Terry Blevins Principal Technologist

Presenters
 Terry Blevins, Principal
Technologist
 Kent Burr, Gary Law, Joe Nelson
– DeltaV Product Engineering

Introduction


Filtering and module execution period can directly impact control performance. In this workshop we will be addressing:
– Protection against 50-60hz pickup provided by analog input card and Charm analog input.
– Filtering of process measurements –configuration guideline to void aliasing and to minimize impact of process noise.
– Control execution – configuration guideline for setting execution period based on process dynamics, impact on control performance.
Guidelines for setting filtering and execution period are presented and examples used to illustrate their impact.

Protection against 50-60 Hz pickup
A/D
Converter
Hardware
Filter
DeltaV Analog Input Card
1 st Order
Configurable
Software
Filter
A/D Converter
3 rd Order
Sigma
Delta
Converter
FIR
Digital
Filter
CHARM Analog Input
2 nd Order
Software
Filter*
*DeltaV v11.3.1


The DeltaV analog input card uses a two pole hardware (RC) filter to provide -3 dB at 2.7 Hz and > -40dB attenuation at 50-60 Hz.
The CHARM analog input uses the A/D software (
FIR ) and configurable
2 nd order software filter after the A/D. By default will provide -3 dB at 2.7
Hz and approx – 70 dB attenuation at 50-60Hz.

A/D FIR Filter – 50-60 Hz Attenuation

Filtering of process measurements
Field Input of 4.5 Hz (green), AI output (blue) of Module executing at 5 Hz (200 msec) - Scaled inTime


The impact of aliasing for noise containing frequencies higher than ½ the module execution frequency
(Nyquist frequency) is illustrated in this examples.
Filtering to prevent aliasing can not be added at the module level since at this point the data is already aliased.

Example – Process Noise
Var 10 PI4735A.PV - Ind.
Primary Cleaner Feed Pressure psig
41.39
39.98
38.57
37.16
Time Series
DO1204AA.dat
05/29/2001 14:15:14
Var 10 PI4735A.PV - Ind.
Primary Cleaner Feed Pressure
Variance (E-3)
7.8138
5.8604
3.9069
1.9535
Power Spectrum (FFT)
DO1204AA.dat
05/29/2001 14:15:14
% Variance
100
75
50
25
35.75
0.00
102.40
204.80
Mean=38.1345 2Sig=1.671 (4.38% )
Var 10 PI4735A.PV - Ind.
Primary Cleaner Feed Pressure
Auto Correlation (FFT)
1.0
307.20
409.60
Sec
DO1204AA.dat
05/29/2001 14:15:14
0.5
0.0
-0.5
-1.0
0.0
3.2
6.4
9.6
12.8
Sec
0.0000
0.00
6.00
12.00
18.00
De-Trend=No, Win=None, Seg=0
Var 10 PI4735A.PV - Ind.
Primary Cleaner Feed Pressure
Power Spectrum Peaks
De-Trend=No, Win=None, Seg=0
Lower Threshold: 1.703E-3, Change Threshold: 2.044E-3
24.00
Cycle/Sec
0
DO1204AA.dat
05/29/2001 14:15:14
Total Variance: 0.69770
Peak Freq.
1 0.018785
2
3
4
5
0.039546
0.38086
0.70557
0.062533
6
7
0.19043
0.36664
Period
53.234
25.287
2.6256
1.4173
15.992
5.2513
2.7275
4
2
1
1
2
% Total
Shape Variance
3.557
1.246
0.8886
0.8553
1.294
1 0.7231
4 1.797
**Truncated**
P-P
Amplit.
0.44559
0.26373
0.22271
0.21849
0.26870
0.20090
0.31669
2 Sigma
Remain.
1.6406
1.6601
1.6631
1.6634
1.6597
1.6645
1.6555

