Detecting change in nonlinear dynamic process systems

Bezuidenhout, Leon Christo (2004-04)

Thesis (MScIng)--University of Stellenbosch, 2004.

Thesis

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.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/16258
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