1. Effectively Addressing Control Applications
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2. Presenters Terry Blevins James Beall
Insert the presenters and their company logos in the order that they will present.

3. Typical Single Input-Output Control Loop

4. Agenda Measurements – How to avoid signal aliasing
The impact measurement status and selected status options on control.
Single loop control –selection of structure based on application requirements, use of PID notch gain option, providing quick recovery from startup conditions.
Application of output characterization to provide linear installed characteristics.
Feedforward – Use of PID feedforward.
Constructing a summing or multiplying feedforward outside the PID block, advantages and disadvantages.
Implementation and commissioning of dynamic compensation.

5. Agenda (Cont)
Override Control - Implementation and commissioning of override control using PID’s and control selector
Cascade control – use of PID dynamic reset option to improve performance. Implementing MPC cascade.
Ratio control – Using the Ratio block based on SP or wild flow. Adjustment of Ratio target, impact of options.
Split range control and valve position control – Implementation using standard blocks, impact of options.
Duty cycle and increase open/close actuation – Use of the AO with a DO channel to achieve precisely timed on/off actuation.
Fuzzy Logic Control for loops that are lag dominant.
DeltaV Predict MPC block in control applications – addressing difficult dynamics, impact on single loop and override control applications.

6. Processing Analog Inputs
Two-pole hardware filter with cutoff point (-3db) at 2.7 Hz
DeltaV Analog Input Module
Digital Filtered Value to Controller
Configurable Software Filter
A/D
Analog-to-digital Conversion (16 bit)
Every 22 milliseconds
Traditional Transmitter (4-20ma or Hart)

7. I/O Module Software Filter
Software filtering may be applied at the I/O module to avoid aliasing.
Only required if the ma signal contains frequencies > 1/2X the control module execution rate
- Value sampled by the control module
- Aliased signal as see by the control module
- Actual signal (with noise removed)

8. Filter Guideline

9. Analog Measurement Limit and Bad Status
High or low limit is set in status if the measurement exceeds the over-range and under-range values specified for the channel
The status quality will be set to BAD if the measurement excees the A/D range (open or short condition).

10. Adjusting Over & Under Range Detection

11. Analog Input Block

12. Analog Input Block (Measurement) 100 % FILTER 0 % SIMULATE
OUT
100 %
0 %
SIMULATE
(VALUE +STATUS)
AUTO
MANUAL
VALUE
CONVERT
SQ. ROOT
LOW_CUT
L_TYPE
MODE
XD_SCALE
OUT_SCALE PV
PV_FTIME
(Measurement)
UNIT
FILTER
SIMULATE_IN 29 29

13. Status Provided by the Analog Input
A status quality of Uncertain or BAD may be created under limit conditions or Man Mode based on STATUS_OPT parameter selections.
Status is set to BAD if the block mode is set to Out-of-Service.

14. AI Status Options

15. PID Block

16. PID Function Block + Note: For simplicity, not all modes are shown.
CALC
OUT
CAS_IN IN SP
FF_VALUE
TRK_IN_D
TRK_VAL
MODE +
BCKCAL_IN
BCKCAL_OUT
GAIN, RESET, RATE
PV_SCALE C A
100 %
0 %
OUT_HI_LIM
OUT_LO_LIM
BYPASS
FF_SCALE
TRK_SCALE
OUT_SCALE
LIMIT
OUTPUT
FF_GAIN
Note: For simplicity, not all modes are shown. 38 38

17. Impact of IN Status
Status_OPTS determine if control will continue with Uncertain status. The recommended default is Use Uncertain as Good
If status is BAD, then actual mode will automatically go to MAN.
An IN limit status of Constant will automatically cause the reset action to hold last value (to prevent windup under this condition).

18. PID Function Block Algorithm
Either Series or Standard form may be selected using the FORM parameter. The default is Standard. Response is identical of either selection if rate action is not used.
Whether proportional and derivative action are taken on error or PV value may be selected using the STRUCTURE parameter.

19. Selection of PID Form

20. Differences Between Forms
Standard and Series have identical response for Proportional and Integral tuning (0 derivative).
The Series form allows derivative (rate) and integral (reset) to be changed independently i.e. non-interacting.
The Standard Form is capable of a more flexible response

21. PID STRUCTURE Parameter

22. PID STRUCTURE Parameter
Example of I on error, PD on PV
Change setpoint without a large disturbance in the manipulated variable (output)
Level of a feed tank
Base level of a column that feeds another column

23. PID STRUCTURE Parameter
Reactor feed tank: PI on error, D on PV
Controller Output – Flow to reactor

24. PID STRUCTURE Parameter
Reactor feed tank: I on error, PD on PV
Controller Output – Flow to reactor

25. Adjustment of Beta & Gamma
If two degrees of freedom is selected as the PID structure, then BETA and GAMA may be adjusted to determine the fraction of proportional and derivative action on error vs. PV.