Example – Process Noise
Var 07 19FC058 - Auto
Stock Flow to Tickler #1
GPM
2893
2722
2550
Time Series
CWAR0907001AG.dat
07/30/2009 15:32:31
Var 07 19FC058 - Auto
Stock Flow to Tickler #1
Variance
41.050
30.788
20.525
Power Spectrum (FFT)
CWAR0907001AG.dat
07/30/2009 15:32:31
% Variance
100
2379 10.263
25
2207
0.00
Var 07 19FC058 - Auto
Stock Flow to Tickler #1
1.0
163.84
327.68
Mean=2527.04 2Sig=169.5 (6.71%)
Auto Correlation (FFT)
0.5
0.0
-0.5
-1.0
0.00
0.64
1.28
491.52
655.36
Sec
CWAR0907001AG.dat
07/30/2009 15:32:31
0.000
0.00
3.00
Total Variance: 7182.2
Peak Freq.
% Total P-P
Period Shape Variance Amplit.
1
2
3
0.21661
0.23534
0.86070
4.6165
4.2491
1.1618
3
3
5
0.7221
0.9107
0.8980
20.370
22.875
22.714
6
7
4
5
0.30203
5.0003
1.1671
3.3109
0.063113
15.845
0.19999
0.85681
3
5
3
2
0.6074
0.9172
0.5105
0.4993
18.682
22.956
17.126
16.938
8
9
10
0.57133
0.65307
0.84623
1.7503
1.5312
1.1817
6
3
3
1.205
0.5172
0.6678
26.317
17.239
19.589
6.00
De-Trend=No, Win=None, Seg=0
Var 07 19FC058 - Auto
Stock Flow to Tickler #1
Power Spectrum Peaks
De-Trend=No, Win=None, Seg=0
Lower Threshold: 2.1918, Change Threshold: 2.6302
2 Sigma
Remain.
168.88
168.72
168.73
168.98
168.72
169.06
169.07
168.47
169.06
168.93
9.00
12.00
Cycle/Sec
0
CWAR0907001AG.dat
07/30/2009 15:32:31
1.92
2.56
Sec
75
50

Configuring Anti-aliasing Filter
Note: Help is providing in setting this filter based on module execution period.
 Rule 1 : If a measurement is characterized by process noise then anti-aliasing filtering should be applied at the IO channel.

Filtering Within a Module
 Rule 2 : To remove process noise the filter time constant of an analog input in a module should be no more than 10% of the process response time.
 Example: For a process response time of 5 seconds the input filter time constant should be no more than 0.5 seconds.

Response Time – Self-regulating Process
 The process dynamic of a self-regulating process may be approximated as first order plus deadtime and the response time assumed to be the process deadtime plus the process time constant.
Value
Gain =
O2 – O1
I2
– I1
Dead Time =
T2 – T1
Time Constant =
Note: Output and Input in
% of scale
T3 – T2
O2
Output
Input
O1
I1
T1 T2 T3
63.2% (O2 - O1)
Time
I2
•Most processes in industry may be approximated as first order plus deadtime processes.
•A first order plus deadtime process exhibits the combined characteristics of the lag and delay process.

Response Time – Integrating Process
 For integrating processes, the response time may be assumed to be the deadtime plus the time required for a significant response to a change in the process input.
Value
Integrating Gain =
O2 – O1
(I2 - I1 ) * (T3
– T2)
Dead Time = T2 - T1
Note: Output and Input in % of scale,
Time is in seconds
Output
O1
Input
I1
Time
T1 T2 T3
I 2
O2
•When a process output changes without bound when the process input is changed by a step, the process is know as a nonself- regulating process.
•The rate of change
(slope) of the process output is proportional to the change in the process input and is known as the integrating gain.

Example: Impact of Filtering (Cont)

Example: Impact of Filtering
Tuning
Method
Typical PI
Filtering as % of
Response Time
No Filtering
PID Tuning Setpoint Change
Gain Reset Rate Response* Overshoo
Time (sec) t (%)
1.13
3.5
7 0.2
Load Disturbance
Recovery* Max Dev
Time (Sec) (%)
11 6
10%
30%
0.92
0.90
4.8
6.7
11
17 -
16
22
7.2
7.7
60% 0.93
8.9
23 27 7.6
Lambda
λ=1.5
120%
No Filtering
10%
30%
60%
1.06
0.6
0.47
0.44
0.47
11.8
4.5
6.5
9.2
12.3
-
-
-
-
19
18
34
53
67 -
-
-
-
33
23
40
59
74
7.0
10.3
11.9
12.8
12.8
83 93 12.1
120% 0.52
16.9
Process Gain=1 , TC=4 sec, DT=1 sec
* Time to return within 2% of setpoint .

Control Execution Period
Deadtime (T
D
)
I
Process Input
Process Output
63% of Change O
Control Execution
New Measurement Available


To minimize delay introduced by IO processing, analog inputs are oversampled at a rate sufficient to support the fastest module execution rate.
To reduce controller load, the module execution rates is adjustable. The default execution rate is 1/sec.

