Many functions of the DeltaV system are unique in the process industry. In this presentation we explore and discuss innovative features of the DeltaV PID and embedded Advanced Control products that can be applied to improve control performance. In particular, PID options are addressed that enhance cascade and override applications and allow effective single loop control using a sampled or wireless measurement. Application examples are used to illustrate how MPC can be easily added and commissioned online with no changes in the existing control strategy. Also, continuous data analytics is used an example that illustrates how future tools will enable improvements to be made in plant operations.

1. Utilizing DeltaV Innovations to
Improve Control Performance

2. Presenters

Terry Blevins

Willy Wojsznis

3. Introduction
The DeltaV control system includes many functions that are unique in
the process industry. Significant value provides embedded into DCS
advanced control functionality which effectiveness and ease of use
was proven over many years and numerous applications, for example:

Insight – integrated with control loop tool for loop performance and
loop state evaluation, loop auto and adaptive tuning and loop
operation reporting

PredictPro – Model Predictive Control tools for process model
identification, controller development and operation. The three
functions blocks: MPC, MPCpro and MPCPlus support various
configuration sizes and functionalities

Fuzzy Logic Control – function block and application for FLC
controller development

4. Advanced Control Foundation



Published by ISA in
2012
Available through ISA
web site or Amazon
Addresses all the
advanced control tools
in DeltaV

5. Advanced Control Foundation Web Site

6. Advanced Control Foundation (Cont)

7. Advanced Control Foundation (Cont)

8. Advanced Control Foundation (Cont)

9. Advanced Control Foundation (Cont)

10. Advanced Control Foundation (Cont)

11. Control Loop Foundation



Published by ISA in
2010
Print and eBook version
available through ISA
web site or Amazon
Addresses all the tools
that have traditionally
been used by a control
engineer in the process
industry

12. Control Loop Foundation Web Site

13. Control Loop Foundation Web Site (Cont)

14. Control Loop Foundation Web Site (Cont)

15. Control Loop Foundation Web Site (Cont)

16. Control Loop Foundation Web Site (Cont)

17. Control Loop Foundation Web Site (Cont)

18. Control Loop Foundation Web Site (Cont)

19. Control Loop Foundation Web Site (Cont)

20. Control Loop Foundation Web Site (Cont)

21. Control Loop Foundation Web Site (Cont)

22. Control Loop Foundation Web Site (Cont)

23. Control Loop Foundation Web Site (Cont)

24. Control Loop Foundation Web Site (Cont)

25. Control Loop Foundation Web Site (Cont)

26. Control Loop Foundation Web Site (Cont)

27. Control Loop Foundation Web Site (Cont)

28. Control Loop Foundation Web Site (Cont)

29. Control Loop Foundation Web Site (Cont)

30. Control Loop Foundation Web Site (Cont)

31. Agenda
In this session we explore features that are less known, but
they may be very effectively used to improve both traditional
and advanced control applications and economic
performance.
 PID options that enhance cascade and override
applications and allow effective single loop control using a
sampled or wireless measurement
 Adding and commissioning Model Predictive Control
(MPC) on-line with no changes in the existing control
strategy
 Future products and tools that enable improvements in
plant operations using data analytic techniques
 Q&A

32. Background – DeltaV Reset
SETPOINT
+
KP
+
-
+
PROCESS
Academic Explanation
SETPOINT
+
KP
-
+
+
PROCESS
FILTER
Industrial
implementation

Automatically
provides anti-reset
windup protection
 Required for
preferred
implementation of
override and
cascade control
Industrial Implementation – DeltaV, Invensys, others

33. Background - DeltaV PID (Cont)

The reset component of
the PID block is
implemented with a
positive feedback
network

Reset windup is
automatically prevented
under limit conditions
associated with process
saturation conditions

Dynamic Reset Limit
selection in
FRSIPID_OPTS
enables use of
BKCAL_IN in the reset
calculation

34. Cascade Control

Cascade control may be
applied when a process is
composed of two or more
(sub)processes in series

Any change in the
manipulated input to the first
process in the series will
impact the output of the other
processes

The output of each process in
the series is the controlled
parameter of the PID
associated with that process

35. Example – Boiler Steam Temperature



The temperature of steam
supplied by utility boilers
can have a large impact
on process operation
In an attemperator, steam
is mixed with water to
regulate the temperature
of steam exiting the boiler
The spray valve is used
to adjust the flow rate of
water introduced into the
attemperator

