Thesis (MScEng)--Stellenbosch University, 2002. / ENGLISH ABSTRACT: Flotation is notorious for its susceptibility to process upsets and consequently its poor
performance, making successful flotation control systems an elusive goal. The control of
industrial flotation plants is often based en the visual appearance of the froth phase, and
depends to a large extent on the experience and ability of a human operator. Machine
vision systems provide a novel solution to several of the problems encountered in
conventional flotation systems for monitoring and control.
The rapid development in computer VISIon, computational resources and artificial
intelligence and the integration of these technologies are creating new possibilities in the
design and implementation of commercial machine vision systems for the monitoring and
control of flotation plants. Current machine vision systems are available but not without
their shortcomings. These systems cannot deal with fine froths where the bubbles are
very small due to the segmentation techniques employed by them. These segmentation
techniques are cumbersome and computationally expensive making them slow in real
time operation.
The approach followed in this work uses neural networks to solve the problems
mentioned above. Neural networks are able to extract information from images of the
froth phase without regard to the type and structure of the froth. The parallel processing
capability of neural networks, ease of implementation and the advantages of supervised
or unsupervised training of neural networks make them potentially suited for real-time
industrial machine vision systems. In principle, neural network models can be
implemented in an adaptive manner, so that changes in the characteristics of processes
are taken into account.
This work documents the development of linear and non-linear principal component
models, which can be used in a real-time machine vision system for the monitoring, and
control of froth flotation systems. Features from froth images of flotation processes were extracted via linear and non-linear
principal component analysis. Conventional linear principal component analysis and
three layer autoassociative neural networks were used in the extraction of linear principal
components from froth images. Non-linear principal components were extracted from
froth images by a three and five layer autoassociative neural network, as well as localised
principal component analysis based on k-means clustering. Three principal components
were extracted for each image. The correlation coefficient was used as a measure of the
amount of variance captured by each principal component.
The principal components were used to classify the froth images. A probabilistic neural
network and a feedforward neural network classifier were developed for the classification
of the froth images. Multivariate statistical process control models were developed using
the linear and non-linear principal component models. Hotellings T2 statistic and the
squared prediction error based on linear and non-linear principal component models were
used in the development of multivariate control charts.
It was found that the first three features extracted with autoassociative neural networks
were able to capture more variance in froth images than conventional linear principal
components, the features extracted by the five layer autoassociative neural networks were
able to classify froth images more accurately than features extracted by conventional
linear principal component analysis and three layer autoassociative neural networks. As
applied, localised principal component analysis proved to be ineffective, owing to
difficulties with the clustering of the high dimensional image data. Finally the use of
multivariate statistical process control models to detect deviations from normal plant
operations are discussed and it is shown that Hotellings T2 and squared prediction error
control charts are able to clearly identify non-conforming plant behaviour. / AFRIKAANSE OPSOMMING: Flottasie is berug daarvoor dat dit vatbaar vir prosesversteurings is en daarom dikwels nie
na wense presteer nie. Suksesvolle flottasiebeheerstelsels bly steeds 'n ontwykende
doelwit. Die beheer van nywerheidsflottasie-aanlegte word dikwels gebaseer op die
visuele voorkoms van die skuimfase en hang tot 'n groot mate af van die ervaring en
vaardighede van die menslike operateur. Masjienvisiestelsels voorsien 'n vindingryke
oplossing tot verskeie van die probleme wat voorkom by konvensionele flottasiestelsels
ten opsigte van monitering en beheer.
Die vinnige ontwikkeling van rekenaarbeheerde visie, rekenaarverwante hulpbronne en
kunsmatige intelligensie, asook die integrasie van hierdie tegnologieë, skep nuwe
moontlikhede in die ontwerp en inwerkingstelling van kommersiële masjienvisiestelsels
om flottasie-aanlegte te monitor en te beheer. Huidige masjienvisiestelsels is wel
beskikbaar, maar is nie sonder tekortkominge nie. Hierdie stelsels kan nie fyn skuim
hanteer nie, waar die borreltjies baie klein is as gevolg van die segmentasietegnieke wat
hulle aanwend. Hierdie segmentasietegnieke is omslagtig en rekenaargesproke duur, wat
veroorsaak dat dit stadig in reële tyd-aanwendings is.
