Survey of applications of identification in chemical and physical processes

Gustavsson, Ivar

doi:10.1016/0005-1098(75)90005-9

Cited by 64 publications

(19 citation statements)

References 33 publications

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“…A considerable amount of work in system identification has been devoted to the problem of experimental design (Astrom and Eykhoff, 1971;Soderstrom et a!., 1974;Gustavsson, 1975). Two interpretations of this problem are particularly relevant to our discussion since they demonstrate a number of desirable features of experimental design that are rarely possible in the collection of water quality data.…”

Section: Active Natural and Passive Experimentsmentioning

confidence: 99%

Uncertainty and Forecasting of Water Quality

1983

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Section: Active Natural and Passive Experimentsmentioning

confidence: 99%

Uncertainty and Forecasting of Water Quality

1983

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“…1, a prerequisite of system identification is an appropriate record of the observed process dynamics. Any a priori knowledge of the system's dynamic behavior is an advantage, since this knowledge can be used in assessing the following important aspects of experimental design (Gustavsson, 1975): (a) major process time constants; (b) sampling (measurement) frequency; (c) duration of the experiment; (d) choice of input test signals u; (e) noise levels ; (f) process nonlinearities. When confronted with a modeling problem it is thus not particularly encouraging to reflect upon the fact that a good experimental design, and hence the likelihood of useful results, is strongly dependent upon a good a priori knowledge (model) of the system!…”

Section: Experimental Designmentioning

confidence: 99%

Model Structure Identification From Experimental Data

Beck¹

1979

Theoretical Systems Ecology

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PREFACEIn recent years there has been considerable interest in the development of models for river and lake ecological systems. Much of this interest has been directed toward the development of progressively larger and more complex simulation models. In contrast, relatively little attention has been devoted to the problems of uncertainty and errors in the field data, of inadequate numbers of field data, of uncertainty in the relationships between the important system variables, and of uncertainty in the model parameter estimates. The International Institute for Applied Systems Analysis Resources and Environment Area's Task on Models for Environmental Quality Control and Management addresses problems such as these.The subject of this paper is model calibration. But rather than solving the customary problem of model parameter estimation, given an established structure for the model, the paper attempts to answer the prior question of identifying the dominant relationships between the system inputs, state variables, and output responses. That, then, is the problem that needs to be solved before one can consider how to estimate the model parameter values accurately. And it is a problem because, despite very many laboratory-scale experiments and a number of major field studies, our knowledge of the relationships between the mineral, organic, and microbiological components of an aquatic ecosystem is still quite uncertain.iii SUMMARY This paper is reprinted from the book Theoretical Systems Ecology: Advances and Case Studies, edited by E. Halfon of the Canada Centre for Inland Waters and published by Academic Press, New York. Acknowledging that systems ecology has had a large impact on all aspects of environmental research, the book aims to bridge the gap in communication between theoreticians, modelers, and field ecologists. Three classes of problems are treated in the book. They separate approximately into (a) how the model should be developed and analyzed prior to the collection and use of field data, (b) how the model should be developed or modified when it is evaluated against field data, and (c) the desired properties of the models for control and management purposes. The objective of this paper is to emphasize the fact that the second of these three problem categories is not simply a matter of straightforward model parameter estimation.The basic problem of model calibration -or system identification -is that information about the "externally" observed behavior of a system is required to be translated into information about the model-based description of the system's "internal" mechanisms of behavior. The measured input and output variables represent the system's external description, whereas state variables and parameter values refer to the system's internal description. In other words, during model calibration, and especially in the process of identifying the model's structure, we seek improved understanding of those physical, chemical, and biological phenomena that are thought to govern the system's observed be...

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“…Este intervalo de tempo deve ser pequeno o suficiente para que sejam coletados dados capazes de acompanhar a evolução temporal das variáveis do sistema, com o menor volume de dados possí-vel (GUSTAVSSON, 1975).…”

Section: Período De Amostragemunclassified

Desenvolvimento de sensor virtual empregando redes neurais para medição da composição em uma coluna de destilação.

Zanata¹

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Survey of applications of identification in chemical and physical processes

Cited by 64 publications

References 33 publications

Uncertainty and Forecasting of Water Quality

Uncertainty and Forecasting of Water Quality

Model Structure Identification From Experimental Data

Desenvolvimento de sensor virtual empregando redes neurais para medição da composição em uma coluna de destilação.

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