26. General Block Diagram Of PID
Reset created by positive feedback network. Automatically provides anti-reset windup protection

27. External Reset
The FRSI_OPTS for “Dynamic Reset Limit” may be selected to enable external reset; i.e., the use of BKCAL_IN in the reset calculation.
When this option is selected, then the downstream block’s CONTROL_OPTS should have “Use PV for BKCAL_OUT” enabled.

28. Enabling PID External Reset
Utilized most often in the primary loop of a cascade
Automatically compensates for poor secondary loop response

29. Improving Process Recovery From Saturated Conditions
On recovery from a saturated condition, when the ARW_HI_LIM and ARW_LO_LIM are set inside the OUT limits, the reset will automatically be increased by 16X until the OUT parameter comes back within the the ARW limits or the control parameter reaches setpoint.

30. Setting ARW limits SP PV
ARW_LO_LIM
OUT
OUT_LO_LIM

31. Eliminating Reset Action When Control Error is Small
By setting the IDEADBAND parameter to a positive value, then reset action is held once control error is reduced below this limit.

32. PID Non-Linear Gain Modifier

33. Mode Determines the Source of Output & SetpointSP
Value
Setpoint Value
BLOCK
AlGORITHM
Operator Entry
Block Algorithm
(internal) A B C D
Output Value
Input
Parameters
(value + Status)
(Operator Entry
ROUT_OUT
Select
RCAS_IN
CAS_IN
ROUT_IN
TRK_IN
CAS_OUT
Back-calculation
Output
Parameter
OUT
RCAS_OUT

34. Mode Parameter Attributes
Target is the requested mode set by operator
Permitted (configured) is the selection available
Actual is the achievable mode given inputs status and target mode
Normal (configured) is the designed mode

35. Rcas & Rout Modes
If the RCAS_IN or ROUT_IN parameter is not updated within a time defined by SHED_TIME when in Rcas or Rout mode respectively, then the actual mode of the associated block will “Shed” based on the configured permitted MODE and the SHED_OPT parameter.

36. Shed Options for Rcas & Rout Modes

37. AO Block

38. Analog Output Block (Output) (Valve Position) HI/LO RATE CONVERT LIMIT
CAS_IN
CONVERT
PV_SCALE
XD_SCALE
MODE
OUT
SIMULATE
CAS
AUTO SP
BCKCAL_OUT
Readback PV
(Output)
(Valve Position)
HI/LO
LIMIT
RATE
IO_OPTS 33 33

39. I/O Options in the AO Block
The Increase to close option should be set to account for field reversal so that the SP value always indicates “implied” valve position.

40. AO Setpoint Rate Limits
The AO setpoint rate limits apply even when the block is in CAS mode (as well as Auto).
This feature may be use to limit the maximum rate at which a valve is change in automatic control.
BKCAL_OUT status is set to limited if changes in OUT are limited. This prevents the PID from winding up under these conditions.

41. Signal Characterization in Control Path

42. Using AO for Duty Cycle Control
Percent time on (ON_TIME) over the duty cycle period is determined by AO setpoint.
Automatically repeats at end of duty cycle using current value of ON_TIME
High resolution of on-off time ON
OFF
% time on
Duty Cycle Period

43. Duty Cycle Control – DO Setup
Typical application is manipulation of heater band input for extruder temperature control
Percise adjustment to ½ of 60 hz since timing is done by DO card hardware
Percent time on is determined by AO output configured to reference the discrete channel

44. Duty Cycle Control – DO Setup
Period of duty cycle is determined by PULSE_PERIOD configured for the DO channel
Period should be set to match the period of execution of the module that contains the AO block that references the DO channel

45. Duty Cycle – Configuring AO
When you browse to a DO channel when configuring the AO block, the only selection is ON_TIME

46. AO With Increase-Decrease Actuator
Continuous Pulse Output Channels

47. Proper Controller Tuning
Is the fastest, quickest, and least expensive improvement one can make in the basic control system to decrease process variability.
The detrimental effects of disturbances, interactions, and control valve dead band are minimized by an appropriate selection of tuning.
There is always a tradeoff between performance and robustness.

