Post on 28-Jan-2015
description
Advanced Control Foundation - Tools & Techniques
Terry Blevins – Principal Technologist
Willy Wojsznis – Senior Technologist
Mark Nixon – Director, Research
Presenters
Terry Blevins
Willy Wojsznis
Introduction
Over the last 10 years significant improvements made in
advanced control tool capabilities and in user interfaces,
improvements that make it easier to design and commission
advanced control solutions.
Also since then, new advanced control applications have been
introduced for batch and continuous processes.
The book Advanced Control Foundation – Tools, Techniques,
and Applications provides a fresh look at some of the latest
advanced control technologies that are available to the process
industry. A web site for the book allows the solutions to the
book’s workshops to be viewed using a web browser.
Areas to be Addressed
This session will focus on how advanced control technique may be
used to improve process operations. Areas that will be addressed
are:
Maximizing Return on Control System Investment
Evaluating Control System Performance
On-demand Tuning
Adaptive Tuning
Fuzzy Logic Control
Intelligent PID
Neural Networks for Property Estimation
Batch and Continuous Data Analytics
Simple MPC
MPC Integrated with Optimization
On-line Optimization
Process Simulation, Integrating Advanced Control Into a DCS
Basis for Presentation
Material that will be presented
is based on Advanced Control
Foundation.
This book was published in
Sept 2012 by ISA and
addresses the advanced
control products in the DeltaV
control system (or targeted for
a future DeltaV release).
The book is available in the
ISA bookstore or may be
purchased on-line through ISA
- see
Basis for Presentation (Cont)
The solution to workshop included in the book may be viewed using
your web browser- see http://www.advancedcontrolfoundation.com/
Maximizing Return on Control System Investment
By reduce process
variation, production
rate or quality
parameter targets
may be shifted. This
simple concept often
justifies control
upgrade.
When target control
performance cannot
be achieved using
Single-loop PID
feedback and multi-
loop traditional control
techniques then
advanced control
techniques may be
required.
Example
Evaluating Control System Performance
The first step in
improving control
is to insure all
controls are
operating as
designed.
Performance
monitoring tools
may be used to
quickly identify
when control is
not being utilized
i.e. control is on
manual.
Evaluating Control System Performance (Cont)
Report generation is
an important feature
of performance
monitoring tools.
Reports can be used
to gain the
management
support that is
required to address
low control utilization
or to determine the
source of excessive
process variation
that impacts
production or
product quality.
Evaluating Control System Performance (Cont)
A plant equipped with
the latest control
systems and field
instrumentation may
still be found to have
low control utilization.
Often the key to getting
control loops back on
automatic is for plant
management to be
aware of the low
control utilization and
its impact on product
quality and production
rate.
Resolving Problems that Impact Control Utilization
On-demand Tuning
Where low control
utilization is due to
PID tuning, then
On-demand tuning
may be used to
commission the
loop.
Capturing process
dynamics in the
field (controller or
device) allows
better process
identification,
particularly for the
fastest loops.
On-demand Tuning (Cont)
When using an on-
demand tuning
application,
consider that the
process gain may
change with the
operating
conditions.
For robust control,
tuning should be
based on the
operating
conditions that
provide maximum
process gain.
Adaptive Tuning
In some cases, the
tuning established
at one operating
point may not
provide the best
control for the full
operating range.
Adaptive tuning
allows the process
gain and dynamics
to be automatically
identified and used
in control.
Viewing Identified Models
Predefined Fuzzy Logic Control Function Block
Fuzzy Logic Control
For some specific
process applications,
fuzzy logic control
enables faster setpoint
recovery with less
overshoot than PID
control for both
setpoint and load
changes
Fuzzy logic is best
suited for controlling
processes
characterized by large
time constants and
little or no deadtime
Fuzzy Logic Control Workshop
Demo
Intelligent PID
The PIDPlus provides
quicker recover from
process saturation.
Also, the PIDPlus allow
the non-periodic, slow
measurement values
provided by a wireless
device to be used in
closed loop control.
Recovery From Process Saturation
Control Using WirelessTransmitter
Compressor Surge Control
Compressor Surge Control (Cont)
Control Response with Preload Applied
Control Response with Variable Preload
Bioreactor with Wireless Instrumentation
Neural Networks for Property Estimation
When a product quality
measurement is available
only from the lab, it is
often possible to use
upstream measurements
to calculate an estimated
value.
To address the non-
linear response of
product quality
parameters to changes in
process inputs, the
estimator can be based
on a neural network
model.
Continuous Digester Example
Batch and Continuous Data Analytics
Through the use of on-line
data analytics, it is
possible to provide:
Product quality
predictions which allow
quality problems to be
identified while there is
time to take corrections
action.
Detection of abnormal
process operation
and/or equipment
problems and support
of root cause analysis
Continuous Example – Static Mixer
Batch Example
Simple MPC
MPC has proven
advantages over multi-loop
PID control techniques in a
variety of small applications
that are characterized by:
Long process delay and
interaction,
Measured disturbances
or constraints
Production is limited by
process input(s)
Embedded MPC
capability in the control
system.is an advantage
Replacing PID with MPC
One Measured Disturbance Input
MPC Constraint Control
MPC Integrated with Optimization
For larger, more complex
applications that are used in
batch or continuous
processing, the plant
operation objective(s) may be
best met using MPC
integrated with optimization.
Such applications are often
characterized by numerous
operating constraints and the
need to address broader
operating objectives, such as
maximizing throughput while
minimizing production costs
Installation examples are
included in this chapter
MEE Process
CTMP Process
CTMP Refiner Process Step Response
On-line Optimization
Examples are provided of
on-line LP (linear
programming) optimizer to
minimize the cost of power
generation.
On-line operation was
achieved by using MPC with
an integrated optimizer;
however,the MPC
functionality was disabled.
The economic benefits
achieved from on-line
optimization applications
indicate use of online
optimization will be
expanding in the coming
years.
Workshop for On-line Optimization
Process Simulation
Dynamic process
simulation can be a
useful tool when working
with basic as well as
advanced control
techniques
Such simulations can
easily be created using
the tools that exist in
most modern control
systems.
The steps for developing
process simulations
starting with the P&ID
are described in detail.
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.
Business Results Achieved
The economics of plant operation can be impacted by process
variation when production is limited by equipment capacity or when
maximum production and operating efficiency are achieved at a
specific operating condition.
Through the application of advanced control techniques such as
performance monitoring, on-demand and adaptive tuning, fuzzy
logic control, intelligent PID, and MPC, it is often possible to
reduce variations in process operation and to shift and maintain
the plant to a more efficient point of operation.
Data analytics may be used to improve batch and continuous
process operation through the on-line prediction of quality
parameter and fault detection.
Summary
Advanced control techniques and tools should be
considered when:
The control objectives cannot be achieved through
the improvement of traditional control techniques.
Traditional control strategies are more difficult to
maintain at optimal performance because of their
complexity.
Advanced control products are available as embedded
applications within modern process control systems or
as layered applications that may be added to older
control systems.
Where To Get More Information
Web site for Advanced Control Foundation workshop Solutions
- see: http://www.advancedcontrolfoundation.com/
Information on the Book
- see: http://modelingandcontrol.com/2012/08/advanced-
control-foundation-coming-soon/
ISA Web Site on the Book
- see: http://modelingandcontrol.com/2012/09/advanced-
control-foundation-isa-web-site/