Image based interface level control in a separation cellslshah/files/nserc_irc... · Image based...
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Image based interface level Image based interface level control in a separation cellcontrol in a separation cell
P.V. Jampana, S. L. Shah, R. Kadali* and T. Hrycay*Department of Chemical Engineering
University of Alberta, Canada* Suncor Energy Inc.
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22Problem Description Problem Description
Schematic of a separation cell
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33Problem Description Problem Description –– MotivationMotivation
The interface level between the Bitumen froth and the middlings affects the froth quality and hence influences process economics.
A good regulation of the interface level improves froth quality, reduces Bitumen losses and results in economic gains (million of dollars per year).
Problem in automatic control of the interface level is the lack of safe and reliable sensors for interface detection.
Conventional sensors such as Capacity probes give very crude estimates; Nuclear density profilers have been abandoned due to concerns over safety.
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Separation cell 6 at Suncor Energy Inc.
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Separation cell 6 at Suncor Energy Inc.
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66Problem Description Problem Description –– Interface videoInterface video
In the absence of measurements, operators watch the side glass video and adjust the tailings pump speed manually
A video clip from sight glass camera
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77Problem Description Problem Description –– manual controlmanual control
Manual control of interface level
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88Problem Description Problem Description –– Desired control systemDesired control system
The desired automatic control system using image sensor estimatesfor use in the feedback loop
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99Interface level detection algorithmInterface level detection algorithm
Image ROI:- Only the contents of this area in the image are processed.
As the interface level is mostly horizontal, horizontal edge detection is performed.
ROI: fixedROI: fixed
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1010Interface level detection algorithm Interface level detection algorithm –– Edge detectionEdge detection
Result of horizontal edge detection:-
For robust tracking in the presence of spurious edges particle filtering methods are used
Edge detectionEdge detection
SpuriousSpuriousedgesedges
after smoothingafter smoothing
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Interface level detection algorithm Interface level detection algorithm Bayesian FilteringBayesian Filtering
• Bayesian filtering is a probabilistic framework for estimating the current state given all the observations.
• Two equations provide the basis of this framework:-
– State equation
– Observation equation
Where and are assumed to be white noise sequences,is the current state and is the current observation.
• The problem is to find the pdf (called the posterior) :-
( ) 11 −− += kkk wxfx
( ) kkk vxgZ +=
1−kw kvkx kz
( )kk ZZZxp ,...,,| 21
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1212Bayesian filtering Bayesian filtering ---- Solutions to the recursive setSolutions to the recursive set
When the state and the measurement equations are linear and if the noise terms and the prior are assumed to be Gaussian, the integral can be evaluated analytically and the recursive solution is given by the Kalman filter algorithm
In the general case the integration cannot be performed analytically (because the process may be non-linear and/or the noise is non-Gaussian). In such cases an approximate solution is obtained using Monte Carlo methods –these methods are collectively called particle filters
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1313Interface level detection algorithm Interface level detection algorithm ---- Particle filtersParticle filters
IntractableIntractable
( )1211 ... −− kk Z,,Z,Z|xpDraw
samples
( ) ( ) ( )∫−
−−−−− =1
112111121 ,...,,||,...,|kx
kkkkkkk dxZZZxpxxpZZZxp
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1414Particle filters Particle filters –– SIR FilterSIR Filter
( )121 ... −kk Z,,Z,Z|xp
State
Equation
( ) ( ) ( )12121 ...... −⋅⋅= kkkkkk Z,,Z,Z|xpx|ZpKZ,,Z,Z|xp
( )1211 ... −− kk Z,,Z,Z|xp
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1515State modelState model
-- Current interface level (in pixels) -- Gaussian white noise sequence with a small variance
JustificationsFirst principle model is difficultTemporal continuity constraint when frame rate is high
11 −−= kkk w+xx
kx1−kw
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Results –Gaussian model
Disadvantage :- A Gaussian likelihood model only supportsa unimodal posterior, hence the tracker is distracted by noise
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Results –Non Gaussian model
AdvantageAdvantage:- Multi-modal likelihood (and posterior), the tracker converges on the interface level after initially being distracted.
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1818Results Results –– Real time imagesReal time images
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1919Interface level quality estimationInterface level quality estimation
Fines situation
Support of posterior p.d.f.
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2020Interface level quality estimationInterface level quality estimation
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2121Problem Description Problem Description –– Desired control systemDesired control system
The desired automatic control system using image sensor estimatesfor use in the feedback loop
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2222Software and HardwareSoftware and Hardware
Intel Image Processing Primitives (IPPL) 5.1 for WindowsIntel Open Computer Vision Library (Intel OpenCV)Matrox Imaging LibraryMatrox Meteor II frame grabber card
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2323Closed loop control resultsClosed loop control results
Manual control Automatic control usingImage sensor estimates
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2424Concluding Remarks
Bitumen-froth and Middling's interface is detected using a Particle filtering algorithm.
The developed algorithm is robust to lighting changes.
Quality estimates of the interface level are also computed to handle process abnormalities.
Results show that variance in the interface level is reduced significantly when the interface level estimates from the image based sensor are used in the feedback loop.
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Future workFuture work
Image-based sensor for line 7. This has 3 sight glasses. Preliminary results will be shown.
AcknowledgementsG. Brandt, M. G. Brandt, M. CollinaCollina, B. Doucette (all at , B. Doucette (all at SuncorSuncor)) for technical help
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2626
THANK YOU