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DATA RECONCILIATIONAND INSTRUMENTATION
UPGRADE. OVERVIEW AND CHALLENGES
PASI 2005.Cataratas del Iguazu, Argentina
Miguel Bagajewicz University of Oklahoma
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OUTLINE
• A LARGE NUMBER OF PEOPLE FROM ACADEMIAAND INDUSTRY HAVE CONTRIBUTED TO THE AREAOF DATA RECONCILIATION.
• HUNDREDS OF ARTICLES AND THREE BOOKS HAVE BEEN WRITTEN.
• MORE THAN 5 COMMERCIAL SOFTWARE EXIST.
• ALTHOUGH A LITTLE YOUNGER, THE AREA OF INSTRUMENATION UPGRADE IS EQUALLY MATURE
• ONE BOOK HAS BEEN WRITTEN
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OUTLINE• OBSERVABILITY AND REDUNDANCY• DIFFERENT TYPES OF DATA RECONCILIATION
- Steady State vs. Dynamic - Linear vs. Nonlinear
• GROSS ERRORS- Biased instrumentation, model mismatch and outliers- Detection, identification and size estimation
• INSTRUMENTATION UPGRADE• SOME EXISTING CHALLENGES• INDUSTRIAL PRACTICE
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Simple Process Model of Mass Conservation
f1 f6 f7
f3 f5 f9 f11
f2 f4 f8 f10
f1 - f2 - f3 = 0f2 - f4 = 0
f3 - f5 = 0f4 + f5 - f6 = 0
f6 - f7 =0 Material Balance Equationsf7 - f8 - f9 = 0
f8 - f10 = 0f9 - f11 = 0
U1
U2
U3
U4
U5
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Variable Classification
Measured (M)
Observable (O)Unmeasured (UM)
Unobservable (UO)
Variables
f1 f6 f7
f3 f5 f9 f11
f2 f4 f8 f10U1
U2
U3
U4
U5
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Variable Classification
Redundant (R)Measured (M)
Non-redundant (NR)
Observable (O)Unmeasured (UM)
Unobservable (UO)
Variables
f1 f6 f7
f3 f5 f9 f11
f2 f4 f8 f10U1
U2
U3
U4
U5
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Conflict among Redundant Variables
f1 - f7 =0Material Balance Equations
f1 – f8 – f11= 0
f1 f6 f7
f3 f5 f9 f11
f2 f4 f8 f10U1
U2
U3
U4
U5
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Conflict Resolution
s.t.
]~[]~[ +−+ −− RRRT
RR ffQffMin 1
0=RR fE ~
( )[ ] +−−= RR
TRRR
TRRR fEEQEEQIf 1~Analytical Solution
Data reconciliation in
its simplest form
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Precision of Estimates
+= NRNR ff~
NRNRORROO fCfCf ~~~+=
( )[ ] +−−= RR
TRRR
TRRR fEEQEEQIf 1~
If , and the variance of x is Q, then the variance of z is given by:
xz Γ=TQQ ΓΓ=
~
( ) FRRTRFRR
TRFRFRFR QCCQCCQQQ ,
1,,,,
~ −−=
FNRFNR QQ ,,~
=
[ ] [ ]TSRORONR
RSROROO CC
CCQ ⎥⎦
⎤⎢⎣
⎡=
~~
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Some Practical Difficulties
• Variance-Covariance matrix is not Known• Process plants have a usually a large number of Tanks• Plants are not usually at Steady State• How many measurements is enough?
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Estimation of the Variance-Covariance Matrix.
TRRR EQErCov =)(
( ) ( )( )⎪⎪⎩
⎪⎪⎨
⎧
−−−
=
=
∑
∑
=
=
n
kjRkjRiRkiRjRiR
n
kkiRiR
ffffn
ffCov
fn
f
1,,,,,,
1,,
~~1
1~,~
~1
•Direct Approach
•Indirect Approach rfE RR =+
1) Obtain r
2) Maximum likelihood estimate QR
However, this procedure is not good if outliers are present. Robust estimators have been proposed (Chen et al, 1997)
Almasy and Mah (1984), Darouach et al., (1989) and Keller et al (1992)
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Tank Hold Up Measurements
Steady State formulations are used
Pseudo-Stream
Level at t=t0
Level at t=t1
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The procedure is based on the following assumptions:
a) A normal distribution of measurement errors. b) A single value per variable.c) A “steady-state” system.
a) Substantiated by the central limit theorem. b) Also valid for means. c) No plant is truly at “steady-state”. Process oscillations occur. Therefore, it is said that it is valid for a “pseudo-steady state”system”
]~[]~[ +−+ −− RRRT
RR ffQffMin 1
0=RR fE ~
90
95
100
105
110
1 250Time
90
95
100
105
110
1 250Time
90
95
100
105
110
1 250Time
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Reconciliation of averages is equal to the average of reconciledvalues using dynamic data reconciliation (Bagajewicz and Jiang, 2000; Bagajewicz and Gonzales,2001).
