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/