logo
CanyamAI summaries for academic research
language
MobileMobile
  • Phone FeatureWeChat Mini Program
EnglishEnglish
noticeNotice
BlogBlog
delete
arrow
Return

Eigenspectra for flocculation quality estimation

delete2020-07-11
delete0
PRE
AI
C
C. N. Veenstra
N
Neville Dubash
S
Scott Webster
W
Wayne A. Brown
A
Abu Junaid *
DOI：10.1002/aic.16539delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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.
Keywords:
eigenfaces
flocculation
image recognition
mixing
online measurement
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

AIChE Journal cover
AIChE Journal
IF:
4
Papers:
1.1W
Citations:
2.9W

Organization

No organization information available