36. Cascade Control Implementation



Cascade control may be implemented when a process is made up of
a series of processes
Also, one PID block is required for each process in the series.
For normal operations, the master loop is maintained in Automatic
mode and the slave loop is operated in Cascade mode
Slave
Cascade Mode
Master
Automatic Mode

37. Cascade Control – Use of External Reset

The PID block is designed to
support dynamic reset
limiting, also commonly know
as external reset

The performance of cascade
control loop may be
improved by enabling this
option in the primary loop

In the secondary loop the
CONTROL_OPTS for Use
PV for BKCAL_OUT should
be selected

38. Cascade Control Implementation



Cascade control may be implemented when a process is made up of
a series of processes
Also, one PID block is required for each process in the series.
For normal operations, the master loop is maintained in Automatic
mode and the slave loop is operated in Cascade mode
Slave
Cascade Mode
Master
Automatic Mode

39. Override Control

The implementation of override
control is often the most effective
way to maintain the process within
its operating constraint limits

In general, override control may
be implemented using two or
more PID blocks and a control
selector block

Under normal operating
conditions, the controlled
parameter is maintained at
setpoint by the selected PID. The
override PID takes an active role if
the value of the constraint variable
approaches its setpoint.

40. Override Example – Compressor

In this example, a large natural
gas compressor is powered by an
electric motor. Under normal
operating conditions, the gas flow
to the compressor is regulated to
maintain a constant discharge
pressure

However, the load on the electric
motor changes as the gas flow
rate changes

To avoid the current exceeding
some limit, the motor current is the
constraint variable and the
discharge pressure is the
controlled parameter in the
override control strategy

41. Override Control Implementation

The control selector block
supports upstream and
downstream back
calculation connections

Numbered pairs of input
and back calculation
outputs of the control
selector should be
connected to the same PID

Dynamic reset should
always be enabled in the
PID blocks involved in the
override control

42. Recovery from Process Saturation

A process saturation condition exists when the setpoint of a
PID can not be maintained and the PID output is limited

When operating conditions change that allow the process to
recover from a process saturation condition, then improved
response is provided by enabling the FRSIPID_OPTS option
for PIDPlus

The PIDPlus option in DeltaV v11 provides improved control
response for recovery from process saturation

43. PIDPlus Feature of DeltaV PID

The PIDPlus feature of the
DeltaV PID (DeltaV v11
and higher) is enabled
through the
FRSIPID_OPTS
parameter

When PIDPlus is enabled
then special behavior is
provided to address:
– Control using Wireless
measurement or
sampled inputs
provided by an analyzer
– Recovery from process
saturation conditions.

44. Recovery From Process Saturation

The recovery of the
PID from process
saturation is critical in
many continuous and
batch applications

One way of
addressing
recover from
process saturation
is to incorporate
preload switching
to the PID.

45. Recovery From Process Saturation

PI Control
PI Control with Variable Pre-load
By utilizing a variable
preload (enabled by the
PIDPlus selection) when
the PID output is limited
for an extended period of
time (process saturation),
it is possible to minimize
setpoint overshoot on
recovery from saturation

46. PIDPlus for Recovery From Process
Saturation

The PIDPlus option in DeltaV v11 provides
improved control response for recovery from
process saturation
– PIDPlus option added in DeltaV v11.3 to improve control
response for recovery from process saturation.
– Anticipation action can be adjusted using the PID
parameter RECOVERY_FILTR. Value of 1 = No
anticipation, Value of 0 = full anticipation utilized to avoid
SP overshoot when recovering from process saturation

47. Example – Steam Temperature Control using
PIDPlus


SP Overshoot
When boiler firing rate is
reduced, then the spray value
should be cut back as the outlet
temperature drops

Standard PID
If steam generation exceeds the
attemperator capacity then the
boiler outlet steam temperature
will exceed the outlet setpoint
with the spray valve fully open
When the FRSIPID_OPTS for
PIDPlus is enabled then the
valve moves before PV reached
DeltaV PIDPlus
50% Drop in
steam
generation
SP – providing improved
response

48. Example - Air Compressor Anti-Surge

The function of the surge
control system is to
detect the approach to
surge and provide more
flow to the compressor
through opening the
recycle valve to avoid
surge

Opening of the vent
valve provides more flow
and reduces compressor
head, to move the
compressor away from
its surge point

49. Control Response – PIDPlus Disabled
60% Reduction
in Air Demand
Surge Margin
Surge Line
Exceeded

50. Control Response – PIDPlus Enabled
Surge Margin
60% Reduction
in Air Demand
Surge Margin
Maintained

51. PIDPlus for Wireless Control
The Challenge – Control Using Wireless

Transmitter power consumption is minimized by reducing the
number of times the measurement value is communicated.