48. Uses State-of-the-Art Technology
Process Identification Based on Relay Self-Oscillation Principle
Applicable to a wide range of processes
Slow
Fast
Self-regulating
Integrating
Immune to Noise and Process Load Disturbances
Minimizes Tuning Time

49. Multiple Input - Single Output (MISO) Control
A multiple input - single output controller uses process inputs and outputs not included in a single input- single output ( SISO) controller to improve control response to disturbance and enforce operating constraints.
Information
needed to
Tune Controller
Feedback
Controller (SISO)
Controlled
Manipulated
Process
Constraint
Measured Disturbance
(Other)
(Other)

50. Types of Process Variables
Manipulated - process input which is adjusted to maintain a controlled output at setpoint.
Controlled - process output which is to be maintained at a specific value; i.e., the setpoint
Disturbance - measured process input which may also affect the value of controlled outputs
Constraint - process output which must be maintain within an operating range by restricting the adjustment of manipulated inputs.

51. First Order Plus Deadtime
Process response exhibits the combined characteristics of the lag and delay response. O2 O1
63.2% (O2 - O1)
O2 - O1
Input
Output
Gain =
I2 - I1 I2
Dead Time = T2 - T1 I1
Time Constant ( T ) = T3 - T2 T1 T2 T3
Time

52. Integrating Response
Process output changes without bound when the process input is changed by a step. O1
Input
Output I2 I1
Time

53. Feedforward - MISO Controller
By immediately correcting for a measured load disturbance through adjustment of the manipulated input, the control performance may be improved by feedforward control.
Information
needed to
Commission
Feedforward
L/L & DT
Feedback
Controller
Dynamic
Compensation + +
L/L
Process
Manipulated
Control DT
Measured Disturbance

54. Feedforward Control
Measurable Disturbance

55. Setup of L/L Block for Dynamic Compensation
Set the LEAD_TIME to Tm and the LAG_TIME to Td. The gain of the L/L should be -(Load Dist Gain/Manip Gain).

56. Setup of Deadtime Block for Dynamic Compensation
The DEAD_TIME parameter should be set to a value of DT2 - DT1.

57. External Feedforward - Bias
Feedforward continues even if PID is in Man mode
PID correction is limited by its output limits

58. External Feedforward - Bias(Cont)
Act on IR should be selected to allow the bias (SP) to be initialized on transition from IMAN to CAS actual mode.

59. External Feedforward - Ratio
Feedforward continued even if PID is in Man mode
Ratio correction by PID is limited by its output limits

60. Override - MISO Controller
Automatic regulation of the process input to maintain one process output at target without violating a constraint on another output is provided by override control.
Max Value
Override Controller
Information
Needed to
Tune Controllers
<
Controller
Manipulated
Process

61. Over-ride Control
Control Meas
Constraint Meas
Unmeasured Disturbance

62. Cascade Control Secondary Primary Controller Controller Process 2
Manipulated
Disturbance
Disturbance

63. Cascade Control
Sec Meas
Pri Meas
Unmeasured Disturbance

64. Cascade –Dynamic Reset
Select FRSIPID_OPTS for Dynamic Reset Limit in the primary of the cascade to automatically compensates for poor response of the secondary loop.
The CONTROL_OPTS in the secondary must be set for Use PV for BKCAL_OUT for Dynamic Reset Limit to provide benefit.

65. Split Range Control
Sec Meas
Unmeasured Disturbance

66. Split Range Control AI PID SPLT AO AO TIC 104 FY 104 IP 104A IP 104B
TT104
TIC104
IP104A
FY104 AO
IP104B
TIC
104 FY
104 IP
104A IP
104B TT
104
HEATER
COOLER 44 44

67. Split Range Output (FY104)
Valve
Position
(% of Span)
TIC104 Output (% of Span)
100
Cooling (IP104B)
Heating (IP104A) TK 23 45 45

68. Splitter Block OUT_1 OUT_2 SP 100 LOCK_VAL “holds ” “is zero OUT_ARRAY
100
OUT_1
OUT_2
LOCK_VAL
“holds ”
“is zero
OUT_ARRAY
IN_ARRAY 47

69. Splitter Block Total Cv and gain of 0-50, 50-100% split ranged valves.47

70. Splitter Block Total Cv and gain of 0-50, 25-100% split ranged valves.47

71. Ratio Control
IN_1 may be a flow measurement (wild flow) or setpoint of another loop
Ratio based on SP will not reflect deviations in flow from setpoint (resulting in incorrect ratio)

72. Addressing Lag Dominated processes – Fuzzy Logic Control
The greatest advantage of fuzzy logic control is evident when it is applied to processes with insignificant dead time in order to accelerate the speed of control while retaining a high-quality level of control.
Where PID controller does not meet expectations, the fuzzy logic control may be considered as an alternative.

