Guidelines for Setting Filtering and Module Execution Rate Terry Blevins Principal Technologist
Jul 05, 2015
Guidelines for Setting Filtering and Module Execution Rate
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
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 2nd 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 Converter
1st Order Configurable
Software Filter
DeltaV Analog Input Card
Hardware Filter
A/D Converter
3rd Order Sigma Delta Converter
FIR Digital Filter
CHARM Analog Input
2nd Order Software
Filter*
*DeltaV v11.3.1
A/D FIR Filter – 50-60 Hz Attenuation
Filtering of process measurements
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. Field Input of 4.5 Hz (green), AI output (blue) of Module executing at 5 Hz
(200 msec) - Scaled inTime
Example – Process Noise
Var 10 PI4735A.PV - Ind. DO1204AA.datPrimary Cleaner Feed Pressure 05/29/2001 14:15:14
Time Series
0.00 102.40 204.80 307.20 409.60Sec
35.75
37.16
38.57
39.98
41.39psig
Mean=38.1345 2Sig=1.671 (4.38%)
Var 10 PI4735A.PV - Ind. DO1204AA.datPrimary Cleaner Feed Pressure 05/29/2001 14:15:14
Power Spectrum (FFT)
0.00 6.00 12.00 18.00 24.00Cycle/Sec
0.0000
1.9535
3.9069
5.8604
7.8138Variance (E-3)
0
25
50
75
100% Variance
De-Trend=No, Win=None, Seg=0
Var 10 PI4735A.PV - Ind. DO1204AA.datPrimary Cleaner Feed Pressure 05/29/2001 14:15:14
Auto Correlation (FFT)
0.0 3.2 6.4 9.6 12.8Sec
-1.0
-0.5
0.0
0.5
1.0
Var 10 PI4735A.PV - Ind. DO1204AA.datPrimary Cleaner Feed Pressure 05/29/2001 14:15:14
Power Spectrum PeaksDe-Trend=No, Win=None, Seg=0
Lower Threshold: 1.703E-3, Change Threshold: 2.044E-3
Total Variance: 0.69770% Total P-P 2 Sigma
Peak Freq. Period Shape Variance Amplit. Remain.1 0.018785 53.234 4 3.557 0.44559 1.64062 0.039546 25.287 2 1.246 0.26373 1.66013 0.38086 2.6256 1 0.8886 0.22271 1.66314 0.70557 1.4173 1 0.8553 0.21849 1.66345 0.062533 15.992 2 1.294 0.26870 1.65976 0.19043 5.2513 1 0.7231 0.20090 1.66457 0.36664 2.7275 4 1.797 0.31669 1.6555
**Truncated**
Example – Process Noise
Var 07 19FC058 - Auto CWAR0907001AG.datStock Flow to Tickler #1 07/30/2009 15:32:31
Time Series
0.00 163.84 327.68 491.52 655.36Sec
2207
2379
2550
2722
2893GPM
Mean=2527.04 2Sig=169.5 (6.71%)
Var 07 19FC058 - Auto CWAR0907001AG.datStock Flow to Tickler #1 07/30/2009 15:32:31
Power Spectrum (FFT)
0.00 3.00 6.00 9.00 12.00Cycle/Sec
0.000
10.263
20.525
30.788
41.050Variance
0
25
50
75
100% Variance
De-Trend=No, Win=None, Seg=0
Var 07 19FC058 - Auto CWAR0907001AG.datStock Flow to Tickler #1 07/30/2009 15:32:31
Auto Correlation (FFT)
0.00 0.64 1.28 1.92 2.56Sec
-1.0
-0.5
0.0
0.5
1.0
Var 07 19FC058 - Auto CWAR0907001AG.datStock Flow to Tickler #1 07/30/2009 15:32:31
Power Spectrum PeaksDe-Trend=No, Win=None, Seg=0
Lower Threshold: 2.1918, Change Threshold: 2.6302
Total Variance: 7182.2% Total P-P 2 Sigma
Peak Freq. Period Shape Variance Amplit. Remain.1 0.21661 4.6165 3 0.7221 20.370 168.882 0.23534 4.2491 3 0.9107 22.875 168.723 0.86070 1.1618 5 0.8980 22.714 168.734 0.30203 3.3109 3 0.6074 18.682 168.985 0.063113 15.845 5 0.9172 22.956 168.726 5.0003 0.19999 3 0.5105 17.126 169.067 1.1671 0.85681 2 0.4993 16.938 169.078 0.57133 1.7503 6 1.205 26.317 168.479 0.65307 1.5312 3 0.5172 17.239 169.0610 0.84623 1.1817 3 0.6678 19.589 168.93
Configuring Anti-aliasing Filter
Rule 1: If a measurement is characterized by process noise then anti-aliasing filtering should be applied at the IO channel.
