A cautious self‐tuning controller for chemical processes

Papadoulis, Apostolos V.; Tsiligiannis, Christos A.; Svoronos, Spyros A.

doi:10.1002/aic.690330306

Cited by 18 publications

(8 citation statements)

References 14 publications

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“…In a more compact notation, (17) becomes where ψ is a vector of functions of pH obtained from either Table 1 or Table 2 and θ is a vector of s and c i parameters. The vector of parameters can then be estimated on-line using a standard linear identification algorithm (e.g., Ljung and Soderstrom, 1983;Papadoulis et al, 1987). V dX dt ) u -FX (1) Once an estimate for the vector of parameters θ is obtained, T(pH) is calculated from Calculation of the strong acid equivalent from (5) and a PI controller in terms of the strong acid equivalent (6) complete the controller.…”

Section: On-line Identification Of the Nonlinearity And The Complete ...mentioning

confidence: 99%

On-Line Identification and Nonlinear Control of pH Processes

Wright

Smith

Kravaris

1998

Ind. Eng. Chem. Res.

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The control of pH is widely recognized as a difficult problem. A novel method for representing the nonlinearity of pH processes is developed. A system consisting of a combination of known and unknown chemical species is viewed as a combination of impulses (for the known species) and a piecewise continuous distribution (for the unknown species). This results in on-line identification of a small number of parameters that can be used to affect the process nonlinearity over a wider pH range than if only impulses were used. Computer simulations and experimental results using the proposed method for large step changes in concentrations of a simple chemical system demonstrate the performance of this approach. In particular, the simulations and experiments provide comparisons between the control of systems with and without identification and investigate the tuning of the controller and identification algorithm.

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Section: On-line Identification Of the Nonlinearity And The Complete ...mentioning

confidence: 99%

On-Line Identification and Nonlinear Control of pH Processes

Wright

Smith

Kravaris

1998

Ind. Eng. Chem. Res.

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“…Note that hi(t) = 0 only for TJ = 0. The measure of plant/model mismatch used here is the same as the one introduced in Papadoulis et al (1987). It is related to the output prediction error, u ( t ) , by…”

Section: Q ( T ) = @(T ) I O ( T ) H ( T ) [ I -H ( T ) ] -' Q(t) Is mentioning

confidence: 99%

“…23, ~( t -1) is substituted by a large value, y, at sampling instants of set-point changes (since it is known that the locally valid parameters will change). Note that the method presented in Papadoulis et al (1987) for tuning the weight, h ( t ) [SISO equivalent to h i ( t ) ] , is based on the minimization of control law sensitivity to the prediction error. In the present paper, hi(?)…”

Section: Q ( T ) = @(T ) I O ( T ) H ( T ) [ I -H ( T ) ] -' Q(t) Is mentioning

confidence: 99%

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A MIMO cautious self‐tuning controller