Conventional PID execution synchronizes the measurement
value with control action, by over-sampling the measurement by
a factor of 2-10X

The rule of thumb to minimize control variation is to have
feedback control executed 4X to 10X times faster than the
process response time (process time constant plus process
delay)

The conventional PID design (i.e., difference equation and ztransform) assumes that a new measurement value is available
at each execution and that control is executed on a periodic
basis

52. Sampling of Wired Measurement

53. *WirelessHART Communication
Window communication is the preferred method of communications for
control applications. A new value will be communicated only if:

The magnitude of the difference between the new measurement
value and the last communicated measurement value is greater that
a specified trigger value

Or if the time since the last communication exceeds a maximum
update period
Thus, the measurement is communicated only as often as required to
allow control action to correct for unmeasured disturbances or response
to setpoint changes.
For Windowed mode you must specify an update period, a maximum
update period, and a trigger value.
*HART 7 specification that has been adopted as an international standard, IEC 62591Ed. 1.0.

54. PID Modification for Wireless Control


To provide the best control for a non-periodic measurement, the PID
must be modified to reflect the reset contribution for the expected
process response since the last measurement update
Control execution is set faster than measurement update. This
permits immediate action on setpoint change and update in faceplate

55. PIDPlus Using Wireless Transmitter vs.
Conventional PID and Wired Transmitter
Lambda Tuning ʎ = 1.0
Communication Resolution = 1%
Communication Refresh = 10sec
Setpoint
PIDPlus
Control
Measurement
PID
PIDPlus
Control Output
PID
Unmeasured
Disturbance

56. Control Performance Difference


Communications transmissions are reduced by over 96 % when
window communication is utilized
The impact of non-periodic measurement updates on control
performance as measured by Integral of Absolute Error (IAE) is
minimized through the PID modifications for wireless
communication

57. PID Performance for Lost Communications


The Conventional PID provides poor
dynamic response when wireless
communications are lost
The PID modified for wireless control
provides improved dynamic response
under these conditions

58. Wireless Communication Loss
– During Setpoint Change
Setpoint
PIDPlus
Control
Measurement
PID
PIDPlus
Control Output
PID
Communication Loss

59. Wireless Communication Loss
– During Process Disturbance
PIDPlus
Setpoint
Control
Measurement
PID
PIDPlus
Control Output
PID
Communication Loss

60. Example - Separations Research Program,
University of Texas at Austin

The Separations Research Program
was established at the J.J. Pickle
Research Campus in 1984

This cooperative industry/university
program performs fundamental
research of interest to chemical,
biotechnological, petroleum refining,
gas processing, pharmaceutical, and
food companies

CO2 removal from stack gas is a
focus project for which WirelessHART
transmitters were installed for
pressure and steam flow control

61. PC215 On-line Column Pressure Control

The same dynamic
control response
was observed for
SP changes

Original plant PID
tuning was used
for both wired and
wireless control
Wired Measurement
Used in Control
GAIN=2.5
Wireless Measurement
Used in Control
RESET=4
RATE=1

62. Control Performance – Wired vs Wireless


Test #1
Test #2
Comparable control as
measured by IAE was
achieved using
WirelessHART
Measurements and
PIDPlus vs. control with
wired measurements and
PID
The number of
measurement samples with
WirelessHART vs Wired
transmitter was reduced by
a factor of 10X for flow
control and 6X for pressure
control – accounting for
differences in test duration

63. Model Predictive Control (MPC)

Model Predictive Control (MPC) was developed by Shell Oil in the
1970s to improve the control of large interactive processes such as
refinery distillation columns
– DeltaV Predict and PredictPro may be used to implement MPC and may be
used to address control of single input-single output (SISO), as well as
multiple input-multiple output (MIMO) processes
– In the DeltaV system MPC runs in the DeltaV controller and may execute as
fast as 1/sec – making it possible to apply MPC to small processes that
have historically been controlled using multi-loop techniques
– No license is required to implement MPC in DeltaV if only one (1)
manipulated process input is utilized in the control strategy
– MPC may be used to more effectively control processes that are dominated
by deadtime and difficult dynamics such as inverse response than is
possible with PID
– The multi-variable constraint handling capability of model predictive control
may often be used to increase a plant’s production rate

