Thesis (Ph.D.)--McMaster University, 2004. / Supervisors: John F. MacGregor, Theodora Kourti. Includes bibliographical references (leaves 221-227). Also available via World Wide Web.

Thesis (Ph.D.) -- McMaster University, 1998. / Includes bibliographical references (leaves 169-173). Also available via World Wide Web.

Thesis (Ph.D.) -- McMaster University, 2000. / Includes bibliographical references (leaves 183-194). Also available via World Wide Web.

Thesis (Ph.D.)--McMaster University, 2003. / Advisor: John F. MacGregor. Includes bibliographical references (leaves 142-153). Also available via World Wide Web.

Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 1995. / Vita. Includes bibliographical references (leaves [243]-247).

Thesis (Ph. D.)--University of Texas at Austin, 2005. / Supervisor: S. Joe Qin. Vita. Includes bibliographical references.

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.

Thesis (Sc.D.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND SCIENCE. / Bibliography: leaves 412-417. / by Siri Jirapongphan. / Sc.D.

The operation of a chemical process plant has become more complex with the addition of process integration and intensification. A greater emphasis on producing goods with the lowest product variability in the safest manner possible and stringent environmental regulation limiting the quantity of effluent release have all put more constraints on the physical and economic performance of the chemical plant. The performance of a plant is quantified by the ability of the process system to achieve its objectives, which is governed by its process design and control. The conventional approach to process design and control selection starts sequentially by proposing a process flowsheet for the plant. The selection criteria for a flowsheet are normally based only on its environmental impact and economic merits. It is after a process flowsheet is deemed financially suitable that process control development commences. However, a more integrated approach to process design and control stage may thus lead to a plant that has better achievable performance. The aim of this project is to provide a new approach to quantitative dynamic controllability analysis for integration of process design and control by using the concept of passivity and passive systems. Passivity is an input/output property of processes. Passive processes are stable and minimum phase and therefore very easy to control. For a given process, its shortage of passivity, which reflects destabilizing effects of factors such as time delays and Right-Half Plane (RHP) zeros, can be used to indicate its controllability. The project focuses in developing the proposed controllability analysis by combining the idea of passivity and IMC invertibility, which is then formulated into an optimization problem that can be solved by either using Semi-Definite Programming or Non-Linear Optimization. The achievable performance of the plant is quantified in terms of the sensitivity function of the open-loop process. The selection of a process from four different heat-integrated distillation column schemes was used as a case study and the result had clearly shown that the passivity-based controllability analysis was able to select a process based on the plant achievable performance under the constraint of passivity and design parameters.