guidelines for setting filter and module execution rate

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%)


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


Auto Correlation (FFT)

0.0 3.2 6.4 9.6 12.8Sec

-1.0

-0.5

0.0

0.5

1.0


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%)


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


Auto Correlation (FFT)

0.00 0.64 1.28 1.92 2.56Sec

-1.0

-0.5

0.0

0.5

1.0


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


Module Period


Overshoot (%)


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


Module Period


Overshoot (%)


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.

guidelines for setting filter and module execution rate

Technology