Utilizing DeltaV Advanced Control Innovations to Improve Control Performance

Utilizing DeltaV Innovations to Improve Control Performance

Presenters

Terry Blevins

Willy Wojsznis

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

Advanced Control Foundation

Published by ISA in

2012

Available through ISA

web site or Amazon

Addresses all the

advanced control tools

in DeltaV

Advanced Control Foundation Web Site

Advanced Control Foundation (Cont)





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

Control Loop Foundation Web Site

Control Loop Foundation Web Site (Cont)


















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

Background – DeltaV Reset

Industrial

implementation

Automatically

provides anti-reset

windup protection

Required for

preferred

implementation of

override and

cascade control

KP SETPOINT

PROCESS + + + -

KP SETPOINT

FILTER

PROCESS + + + -

Academic Explanation

Industrial Implementation – DeltaV, Invensys, others

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

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

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

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

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

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

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.

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

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

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

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.

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.

Recovery From Process Saturation

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

PI Control with Variable Pre-load

PI Control

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

Example – Steam Temperature Control using PIDPlus

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 boiler firing rate is

reduced, then the spray value

should be cut back as the outlet

temperature drops

When the FRSIPID_OPTS for

PIDPlus is enabled then the

valve moves before PV reached

SP – providing improved

response

Standard PID DeltaV PIDPlus

SP Overshoot

50% Drop in

steam

generation

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

Control Response – PIDPlus Disabled

Surge Margin

60% Reduction

in Air Demand

Surge Line

Exceeded

Control Response – PIDPlus Enabled

Surge Margin

60% Reduction

in Air Demand

Surge Margin

Maintained

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 z-

transform) assumes that a new measurement value is available

at each execution and that control is executed on a periodic

basis

Sampling of Wired Measurement

*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.

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

PIDPlus Using Wireless Transmitter vs. Conventional PID and Wired Transmitter

Control

Measurement

Control Output

Unmeasured

Disturbance

Setpoint PIDPlus

PIDPlus

PID

PID

Lambda Tuning ʎ = 1.0 Communication Resolution = 1%

Communication Refresh = 10sec

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

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

Wireless Communication Loss – During Setpoint Change

Communication Loss

PID

PIDPlus

PIDPlus

PID

Control

Measurement

Control Output

Setpoint

Wireless Communication Loss – During Process Disturbance

Communication Loss

PIDPlus Setpoint Control

Measurement

Control Output

PID

PIDPlus

PID

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

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

GAIN=2.5

RESET=4

RATE=1

Wired Measurement

Used in Control

Wireless Measurement

Used in Control

Control Performance – Wired vs Wireless

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

Test #1 Test #2

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

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

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

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

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

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

Future Data Analytic Products and Tools

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

4. The focus in this presentation

will be on continuous data

analytic based on the field

trial results

Batch List

Continuous List

Continuous Data Analytic Functionality

Continuous Data

Analytic predicts

on-line product

quality and

monitor process

operation.

Process operation

faults are

detected,

identified and

diagnosed

Quality prediction

Fault detection

Fault identification

Fault diagnosis

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

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

Thank You for Attending!

Enjoy the rest of the conference.