73. Fuzzy Logic Control vs PID
Setpoint Change Load Disturbance
FLC
FLC
PID
PID
FLC
FLC
PID
PID
Notice at both SP change and at load disturbance the FLC output change is more dramatic than PID. Resulting in faster return to SP. Also notice as PV approaches SP, the FLC exhibits less overshoot.

74. DeltaV Fuzzy Implemented As A Function Block
Pre-defined Fuzzy
Logic Control

75. Addressing Difficult Dynamics and Process Interaction - Model Predictive Control
Fully integrates DeltaV Historian and off-line process identification
setup is trivial
Model can be easily updated.
MPC with DeltaV is easy

76. MPC -Addressing Difficult Dynamics
TemperatureProcess (1X1)
MPC

77. MPC -Addressing Difficult Dynamics

78. MPC -Addressing Difficult Dynamics

79. Automated process testing to identify the process model

80. Step Response Model

81. Verification of identified model

82. Testing of control using simulated environment

83. Operator interface to MPC
Future Values of control

84. MPC Replacement for PID with Feedforward
Process (2X1)
MPC
Measured
Disturbance

85. MPC Replacement for PID with Feedforward

86. MPC Replacement for PID Overrride
Process (1X2)
MPC
Constraint
UnmeasuredDisturbance

87. MPC Replacement for PID Overrride

88. Addressing Process Interaction
Process (2X2)
MPC

89. Addressing Process Interaction

90. Addressing Process Interaction

91. Layering MPC on Existing Strategy
RCAS_IN
RCAS_OUT
PID AO AI
Process (1X1)
Unmeasured Disturbance

92. Exposing RCAS_IN & RCAS_OUT
Right click on the control or AO block to expose the RCAS_IN as an Input parameter and RCAS_OUT as an Output parameter.

93. Layering MPC on an Existing Strategy

94. Example DeltaV Predict Installations
Control Application Industry Location
Evaporator Chemical South Africa
Lime Kiln Pulp&Paper Canada
Pipeline Gas Blending Power Florida
Bleach Plant Pulp&Paper Canada
pH Control Pulp&Paper US
Distillation Column Pharmaceutical Puerto Rico

95. Summary
The function blocks in DeltaV may be used to address plant requirement.
The DeltaV MPC and Fuzzy Function blocks may be used to better address difficult dynamic and process interaction
Your feedback on this presentation and input on future control topic you would like to see presented at Emerson Exchange are always appreciated.

96. Learning More About DeltaV Advanced Control
Book was inspired by DeltaV Advanced Control Products. This book was introduced at ISA2002 may also be ordered through ISA, Amazon.com or at EasyDeltaV.com/Bookstore
The application sections include guided tours based on DeltaV Advanced Control Products
CD provides an overview video for each section and examples. Copies of the displays, modules, and HYSYS Cases are included on the CD.

97. DeltaV Predict and other DeltaV Advanced Control Products
Overview - Courses 7201, 7202, & 7203
These courses, beginning with the 7201, overview all of the major DeltaV advanced control tools. Courses 7202, & 7203 each drill deeper into a specific advanced control product and its application.
DeltaV advanced controls are unique in the process control industry, in that users do not need detailed knowledge of the underlying mathematical principles to successfully apply the DeltaV advanced controls technology.
Course # 7201
DeltaV Advanced Controls
Overview
Course # 7202
Course # 7203
DeltaV PredictPro
DeltaV Neural
Implementation
Implementation

98. Emerson Exchange – Advanced Control Presentations
Effectively Addressing Control Applications Monday 1:00-2:45, 3:00-4:45, Dallas 5-6-7
Addressing Multi-variable Process Control Applications Tuesday 8-10, Grapevine D
Lime Kiln Optimization Using Supervisory APC – “Model Predictive Control” & DeltaV Wednesday 3:25-4:10, Texas 4
Adaptive Control – Better Control with No Tuning!, Thursday 1:40 – 2:25 Grapevine A
DeltaV and Model Predictive Control - A primer on DeltaV Predict and PredictPro Thursday 8:40 – 9:25 Grapevine A
Field Experience in Property Estimation, Friday, 8:00 - 9:40am & 9: :35am Texas 5
Utilizing adaptive Control Friday, 8:00 - 9:40am & 9: :35am Texas 4