Eigenspectra for flocculation quality estimation

We present an image analysis algorithm for flocculation quality estimation in high‐solids slurries, and demonstrate its performance using inline process images of oil sands tailings flocculation. While a skilled human operator can often successfully evaluate such images, variations in feed as well a...

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Bibliographic Details
Published in:AIChE journal 2020-09, Vol.66 (9), p.n/a
Main Authors: Veenstra, Christian N., Dubash, Neville, Webster, Scott E., Brown, Wayne A., Junaid, Abu S. M.
Format: Article
Language:English
Subjects:
Algorithms
eigenfaces
Flocculation
Image analysis
Image processing
Image quality
image recognition
Mine tailings
mixing
Object recognition
Oil sands
online measurement
Slurries
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Summary:We present an image analysis algorithm for flocculation quality estimation in high‐solids slurries, and demonstrate its performance using inline process images of oil sands tailings flocculation. While a skilled human operator can often successfully evaluate such images, variations in feed as well as the lack of isolated flocs or spatial reference‐points inherent in a high‐solids slurry can cause conventional image analysis techniques to fail. We overcome these challenges by recasting the images in Fourier space, discarding phase information, and applying an eigenfaces‐inspired image recognition algorithm to the resulting spectra. Each image is represented using a few projection coefficients onto an orthogonal basis and evaluated using likelihood‐based classification schemes. This algorithm shows a high degree of success evaluating the flocculation quality of 129 batch and inline flocculation experiments (5,610 images total) utilizing feed tailings from two different oil sand producers at a variety of feed dilutions and flocculant dosing levels.
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.16539