That is, there is no need to adjust the variance-covariance matrix for process variations.
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Dynamic Data Reconciliation
Linear Case(after cooptation):
{ }]~[]~[]~[]~[ 11 +−++−+
∀
−−+−−∑ RiRiRVT
RiRiRiRiRfT
RiRii
VVQVVffQffMin
0~
~~
=
=
R
RR
fC
fAdtVdB
When B=I, the Kalman filter can be used.
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Dynamic Data Reconciliation
Difference Approach: Darouach, M. and M. Zasadzinski, 1991, Rollins, D. K. and S. Devanathan, 1993.
An algebraic system of equations follows. Integral Approach: Jiang and Bagajewicz, 1997.
The technique estimates the coefficients of polynomials.
( ) 0~~~~,,,, ==− iRiRiRiR FCFAVVB
{ }]~[]~[]~[]~[ 11 +−++−+
∀
−−+−−∑ RiRiRVT
RiRiRiRiRfT
RiRii
VVQVVffQffMin
0~
~~
=
=
R
RR
fC
fAdtVdB
f tR kR
k
sk≈
=∑α
0[ ] 1
00
110 1
+
==
++ ∑∑ +
==− ks
k
Rk
R
s
k
kRkRRRR t
kAtBVVB αω
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Nonlinear Data Reconciliation ])(~[])(~[ ,
1,
0kMkM
TkMkM
N
kztxQztxMin −− −
=∑
)~,~(~
2111 xxg
dtxd
=
0)~,~( 212 =xxg
Applied in practice to steady state models with material, component and energy balances. In the dynamic case, orthogonal collocationwas used (Liebmann et al, 1992) or linearization (Ramamurthi et al.,1993) or use of DAE (Albuquerque and Biegler, 1996).
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Gross Errors
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Types of Gross Errors
Biases Leaks (Model departures)True outliers
9596979899
100101102103104105
1 250Ti me
96979899
100101102103104
0 250Time
Tem
pera
ture
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Hypothesis Testing Global Test (Detection)
Nodal Test (Detection and Identification)
Maximum Power versions of this test were also developed. Rollins et al (1996) proposed an intelligent combination of nodes technique
rEQEr TRRR
1)( −=γ
iiTRRR
iNi
EQE
rnZ
)(2/1=
NormalonDistributiH
H
r
r
:0:
0:
1
0
⎪⎩
⎪⎨⎧
≠
=
µ
µ
SquaredChionDistributiEQEH
EQEH
RTRRR
TR
RTRRR
TR
−
⎪⎩
⎪⎨⎧
≠
=
:0)(:
0)(:
1
0
µµ
µµ
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Hypothesis Testing Principal Component (Tong and Crowe, 1995)
rWp Tr =
)(: TRRRr EQEofrseigenvectoofmatrixW
NormalonDistributiWH
WH
rT
rT
:0:
0:
1
0
⎪⎩
⎪⎨⎧
≠
=
µ
µ
)(: TRRRr EQEofseigenvalueofmatrixΛ
),0(~ INpr
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Hypothesis Testing Measurement Test
This test is inadmissible. Under deterministic conditions it may point to the wrong location.
iiR
iiLCT
Q
ffZ)~(
~ +−=χ
NormalonDistributifH
fH
ii
ii
:0:
0:
1
0
⎪⎩
⎪⎨⎧
≠−
=−
φ
φ
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Hypothesis Testing Generalized Likelihood ratio
Leaks can also be tested.
SquaredChionDistributibAeH
H
ir
r
−
⎪⎩
⎪⎨⎧
=
=
::
0:
1
0
µ
µ{ }{ }0
1
PrPr
supHrHr
i∀=λ
})(5.0exp{)}()()(5.0exp{sup 1
1
, rQEErbAerQEEbAer
TRR
Ti
TRR
Ti
ib−
−
∀ −−−−
=λ
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Multiple Error Detection Serial Elimination
Apply recursively the test and eliminate the measurement
Serial CompensationApply recursively the test, determine the size of the gross error and adjust the measurement
Serial Collective CompensationApply recursively the test, determine the sizes of all gross error and adjust the measurements
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Multiple Error Detection Unbiased Estimation
One shot collective information of all possible errors followed by hypothesis testing. Bagajewicz and Jiang, 2000, proposed an MILP strategy based on this.
Two distributions approachAssume that gross error have a distribution with larger variance and use maximum likelihood methods (Romagnoli et al., 1981) (Tjoa and Biegler, 1991) (Ragot et al., 1992)
Multiscale Bayesian approach. Bakshi et al (2001).
