentioned above, the aim of this paper is to propose a novel method to measure the amount of coke deposition on the catalysts through image analysis. Some preliminary results of this method were reported at the 2015 IEEE International Conference on Imaging Systems & Techniques [15]. In this paper, we present, in detail, the image acquisition and processing system, extract the effective features from the images of the catalyst pellets and establish a model to predict the coke amount through soft computing.…”