Papadoulis

Svoronos

1989

AIChE Journal

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IntroductionAdaptive controllers are generally based on linear models. Most chemical processes, however, are inherently nonlinear. In addition, extra nonlinearities are introduced by nonlinear sensors and control valves. The existing linear-model-based, adaptive algorithms face serious problems when applied to highly nonlinear systems. One might observe large sustained oscillations, failure of the algorithm to drive the system to the setpoint, and even instability. Such incidents have been reported by Song et al. (1984), Gustafsson (1984). for the minimum variance self-tuning regulator (STR).Self-tuning controllers (Clarke and Gawthrop, 1975) involving input penalization have been proved to be more robust than the STR (Gawthrop and Lim, 1982) and are perhaps the most industrially accepted adaptive controllers (Dumont, 1986). The major drawback these controllers face is the requirement to know, apriori, weights that result in a closed-loop stable system. For open-loop stable systems, stability and robustness can be achieved with heavy penalization of the control variable, but this results in sluggish performance. Pole/zero placement adaptive controllers with fixed poles and fast performance specifications are faced with similar problems (Papadoulis, 1987). Another alternative is the approach taken by generalized predictive control (GPC) algorithms (e.g., Ydstie and Liu, 1984; Ydstie et al., 1985; Clarke et al., 1987a, 198713). Robustness is enhanced by aiming to reach the set points (or achieve another performance measure) at a time (horizon) greater than the minimum dictated by time delays and zeroes outside the unit circle. With constant horizons, this approach has the disadvantage that large horizons might be necessary to ensure robustness, thus leading to sluggish performance. To circumvent these problems, algorithms involving input penalization with time-varying weights automatically adjusted on-line, have been presented. Allidina and Hughs (1 980) calculate the weights on-line as the solution to pole placement related, Diophantine equations. Latawiec and Chyra (1 983) utilize online stability criteria to adjust the weights. Toivonen (1983) interprets the input weight as the Langrange multiplier of a control variance constrained optimal control problem. These algorithms (all developed for SISO systems) require a large amount of on-line computations which may become prohibitive in the multivariable case.A different approach for some nonlinear systems is the use of nonlinear adaptive controllers. Promising algorithms have been proposed by Gustafsson (1984), Golden and Ydstie (1985), and Agarwal and Seborg (1987). But these controllers require that the nonlinearities be known apriori and that they be static, conditions seldom fulfilled in practice.An alternative method for SISO systems that maintains the essential simplicity and small computational requirements of the STR has been presented by Papadoulis et al. (1987). Namely, the CSTC is a simple STC whose weight is tuned on-line, based on a measure of the ...

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“…Η δομή του μοντέλου είναι πολύ σημαντική και πρέπει να επιλεγεί με προσοχή. (Papadoulis et al, 1987) :…”

Section: προσαρμοσμένη βελτιστοποίησηunclassified

Βιολογική Απομάκρυνση Αζώτου Και Προσαρμοζόμενη Βελτιστοποίηση Της Νιτροποίησης Σε Αντιδραστήρα Διαλείποντος Έργου Με Περιοδική Λειτουργία

Κατσογιάννης¹

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Nitrogen removal from water, either potable or domestic and industrial wastewater, has become mandatory for the protection of the environment and public health. Biological nitrogen removal is achieved by the combination of two processes: nitrification and denitrification. Nitrification consists in the sequential oxidation of ammonium-nitrogen to nitrite and finally to nitrate-nitrogen and is catalyzed by autotrophic microorganisms under aerobic conditions. Denitrification, on the other hand, consists in the reduction of produced nitrate-nitrogen finally to nitrogen gas under anoxic conditions and is catalyzed by heterotrophic, facultative aerobic microorganisms.In the recent years, considerable attention has been paid to the usage of a treatment system called Sequencing Batch Reactor (SBR), instead of the typical continuous flow system. SBR system operation consists in wastewater treatment through a sequence of discrete process periods in a single basin.Significant research effort has been spent in finding alternative carbon sources for denitrification and bypassing the nitratification process. Both goals are attractive from an economic point of view due to the minimization of the operational cost of the process.The present work aims to the study of the performance of an SBR for nitrogen removal by using, endogenous carbon sources for denitrification and operational strategies that promote nitratification bypass. The experimental work showed that nitrogen removal is achievable with satisfactory performance using operational strategy with a small aerobic:anoxic phase duration ratio of 1:3. Moreover, implementation of multiple aerobic/anoxic pairs with the same aerobic:anoxic phase ratio leads to nitratification bypass, giving excellent performance in terms of nitrogen removal.Aiming towards minimization of the operational cost (that of aeration) while ensuring acceptable effluent quality (ammonium concentration under a limit level), an adaptive optimization algorithm was implemented to a nitrifying SBR. The ability of the algorithm to handle disturbances in the influent ammonium-nitrogen concentration, leading the system to its new optimal operation point after the introduction of each disturbance, is demonstrated.

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A cautious self‐tuning controller for chemical processes

Cited by 18 publications

References 14 publications

On-Line Identification and Nonlinear Control of pH Processes

On-Line Identification and Nonlinear Control of pH Processes

A MIMO cautious self‐tuning controller

Βιολογική Απομάκρυνση Αζώτου Και Προσαρμοζόμενη Βελτιστοποίηση Της Νιτροποίησης Σε Αντιδραστήρα Διαλείποντος Έργου Με Περιοδική Λειτουργία

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