Die benadering wat in hierdie werk gevolg is, wend neurale netwerke aan om die
bovermelde probleme op te los. Neurale netwerke is instaat om inligting te onttrek uit
beelde van die skuimfase sonder om ag te slaan op die tipe en struktuur van die skuim.
Die parallelle prosesseringsvermoëns van neurale netwerke, die gemak van
implementering en die voordele van die opleiding van neurale netwerke met of sonder
toesig maak hulle potensieel nuttig as reële tydverwante industriële masjienvisiestelsels.
In beginsel kan neurale netwerke op 'n aanpassende wyse geïmplementeer word, sodat
veranderinge in die kenmerke van die prosesse deurlopend in aanmerking geneem word.
Kenmerke van die beelde van die skuim tydens die flottasieproses is verkry by
wyse van lineêre en nie-lineêre hootkomponentsanalise. Konvensionele lineêre hoofkomponentsanalise en drie-laag outo-assosiatiewe neurale netwerke is gebruik in die
onttrekking van lineêre hoofkomponente uit die beelde van die skuim. Nie-lineêre
hoofkomponente is uit die beelde van die skuim onttrek by wyse van 'n drie- en vyf-laag
outo-assosiatiewe neurale netwerk, asook deur 'n gelokaliseerde hoofkomponentsanalise
wat op k-gemiddelde trosanalise gebaseer is. Drie hoofkomponente is vir elke beeld
onttrek. Die korrelasiekoëffisiënt is gebruik as 'n maatstaf van die afwyking wat deur elke
hoofkomponent aangetoon is.
Die hoofkomponente is gebruik om die beelde van die skuim te klassifiseer. 'n
Probalistiese neurale netwerk en 'n voorwaarts voerende neurale netwerk is vir die
klassifisering van die beelde van die skuim ontwerp. Multiveranderlike statistiese
prosesbeheermodelle is ontwerp met die gebruik van die lineêre en nie-lineêre
hoofkomponentmodelle. Hotelling se T2 statistiek en die gekwadreerde voorspellingsfout,
gebaseer op lineêre en nie-lineêre hoofkomponentmodelle, is gebruik in die ontwikkeling
van multiveranderlike kontrolekaarte.
Dit is gevind dat die eerste drie eienskappe wat met behulp van die outo-assosiatiewe
neurale netwerke onttrek is, instaat was om meer variansie by beelde van skuim vas te
vang as konvensionele lineêre hoofkomponente. Die eienskappe wat deur die vyf-laag
outo-assosiatiewe neurale netwerke onttrek is, was instaat om beelde van skuim akkurater
te klassifiseer as daardie eienskappe wat by wyse van konvensionele lineêre
hoofkomponentanalalise en drie-laag outo-assosiatiewe neurale netwerke onttrek is. Soos
toegepas, het dit geblyk dat gelokaliseerde hoofkomponentsanalise nie effektief is nie, as
gevolg van die probleme rondom die trosanalise van die hoë-dimensionele beelddata.
Laastens word die aanwending van multiveranderlike statistiese prosesbeheermodelle,
om afwykings in normale aanlegoperasies op te spoor, bespreek. Dit word aangetoon dat
Hotelling se T2 statistiek en gekwadreerdevoorspellingsfoutbeheerkaarte instaat is om
afwykende aanlegwerksverrigting duidelik aan te dui.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/52945 |
| Date | 04 1900 |
| Creators | Kharva, Mohamed |
| Contributors | Aldrich, C., Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | English |
| Type | Thesis |
| Format | 212 p. : ill. |
| Rights | Stellenbosch University |
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