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EQUIVALENCY THEORY
EXACT LOCATION DETERMINATION IS NOT ALWAYS POSSIBLE, REGARDLESS OF THE METHOD USED.MANY SETS OF GROSS ERRORS ARE EQUIVALENT, THAT IS, THEY HAVE THE SAME EFFECT IN DATA RECONCILIATION WHEN THEY ARE COMPENSATED.
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BASIC EQUIVALENCIESIn a single unit a bias in an inlet stream is equivalent to a bias in an output stream.
S1 S2
S1 S2Measurement 4 3
Reconciled data 3 3 Case 1Estimated bias 1
Reconciled data 4 4Case 2Estimated bias -1
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BASIC EQUIVALENCIES
In a single unit a bias in a stream is equivalent to a leak
S1 S2Leak
S1 S2 LeakMeasurement 4 3
Reconciled data 4 3Case1Estimated bias/leak 1
Reconciled data 4 4Case2Estimated bias/leak -1
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EQUIVALENCY THEORY
For the set Λ={S3, S6} a gross error in one of them can be alternatively placed in the other without change in the result of the reconciliation. We say that this set has Gross Error Cardinality Γ(Λ)=1. ONE GROSS ERROR CAN REPRESENT ALL POSSIBLE GROSS ERRORS IN THE SET.
6S
1S 2S 3S 5S 4S
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GROSS ERROR DETECTIONTWO SUCCESFUL IDENTIFICATIONS:
Exact location
Equivalent location
THIS MEANS THAT THE CONCEPT OF POWER IN LINEAR DATA RECONCILIATION SHOULD BE REVISITED TO INCLUDE EQUIVALENCIES
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COMMERCIAL CODESPackage Nature Offered by
IOO (Interactive On-Line Opt.)
Academic Louisiana State University(USA)
DATACON Commercial Simulation Sciences (USA)
SIGMAFINE Commercial OSI(USA)
VALI Commercial Belsim (Belgium)
ADVISOR Commercial Aspentech (USA)
RECONCILER Commercial Resolution Integration Solutions (USA)
PRODUCTION BALANCE
Commercial Honeywell(USA)
RECON Commercial Chemplant Technologies (Czech Republic)
While the data reconciliation in all these packages is good, gross error detection has not caught with developments in the last 10 years.
Global test and Serial Elimination using the measurement test seem to be the gross error detection and identification of choice.
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INSTRUMENTATION UPGRADE (The inverse engineering problem) Given
Data Reconciliation (or other) monitoring Objectives
Obtain:
Sensor Locations
(number and type)
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INSTRUMENTATION DESIGN
Minimize Cost (Investment + Maintenance)s.t.
-Desired precision of estimates-Desired gross error robustness
Detectability, Residual Precision, Resilience.
-Desired reliability/availability
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Design of Repairable Networks EXAMPLE: Ammonia Plant
5 S8 S4 S7
S6 S5 1 6 4
S1 S3 S2 2 3
Table 3: Optimization results for the simplified ammonia process flowsheet
RepairRate
MeasuredVariables
InstrumentPrecision
(%)
Cost Precision(%)(S2)
(S5)
PrecisionAvailability(%)
(S2) (S5)
Availability(S1)
(S7)
1 S1 S4 S5S6 S7 S8
3 1 1 1 3 2 2040.2 0.8067
1.2893
0.9841
1.2937
0.9021
0.9021
2 S4 S5 S6S7 S8
3 3 1 3 1 1699.8 0.9283
1.9928
1.9712
2.0086
0.9222
0.9062
4 S4 S5 S6S7 S8
3 3 1 3 3 1683.7 1.2313
1.9963
1.9712
2.0086
0.9636
0.9511
20 S4 S5 S6S7 S8
3 3 1 3 3 1775.2 1.2313
1.9963
1.9712
2.0086
0.9983
0.9969
There is a minimum in cost as a function of the repair rate. This allows the design of maintenance policies.
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Upgrade
Upgrade consists of any combination of :
Adding instrumentation.Replacing instruments.Relocating instruments (thermocouples,sampling places, etc).
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Upgrade Example
S6 S8 S7
S1 U1 S2 U2 S3 U3 S4
S5 S9 S8
Flowmeters 3%
Thermocouples 2oF
Reallocation and/or addition of thermocouples as well as a purchase of a new flowmeter improve the precision of heat transfer coefficients
Case ∗1Uσ ∗
2Uσ∗
3Uσ 1Uσ2Uσ 3Uσ c Reallocations New
Instruments
1 4.00 4.00 4.00 3.2826 1.9254 2.2168 10061 ,2, TTu -
2 2.00 2.00 2.00 - - - - - -3 2.00 2.00 2.20 1.3891 1.5148 2.1935 3000 - T2, T6
4 1.50 1.50 2.20 1.3492 1.3664 2.1125 5250 - F4, T2, T6
5 2.40 2.30 2.20 2.0587 1.8174 2.1938 1500 - T6
6 2.20 1.80 2.40 1.7890 1.6827 2.2014 160021 ,2, TTu T6
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Latest Trends
+ Multiobjective Optimization (Narasimhan and Sen, 2001, Sanchez et al, 2000): Pareto optimal solutions (cost vs. precision of estimates are build)
+ Unconstrained Optimization (Bagajewicz 2002, Bagajewicz and Markowski 2003): Reduce everything to cost, that is find the economic value of precision and accuracy.
