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Published byLeon Milstead Modified about 1 year ago

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Control performance assessment in the presence of valve stiction Wei Yu, David Wilson & Brent Young Industrial Information & Control Centre New Zealand diwilson@aut.ac.nz

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Question for the day? What do you do if your control loop looks like this …? Re-tune the loop, Service the valve, or just ignore it Controller plant Characteristic triangular wave

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Context & rationale Many control loops are badly tuned We are lazy Conditions change Valves get sticky Should I retune or service the valve ? Retuning is easier, service requires a shutdown What is my expected economic return? Calculate a CPA

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What is valve stiction ? Vague term meaning valve problems Stiction: sticky/friction Typically valve sticks & jumps

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Various levels of stiction

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Plant under consideration Linear plant with known delay With disturbance model plant disturbance We want to control this system as best we can NL plants in CPI are smooth & benign Key NL are in actuators

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Control Performance Assessment (CPA) Use a minimum variance controller as a performance benchmark Estimate from closed-loop data Must know plant delay Harris Index Zero is bad, 1 is probably “too good”, 0.7 is optimal Calculate from disturbance model Estimate from ARIMA identification Direct from data But we need to know the deadtime, b

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How do I estimate 2 mv with stiction ? Why bother? Gives an indication on how good the loop would be if the valve was maintained Is it worth shutting down & servicing the valve? How to remove the nonlinearities? Existance of the control invariant for NL systems ( Harris & Yu ) Run a smoothing spline through the data Identify periods when valve is stuck fast

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Controller System under consideration (With stiction) plant Valve stiction Nonlinear phenomena disturbance measurements Un-observable

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Our scheme Fit a non-parametric spline curve to y OK for non-differential nonlinearities Remove nonlinearity with spline Adjust smoothing to “just remove” NL Use linearity check Compute 2 MV from d sequence

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Removing the nonlinearity Fit a smooth curve to approximate z Reconstruct d from y-z Unobservable, but smoothish

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Establishing linearity & Gaussianity The power spectrum A(f) and bispectrum B(f 1,f 2 ) of this series are The squared bicoherence is

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Controllerplant Valve stiction disturbance measurements Example: A plant with a sticky valve

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Testing the method Increasing level of smoothness Statistical tests

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Monte-Carlo Simulation results True value Estimation gets worse

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Waiting for steady-state Select periods at steady-state Use only this data for the identification

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Areas of long periods “Islands” of long periods Increasing noise We are interested in the long periods when we might reach steady state

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Uninteresting area Period < 10 Period = 10 contour Optimum noise level Valve too “jumpy” Too much noise, so valve is continually “dancing”

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Does it work? True value Uncertainty bounds Good estimation due to longer sequences Bad estimation due to excessive nonlinearities

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Conclusions Estimate the CPA even with stiction nonlinearities Do we need to shut down? Heuristic curve smoothing is OK Extracting steady-state is better provided: Sequences are long enough System is stable and relatively short time constants Need to know: Approximate process deadtime Approximate dominant time constants Now we know if it is worthwhile to service the valve.

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