Self-tuning regulator applied to a binary distillation column

Sastry, V. A.; Seborg, Dale E.; Wood, R. K.

doi:10.1016/0005-1098(77)90026-7

Cited by 65 publications

(9 citation statements)

References 10 publications

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“…A detailed treatment of the time varying situation, with generalisations, is given by Bohlin.17 Industrial applications of Model 1 are appearing regularly in the literature. 18,19 It is salutary to note that identification using the structure of Model 1 need not necessarily be as sophisticated as the above development might imply. When only a single time constant and perhaps a time delay are involved, visual fit of an exponential to a step function response is all that is necessary for identification.…”

Section: Multiple-input Single-output (Miso) Modellingmentioning

confidence: 97%

Practical aspects of the identification of process dynamics

Godfrey

Brown

1979

Transactions of the Institute of Measurement and Control

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The steps to be followed in the identification of process dynamics are developed logically. These steps cover experiment design, the use of physical laws and a priori knowledge, model structure determination, parameter estimation and model validation. The presentation is directed toward guiding the control engineer in industry to the intelligent use of the copious, well-documented supply of theory and case studies. Emphasis is laid upon techniques which have proven practical value. I ntroductionFollowing Box and Jenkinsl and Mehra,2 the steps in system identification are as shown schematically in Fig 1, which is self-explanatory. A first step is often to install measurement transducers and put data logging equipment on to the plant. Thereby valuable information is obtained on normal plant operation under steady-state and transient conditions. Often identification stops at this point in industrial applications. A typical example is in the plotting of engine performance maps for diesel and internal combustion engines. Such maps give a static relation between mean output torque, mean piston speed and brake specific fuel consumption. From a knowledge of the load-speed duty cycle, perhaps expressed in joint probabilities of various loads and speeds, the mean fuel consumption rate can be calculated, and hence gearbox design/control strategies can be engineered to minimise fuel consumption. This is a problem where a deterministic static model is adequate, and is characteristic of many industrial control problems.Not infrequently, the dynamics of the system cannot be totally neglected. For example, in start-up and shut-down of plant, the sequencing controls may have to take account of time delays in long pipelines and thermal inertias in heat processes. Therefore, as a further step in identification, simple test inputs may be applied, such as step, pulse or sinewave functions, to give information on time delays, gains and resonances. This brings in classical control theory, where information on frequency response characteristics may be used to design a single-loop compensator.The routine uses of identification in the process industries are largely as already outlined, and represent superficial application of the identification format indicated in Fig 1. The use of statistical techniques (correlation analysis, spectral analysis and maximum likelihood methods) in process identification,3 despite wide reporting in the literature, is not routine, although there are specialist applications, for example of spectral analysis in vibration and fatigue testing of jet engines and vehicle structures. However, the situation is changing dramatically. Three innovative forces are acting simultaneously to cause this:(1) The new-generation control engineers are well versed in modem control theory and the use of computers and are anxious to apply their knowledge.(2) Low-cost microprocessor technology is enabling data 4 to be collected more easily and in pre-processed form.4 In addition, the advent of effective time sharing systems with che...

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Section: Multiple-input Single-output (Miso) Modellingmentioning

confidence: 97%

Practical aspects of the identification of process dynamics

Godfrey

Brown

1979

Transactions of the Institute of Measurement and Control

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“…In , the SISO version of the algorithm has been applied to control the temperature in a catalytic reactor, including a detailed experimental study of the effect of parameter ρ . Other application examples include the control of a binary distillation column , cement raw material blending , pH neutralization , a phosphate drying process , domestic stored‐energy heating systems , and bottom temperature control of a glass furnace . Application to two‐dimensional processes, such as paper production, is addressed in .…”

Section: Background To Optimized Adaptive Controlmentioning

confidence: 99%

Survey of industrial optimized adaptive control

Martín‐Sánchez

Lemos

Rodellar³

2012

Adaptive Control & Signal

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This survey paper aims, to present in a systematic way, the 'state-of-the-art' of a class of so called optimized adaptive control methodologies, where adaptive systems theory is complemented by optimal control theory. It is based on literature published since 1958 and emphasizes results with proven industrial relevance. After recalling previous adaptive systems, results that had prepared the ground for the new ideas, the analysis departs from the work on adaptive predictive control in the mid 1970s, which provided the conceptual basis for the development of this kind of technique. Well-known design methods, both from the stability and optimization perspectives, along with their results, are interpreted in an intuitive way in order to understand how control law and the adaptive mechanism complement each other to produce mature controllers that can reliably solve the industrial process dynamic stabilization problem. Surveys of industrial applications from both design perspectives are presented and industrial products based on optimized adaptive control are cited.

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“…The 22.8 cm diameter, 8-plate, pilot-scale distillation column used in this study has been used in a number of previous control studies [10,11]. Each plate at a spacing of 30.5 cm is fitted with four bubble caps.…”

Section: Distillation-column Equipmentmentioning

confidence: 99%

Robust multivariable control of a binary distillation column

Coppus

Shah

Wood

1983

IEE Proc. D Control Theory Appl. UK

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Davison's robust, multivariable, feedback-feedforward controller (DAVISON, E.J. : IEEE Trans., 1976, AC-21, pp. 35-47) is applied to the control of a pilot-scale binary distillation column, as a master loop in a cascaded configuration. The inner or slave loop consists of the original plant with multiloop proportional control. This cascaded configuration is shown to have a number of advantages for practical implementation. The performance of the controller is evaluated by experimental application to a computer-controlled distillation column. The robust controller gives improved control performance compared to that achieved using conventional multiloop proportional plus integral plus derivative (PID) control.

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Self-tuning regulator applied to a binary distillation column

Cited by 65 publications

References 10 publications

Practical aspects of the identification of process dynamics

Practical aspects of the identification of process dynamics

Survey of industrial optimized adaptive control

Robust multivariable control of a binary distillation column

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