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Unconstrained OptimizationLet SN0 be an existing network, then an upgrade to network SN has a Value defined as:
Value (SN) = Profit (SN) - Profit (SN0)
Then the upgrade SND problem is defined as:
Maximize { Value (SN) - Cost (SN) }
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Integrated Approach
where Vi (SN) are the Value functions from the three perspectives
i=1 Control Systems
i=2 Material Accounting
i=3 Fault Diagnosis
Maximize { Vi (SN) } - Cost (SN) ∑=
3
1i
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Material Accounting PerspectiveGiven an distribution one can calculate the probability that target production is not met.
This is quantified as the Downside Expected Production Loss:
The above expression assumes process variability ( )<<<
( ))(ˆ, 0, SNg mpσξ
p
m
p*
p ˆT.d)ˆ,(g)m(T)ˆ(DEPL*
σ≈ξσξξ−=σ ∫ ∞−20
pσ̂pσ
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Material Accounting Perspective
....i,iFLDE.iDEFLDEFLDEFL i,ii,i
ii
+Ψ+Ψ+Ψ= ∑∑ 212221
21
1100
In the presence of biases we have:
Financial loss when one gross error is present
Financial loss when no gross error is present
Financial loss when two gross error are present
Portion of time in each state
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Control Perspective
MV’s
CV’s
Conservative Operating Point
Dynamic Operating Regions
*
**
Backed-off Point
Optimal Steady-State
Point
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Control Perspective
Dynamic Operating Regions
for Different Sensor Networks
*
Minimally Backed-off
Points
Optimal Steady-State
Point
*
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Faults Perspective• Consider a set F of possible faults F={ fi }.
Define a set Ai(SN) as the set of sensors in SN that can observe fault fi .
If Ai(SN) is not empty then fi can be detected.
Assume immediate correction occurs for detected faults.
If all faults in F can be detected, then no production losses or safety incidents will be expected.
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Example
Assume the current
network (SN0) consists
of 6 sensors located at
CAi, CA, T, V, F, P
each having a precision of 2%.
CSTR Process (A B+C)
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Results (Control)
000none72,1404,0006,150T, Tc, V, P63,0805,0008,090CA, T, Tc, V, P53,4202,0005,420T, P43,5001,0004,500P34,6303,0007,630CA, Tc, P 25,0602,0007,060CA, P1
Value - Sensor Costs ($/yr)
Sensor Costs ($/yr)
Value($/yr)
New SensorsNo
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Results (Material Accounting)
-12,64413,000355All sensors4
-922100077F23
-8721000127Fvg2
-8681000131CAi1
Value - Sensor Costs
($/yr)
Sensor Costs ($/yr)
Value ($/yr)
New Sensors
No
In all cases the cost of adding sensors far exceeds the profit retuned in the form of Upgrade Value.
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Results (Faults)
2,7202,0004,720Fc, Tc4
2,7202,0004,720Tc, Tci3
4,8103,0007,810Fc, Tc, Ti2
5,8102,0007,810Tci, Ti1
Value - Sensor Costs ($/yr)
Sensor Costs ($/yr)
Value ($/yr)
New SensorsNo
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INTEGRATED PERSPECTIVE
10,5255,00015,525CA, P, Fc, Tc, Ti2
10,9304,00014,930CA, P, Tci, Ti1
Value - Sensor Costs ($/yr)
Sensor Costs ($/yr)
Value ($/yr)
New SensorsNo
Case 1: union of best networks from individual perspectives.
Case 2: union of second best networks.
• These are the best combinations given the tables presented.
• Exhaustive enumeration search is underway.
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CHALLENGES
Academic: Multiple Gross Error Identification
Gross Errors for Nonlinear Systems.
Unconstrained Methods. Solution Procedures
Industrial: Dynamic data reconciliation.
Gross Error Handling.
Sensor Upgrades
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CONCLUSIONS• Data Reconciliation is an academically mature field.
• It is a must when parameter estimation (mainly for on-line
optimization) is desired.
• Commercial codes are robust but lack of up to date gross
error detection/location techniques.
• Instrumentation Upgrade methodologies have reach maturity
• Industry understands the need for upgrading, but academic
efforts have not yet reached commercial status. They will, soon.