what you will learn in this seminar - pronamics control … tuning for first order processes...

Lambda Tuning for First Order Processes

Why Process Variability is Bad

The Role of the Control Loop in Reducing Process Variability

Current Industry Status – Control Loop Performance

Process Dynamics – First Order Processes

Lambda Tuning the Control Loop – First Order Processes

Examples of Proper / Improper Controller Tuning

What You Will Learn in This Seminar


Variability Reduces Production Rate:

Increased Off Grade

Reduced Operating Efficiency

Equipment Limitations

The Impact of Process Variability

Tightened Distribution

Setpoint Response and Regulation

Slow variability cycles

Fast variability cycles

Variability Increases Operating Cost:

Raw Material Application is Unnecessarily High Due to Conservative Targets

Raw Material Usage Inefficiency


Improved Process Performance

Product Quality Improvements

Production Increases

Chemical, Material, Energy Savings

Reliability and Maintenance

High Process variability Leads to Excessive Equipment Wear and Premature Failure

The Benefits of Reduced Variability

Chemical, Material and Energy Cost

Productivity and Yield

Product Quality

Operating Average ShiftHard Spec.Limit

OperatingAverage

Hard Spec.Limit

OperatingAverage

Minimizing variability creates the potential for

production increases and operating cost

reduction through target shifting


The Role of the Control Loop

The average installed cost of the control loop is $40,000. The controller has a key role in minimizing process variability The primary function of the control loop is to respond to process upsets/disturbances to keep the process on target. It is particularly important to minimize variability in the key processes – those that affect process efficiency and product quality.

CausticWater

MAKE-DOWN

CHEST

Dilution Header

Slow variability is

attenuated by the feedback

control loop Disturbances

Process

Dynamics

PID

ControllerProcess

Variable

PVCO

-

+S

+

+ES

L

SP


Current Industry Status

The majority of control loops do not deliver maximum value in reducing variability, and a significant percentage of loops act to increase process variability.

There are some straightforward indicators that the control loop is not functioning properly.

The Loop is in Manual Mode

The Output Signal is Fixed at 0% or 100% (i.e. Saturated)

There is Dead-Time in the Set Point Response of Flow and Pressure Loops

The Set Point Response is Very Slow or Oscillatory

There are Slow Cycles in the Process

The Loop Components Wear Out Frequently

The Trend Lines are Very Smooth



The E&I mechanic fixes control loop problems identified by the operators. The Operator often does not flag control related problems until the situation has become serious.

The majority of E&I Mechanics use ‘guesswork’ tuning. Control loop optimization cannot be achieved “guesswork”.

Successful tuning is not well defined?

The Loop is Still in Auto Mode the Day After I Tuned It

The Operator Says it’s OK, so I Can Go Home Now

The Process Variable Stays Close to Set Point



The Primary Reasons for Poor Loop Performance:

Loop Health Problems

Control Valve Backlash and Stiction

High Sensor Noise

Sensor Reliability

Poor Control Strategy Design

The Strategy Does Not Attenuate Known External Disturbances Effectively

The Strategy Does Not Account for Highly Non-Linear Dynamics

Unnecessary Control Loops Result in Interaction

Loop Design Resulting in Poor Process Dynamics

Excessive Process Dead-Time Due to Sensor Positioning

Process Gain is Too High Due to Conservative Engineering Practices

Process Time Constant is Too High Due to Unnecessary Filtering

Poor Controller Tuning

Lack of a Structured Tuning Method

The Process and Control Objectives are Not Clearly Defined


Process Dynamics and PID Controller Tuning

Optimizing controller tuning is a straight-forward and inexpensive first step in process optimization.

Controller Tuning – General Approach

Define the Process and Control Objectives of the System

Measure the process dynamics by conducting open loop bump tests.

Determine if there are Outstanding Control Loop Health Issues

Estimate the Degree of Non-Linearity in the Control Loops

Develop a tuning strategy that minimizes variability in the key process loops. Remember: It is not possible to minimize variability in all control loops. Strive to minimize variation in the key processes.

Implement / Validate the Tuning


Measuring the Process Dynamics

• The simplest method for determining the dynamic response of a

process is to conduct a Bump Test. A Bump Test is performed by

placing the controller in manual mode and stepping the controller

output. The dynamics are determined by measuring the process

response to the output step.

• Multiple and bi-directional output steps should be conducted.

Several open/closed steps should be performed at intervals such

as 1%, 2% and 4%.


Process Dynamics – First Order Plus Dead-Time

The process response is fitted to a process model. A first order response is described by the Process gain (KP), the Time Constant

(tP) and the Dead-Time θP .

Steady State Process Gain, Kp How Far PV Travels

Process Time Constant, tP How Fast PV Responds

Dead-Time, Өp How Much Delay Before PV Responds

DCO

DPV

95.0 86.5

Time to

Steady State

t t t t

98.2

63.2

KP = DPV

DOP

Process Gain

qP

Open Loop Bump Test

dPVp + PV = Kp CO(t θp)

dtt


Process Dynamics – Good Targets

An important insight is that the PID control performance is largely dictated by the process dynamics. Control loops with slow dynamics will usually require slow tuning. Attempting to speed up the PID controller beyond the inherent loop dynamics usually ends up increasing process variability.