Control Execution
 Rule 3 : Control loop execution period should be ¼ the process response time or less to achieve best control performance.
 Rule 4 : The module execution period should be 2X the Process
Deadtime or less.
Note: Executing control faster than the guideline provides little improvement in setpoint and load disturbance response. Quality of control will be degraded if execution is set significantly slower than the Guideline.

Example: Control Execution - Rule 3
Tuning
Method
Typical
PI
Module
Period
Gain
PID Tuning
Reset
0.2 sec 0.89
3.3
-
Rate
0.5 sec 1.01
3.9
1 sec
2 sec
1.31
1.0
5.4
14.3
-
5 Sec 0.22
22 -
9
12
47
Setpoint Change
Response* Overshoot
Time (sec) (%)
7 -
Load Disturbance
Recovery* Max Dev
Time (Sec) (%)
12 10
-
-
14
16
53
10
10
12
316 310 15
Module Execution Impact - Process Gain=1 , TC=3 sec, DT=1 sec

Example: Control Execution - Rule 3 (Cont)

Example: Control Execution - Rule 4
Tuning
Method
Typical
PI
Module
Period
PID Tuning
Gain Reset Rate
0.5 sec
1 sec
2sec
0.49
0.57
0.6
3.2
4.5
7.0
5 sec 0.21
22
10 Sec 0.12
0.44
-
-
-
-
Setpoint Change
Response Overshoo
* Time
(sec) t (%)
16 -
23 -
38 -
Load Disturbance
Recovery Max Dev
* Time
(Sec)
(%)
20 15
27 15.3
42 16.9
316
>600 -
330
>600
18
19
Module Execution Impact - Process Gain=1 , TC=2 sec, DT=2 sec

Example: Control Execution - Rule 4 (Cont)

Examples – Applying Execution Rules
Process Type
Liquid Flow/Pressure
Gas Flow
Column Pressure
0.1
0.1
1
Furnace Pressure
Vessel Pressure
0.1
0.2
Compressor Surge Control 0.05
Liquid Level
Exchanger Temperature
Batch Temperature
Column Temperature
Boiler Steam Temperature
Vessel Temperature
Gas composition – O2
Vessel Composition
Inline (static Mixer) pH
Vessel pH
30
2
30
30
10
30
10
0.05
10
10
Deadtime Time
Constant
30
30
300
600
30
300
12
0.4
1
10
0.5
10
0.5
300
2
60
Fast Process (sec)
Execution
Period
10
20*
60
60
20*
60
20*
0.1
0.2
2
0.2*
2
0.1
60
4*
60*
Deadtime
60
3
60
60
30
60
20
0.3
30
30
0.1
0.3
5
0.3
0.6
0.2
Time
Constant
300
180
500
600
180
600
60
5
30
5
1
5
50
600
5
600
Typical Process (sec)
Execution
Period
60
6*
60
60
60*
60
60
60*
60
40*
1
10
1
0.2
1
10
• Rule 4 applies Note: Maximum was limited to 60 sec. Faster update may be needed for operator visibility, calculations or alarming

Business Results Achieved


Control variability caused by process noise and unmeasured load disturbances can be minimize through tuning and by following the guidelines for module execution period and input filtering.
When plant throughput is limited by an operating constraint or variation from target operating conditions impacts operating efficiency or product quality, then a reduction in process variation provides direct economic benefit in plant operation.
Maximum
Maximum
$ Lost
$ Lost $/HR
Profit
$/H
R
Pro fit
Time
Time
Maximum
$ Lost
Maximum
$ Lost
$/HR
Profit
$/HR
Profit
Time Time
“Better” Control
“Better” Control

Summary



Easy to follow filtering and execution guidelines are proposed as a means of improving control performance and reducing process variability.
These guidelines are based on the process response time to changes in setpoint and disturbance inputs.
A reduction in process variation can provide direct economic benefit in plant operation when throughput is limited or variations impact operating efficiency or product quality.

Where To Get More Information




DeltaV Product Data Sheet, DeltaV S-Series Traditional
I/O
DeltaV Product Data Sheet, S-series Electronic
Marshalling
W.L. Bialkowski and Alan D. Weldon, The digital future of process control; possibilities, limitations, and ramifications . Vol No. 10, Tappi Journal, October, 1994.
Jeffrey Li, A PID Tuning Method Using MINLP with
Nonparametric Process and Disturbance Models, AIChE
2010 Spring National Meeting, San Antonoio, TX.