Note: Help is providing in setting this filter based on module execution period.
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.
•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.
Input
Time
ValueOutput
I1
O1
T2
O2
I2
Gain = O2 – O1
I2 – I1
Note: Output and Input in %
of scale
Dead Time = T2 – T1
63.2% (O2 - O1)
T3T1
Time Constant = T3 – T2
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.
Time
Value
T2
O2
T3T1
I2
Integrating Gain = O2 – O1
(I2 - I1 ) * (T3 – T2)
Dead Time = T2 - T1
Note: Output and Input in % of scale, Time
is in seconds
Input
Output
I1
O1
•When a process output changes without bound when the process input is changed by a step, the process is know as a non-self- 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
PID Tuning Setpoint Change Load DisturbanceTuning Method
Filtering as % of Response Time
Gain Reset Rate Response* Time (sec)
Overshoot (%)
Recovery* Time (Sec)
Max Dev (%)
Typical PI
No Filtering 1.13 3.5 - 7 0.2 11 6
10% 0.92 4.8 - 11 - 16 7.2
30% 0.90 6.7 17 - 22 7.7
60% 0.93 8.9 - 23 - 27 7.6
120% 1.06 11.8 - 19 - 33 7.0
Lambda
λ=1.5
No Filtering 0.6 4.5 - 18 - 23 10.3
10% 0.47 6.5 - 34 - 40 11.9
30% 0.44 9.2 53 59 12.8
60% 0.47 12.3 - 67 - 74 12.8
120% 0.52 16.9 - 83 - 93 12.1
Process Gain=1, TC=4 sec, DT=1 sec
* Time to return within 2% of setpoint.
Control Execution Period
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
63% of Change
Process Output
Process Input
Deadtime (TD )
O
I
New Measurement Available
τTime Constant ( )
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
PID Tuning Setpoint Change Load DisturbanceTuning Method
Module Period
Gain Reset Rate Response* Time (sec)
Overshoot (%)
Recovery* Time (Sec)
Max Dev (%)
Typical PI
0.2 sec 0.89 3.3 - 7 - 12 10
0.5 sec 1.01 3.9 - 9 - 14 10
1 sec 1.31 5.4 12 - 16 10
2 sec 1.0 14.3 - 47 - 53 12
5 Sec 0.22 22 - 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
PID Tuning Setpoint Change Load DisturbanceTuning Method
Module Period
Gain Reset Rate Response* Time (sec)
Overshoot (%)
Recovery* Time (Sec)
Max Dev (%)
Typical PI
0.5 sec 0.49 3.2 - 16 - 20 15
1 sec 0.57 4.5 - 23 - 27 15.3
2sec 0.6 7.0 38 - 42 16.9
5 sec 0.21 22 - 316 - 330 18
10 Sec 0.12 0.44 - >600 - >600 19
Module Execution Impact - Process Gain=1, TC=2 sec, DT=2 sec
Example: Control Execution - Rule 4 (Cont)
Examples – Applying Execution Rules
Fast Process (sec) Typical Process (sec)
Process Type Deadtime Time Constant
Execution Period
Deadtime Time Constant
Execution Period
Liquid Flow/Pressure 0.1 0.4 0.1 0.1 1 0.2
Gas Flow 0.1 1 0.2 0.3 5 1
Column Pressure 1 10 2 5 50 10
Furnace Pressure 0.1 0.5 0.2* 0.3 5 1
Vessel Pressure 0.2 10 2 0.6 30 10
Compressor Surge Control 0.05 0.5 0.1 0.2 5 1
Liquid Level 0.05 30 10 0.3 300 60
Exchanger Temperature 10 30 20* 30 180 60*
Batch Temperature 10 300 60 30 500 60
Column Temperature 30 600 60 60 600 60
Boiler Steam Temperature 10 30 20* 30 180 60*
Vessel Temperature 30 300 60 60 600 60
Gas composition – O2 10 12 20* 20 60 40*
Vessel Composition 30 300 60 60 600 60
Inline (static Mixer) pH 2 2 4* 3 5 6*
Vessel pH 30 60 60* 60 600 60
•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.
$/HR
Profi
t
$/HR
Profit
Maximum
Maximum
$ Lost
$ Lost
“Better” Control
Time
Time
$/HR
Profit
$/HR
Profit
Maximum
Maximum
$ Lost
$ Lost
“Better” Control
Time
Time
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.