64. Model Predictive Control(MPC) in DeltaV
Three versions of Model Predictive Control (MPC) are provided in DeltaV

DeltaV Predict (DeltaV v7 or later) – Addresses processes as large as 8x8.
Pusher capability is provided to allow process throughput to be maximized by
maintaining the process at its operating constraints. No cost if only one(1)
manipulated parameter. Module runs in Controller or Application station

DeltaV PredictPro DeltaV v9 or later) - For larger, more complex process as
large as 40x80 . Linear Program (LP) embedded to support process
optimization based on user defined control objective. Module runs in
Controller or Application station

DeltaV PedictPlus (DeltaV v12 or later)– Adds greater capability to address
changing operating constraints and integrating processes. Module runs only
in Application station

65. Addressing Difficult Dynamics

The control performance achieved may not be satisfactory when PID feedback
control is applied to a deadtime-dominant process. In such cases, control
performance may be improved by replacing PID feedback control with Model
Predictive Control

66. Using MPC to Address Process Interactions

When a process is characterized by multiple manipulated process inputs and
multiple controlled process outputs, there is a potential for process interaction

The interaction of the manipulated inputs and controlled outputs is automatically
accounted for by MPC

67. Layering MPC onto an Existing Strategy

An easy way to initially learn about MPC and to gain experience commissioning
MPC blocks is to layer MPC blocks on top of traditional PID-based control strategies

The RCAS_IN and RCAS_OUT of the Analog Output block allow the MPC block to
be in control when the Analog Output block mode changes from Cas to RCas

68. Integrating Advanced Control Into a DCS

When advanced control is
embedded in the distributed
control system (DCS), the
plant operator has a single
window interface with
consistent system interaction
and single log-in and span of
control

If the DCS does not support
advanced control, then the
advanced control
applications must be layered
onto the DCS. Several
approaches may be taken
depending on the DCS
support for layered
applications

69. Future Data Analytic Products and Tools
Continuous List
1. Batch Data Analytic product
in DeltaV v12 – presented and
discussed in several
workshops
2. Continuous Data Analytic
prototype has been
developed and tested in two
plants: Lubrizol and
Huntsman
3. Operator user interface can
be common for both products
as it was tested in the
prototype
Batch List
4. The focus in this presentation
will be on continuous data
analytic based on the field
trial results

70. Continuous Data Analytic Functionality
Quality prediction
Fault detection
Fault identification
Fault diagnosis
Continuous Data
Analytic predicts
on-line product
quality and
monitor process
operation.
Process operation
faults are
detected,
identified and
diagnosed

71. Data Analytics Workshops
Learn more about continuous and batch data analytics by
accessing workshop presentations at this year’s Emerson
Exchange:
 8-4775 Challenges and Solutions in Data Analytics
Application for a Distillation Column
 8-4342 How to install Batch Analytics on a non-V12
DeltaV system
 8-4240 Application of On-line Data Analytics to a
Continuous Process Polybutene Unit

72. Where To Get More Information

Terrence Blevins, Willy K. Wojsznis and Mark Nixon Advanced Control Foundation,
ISA, 2013

Dunia, R., Edgar, T., Blevins, T., Wojsznis, W., Multistate PLS for Continuous
Process Monitoring, ACC, March, 2012

J.V. Kresta, J.F. MacGregor, and T.E. Marlin., Multivariate Statistical Monitoring of
Process Operating Performance. Can. J. Chem.Eng. 1991; 69:35-47

Dunia, R., Edgar, T., Blevins, T., Wojsznis, W., Multistate Analytics for Continuous
Processes, Journal of Process Control, 2012

MacGregor J.F., Kourti T., Statistical process control of multivariate processes.
Control Engineering Practice 1995; 3:403-414

Kourti, T. Application of latent variable methods to process control and multivariate
statistical process control in industry. International Journal of Adaptive Control and
Signal Processing 2005; 19:213-246

Kourti T, MacGregor J.F. Multivariate SPC methods for process and product
monitoring, Journal of Quality Technology 1996; 28: 409-428

73. Thank You for Attending!
Enjoy the rest of the conference.