Thesis (MScIng)--University of Stellenbosch, 2004. / ENGLISH ABSTRACT: As result of the increasingly competitive performance in today’s industrial environment, it
has become necessary for production facilities to increase their efficiency. An essential step
towards increasing the efficiency of these production facilities is through tighter processes
control. Process control is a monitoring and modelling problem, and improvements in these
areas will also lead to better process control.
Given the difficulties of obtaining theoretical process models, it has become important to
identify models from process data. The irregular behaviour of many chemical processes,
which do not seem to be inherently stochastic, can be explained by analysing time series
data from these systems in terms of their nonlinear dynamics. Since the discovery of time
delay embedding for state space analysis of time series, a lot of time has been devoted to
the development of techniques to extract information through analysis of the geometrical
structure of the attractor underlying the time series. Nearly all of these techniques assume
that the dynamical process under question is stationary, i.e. the dynamics of the process did
not change during the observation period. The ability to detect dynamic changes in
processes, from process data, is crucial to the reliability of these state space techniques.
Detecting dynamic changes in processes is also important when using advanced control
systems. Process characteristics are always changing, so that model parameters have to be
recalibrated, models have to be updated and control settings have to be maintained. More
reliable detection of changes in processes will improve the performance and adaptability of
process models used in these control systems. This will lead to better automation and
enormous cost savings.
This work investigates and assesses techniques for detecting dynamical changes in
processes, from process data. These measures include the use of multilayer perceptron
(MLP) neural networks, nonlinear cross predictions and the correlation dimension statistic.The change detection techniques are evaluated by applying them to three case studies that
exhibit (possible) nonstationary behaviour.
From the research, it is evident that the performance of process models suffers when there
are nonstationarities in the data. This can serve as an indication of changes in the process
parameters. The nonlinear cross prediction algorithm gives a better indication of possible
nonstationarities in the process data; except for instances where the data series is very short.
Exploiting the correlation dimension statistic proved to be the most accurate method of
detecting dynamic changes. Apart from positively identifying nonstationary in each of the
case studies, it was also able to detect the parameter changes sooner than any other method
tested. The way in which this technique is applied, also makes it ideal for online detection
of dynamic changes in chemical processes. / AFRIKAANSE OPSOMMING: Dit is belangrik om produksie aanlegte so effektief moontlik te bedryf. Indien nie, staar
hulle die moontlikheid van finansiële ondergang in die gesig – veral as gevolg van
toenemende mededinging die industrie. Die effektiwiteit van produksie aanlegte kan
verhoog word deur verbeterde prosesbeheer. Prosesbeheer is ‘n moniterings en
modellerings probleem, en vooruitgang in hierdie areas sal noodwendig ook lei tot beter
prosesbeheer.
Omdat dit moeilik is om teoretiese proses modelle af te lei, word dit al hoe belangriker om
modelle vanuit proses data te identifiseer. Die ongewone optrede van baie chemiese
prosesse, wat nie inherent stogasties blyk te wees nie, kan meestal verklaar word deur
tydreeks data vanaf hierdie prosesse te analiseer in terme van hul nie-liniêre dinamika.
Sedert die ontdekking van tydreeksontvouing vir toestandveranderlike stelsels, is baie tyd
daaraan spandeer om tegnieke te ontwikkel wat inligting uit tydreekse kan onttrek deur die
onderliggende geometriese struktuur van die attraktor te bestudeer. Byna al hierdie tegnieke
aanvaar dat die dinamiese proses stationêr is, m.a.w dat die dinamika van die proses nie
verander het tydens die observasie periode nie. Die vermoë om hierdie dinamiese proses
veranderinge te kan identifiseer, is daarom baie belangrik.
Ook in gevorderde beheerstelsels is vroegtydige identifisering van dinamiese veranderinge
in prosesse belangrik. Proses karakteristieke is altyd besig om te verander, sodat model
parameters herkalibreer moet word, modelle opgedateer moet word en beheer setpunte
onderhou moet word. Meer betroubare tegnieke om veranderinge in prosesse te identifiseer
sal die aanpasbaarheid van proses modelle in hierdie beheerstelsels verbeter. Dit sal lei tot
beter outomatisering en sodoende lei tot enorme kostebesparings.
Hierdie werk ondersoek tegnieke om dinamiese veranderinge in prosesse te identifiseer,
deur die analise van proses data. Die tegnieke wat gebruik word sluit die volgende in:multilaag-perseptron neurale netwerke, nie-liniêre kruisvoorspelling statistieke en die
korrelasie dimensie statistiek. Die tegnieke is op drie gevallestudies toegepas om te sien of
hulle die dinamiese veranderinge in die data kan identifiseer.
Vanuit die navorsing is dit duidelik dat proses modelle nadelig beinvloed word deur niestationêre
data. Dit kan dien as ‘n indikasie van veranderinge in die proses parameters. Die
nie-liniêre kruisvoorspellings algoritme gee ‘n beter indikasie van dinamiese veranderinge
in die proses data, behalwe waar die tydreeks baie kort is. Toepassings van die korrelasie
dimensie statistiek gee die beste resultate. Hierdie tegniek kon dinamiese veranderinge
vinniger as enige ander tegniek identifiseer, en die manier waarop dit gebruik word maak
dit ideaal vir die identifisering van dinamiese veranderinge in chemiese prosesse.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/16258 |
| Date | 04 1900 |
| Creators | Bezuidenhout, Leon Christo |
| Contributors | Aldrich, C., University of Stellenbosch. Faculty of Engineering. Dept. of Process Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | xiii, 1 v. (various foliations) : ill. |
| Rights | University of Stellenbosch |
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