Process Type Kp,

%Span/%CO

Flow, Hydraulic 0.5 to 2.0

Pressure,

Hydraulic

0.5 to 2.0

Concentration 0.2 to 1.0

A low process gain limits the control range. Investigate valve sizing. A high process gain limits the process resolution. Investigate pump and valve sizing.

A high time constant can limit

control loop performance. Check

sensor filtering and valve response

time.

Dead-Time is very destabilizing and

should be minimized. Check sensor

positioning, valve response.


Process Dynamics – Example

Process Gain = 6.37 lps / %CO or 2.55 %SPAN/%CO

Time Constant = 0.025 min or 1.5 seconds

Dead-Time = 0.0042 min or 0.25 seconds


Tuning is the manipulation of the controller Proportional (P), Integral (I) and Derivative (D) terms in order to produce the desired response

KC - Proportional Gain, %CO/%SPAN

tI - Integral Time, seconds or minutes

Disturbances

Process

DynamicsController Process

Variable

PVOP

-

+S

+

+ES

L

SP

Kc

CO=CO + Kc e(t) + e(t)dtbias

t I

Controller Tuning – What is it?


Controller Tuning – Popular Methods

PV

P

V

Time

PV

Fast Response

Robust

Slow Response to Disturbances

Higher Variability

Robust

Lambda Tuning (LOOP-PRO)

Growing popularity

Guesswork Tuning

Most Popular method

QAD

Still Taught in colleges

Very Fast Response

Not Robust, Watch out when process

dynamics change


Lambda Tuning (IMC) – 1st Order Process Loops

Lambda (l) Tuning Results in a 1st Order Response to a Set Point Step. There is No Overshoot. The Lambda Value is the Closed-Loop Time Constant.

The user gets to decide on the Lambda value. The Lambda value should be based on achieving the process objectives.

The process will reach a new Set Point in 4 Lambda (l) periods.

Typically, Lambda should be at least 2 times greater than the process time constant or 2 times greater than the process Dead-Time – whichever is bigger.

Setpoint Step

Process

Time

l

63.2

86.5 95

98.2

l l l

Once the process dynamics are

known, the tuning constants can be

easily calculated to achieve the chosen

Lambda value


Lambda Tuning – 1st Order Process Loops (Procedure) 1. Conduct a series of open loop output bump tests to determine the

process dynamics.

a) Identify loop health problems such as backlash/stiction. If severe then fix problem before retuning

b) If the process dynamics will result in poor control performance then upgrade dynamics before retuning

2. Select a Lambda value. The process objective should be the most important criteria in Lambda selection. On most loops, the Lambda value should be considerably slower than the process time constant and process Dead-Time to ensure a minimum level of robustness.

3. Calculate the Lambda tuning constants using the formulas shown below.

4. Enter the tuning constants into the PID controller and confirm the Lambda tuning by conducting Set Point step tests.

)P

P

C

KpK

ql

t

PI tt


Lambda Tuning – 1st Order Process Loops (Example)

) SPAN

CO

CO

SpanKpK

P

P

C

%

%25.0

sec)26(*%

%5.1

sec3

ql

t

sec3 PI tt

3 %Span

2 %Output

DPV

DOP KP = = = 1.5

%Span

%Output

qD = 2 seconds

t = 3 seconds

Select λ = 6 seconds

Tuning Calculations


Control Performance – What Can Be Expected

Hydraulic pressure and flow loops will typically have fast process dynamics. A small Lambda value (fast response) can be safely selected. Typical Lambda values of 3 to 10 seconds are achievable.

Concentration (consistency) loops often have significant process Dead-Time, limiting the Lambda selection (and control capability). Typical Lambda values range from 10 seconds to 2 minutes.

Temperature loops often have high process time constants and Dead-Time and a large Lambda value (slow response) is required.


FC

4g

pm

FC

5

gp

m

Time

Setpoint Response

Process

Setpoint Response

Process

LC

1%

Ou

tpu

t Output Bump

Time to reach desired blend

Different flow responses mean that the fibre ratio is not maintained constant during a production rate change

Lambda Tuning – Defining the Process Objectives The tuning is optimized when it supports the process objectives. Often the tuning of several loops has to be coordinated to achieve the best overall result.

The E&I mechanic needs to understand how to calculate tuning constants to achieve the desired response.

The Lambda tuning method is recommended. It allows the tuner to select a speed of response.

Hardwood

HWD

Softwood

SWD


Lambda Tuning – 1st Order Process Loops (Example)

Loop KP (%SP/%OP)

τ (sec)

qD

(sec)

FC4 0.5 5 1

FC5 1.5 2 1

Process Dynamics Loop λ

sec Kc %OP/%SP

TI

sec

FC4 10 0.91 5

FC5 10 0.12 2

Lambda Tuning

FC

4g

pm

FC

5

gp

m

Time

Setpoint Response

Process

Setpoint Response

Process

LC

1%

Ou

tpu

t Output Bump

Desired blend maintained over

entire response


Lambda Tuning – 1st Order Process Loops (Flow Chart)

Conduct Set Point Response Tests to Confirm Tuning. Measure Final

Variability.

Review Flow Chart / Define Process and Control Objectives

Select Lambda Value. Calculate Tuning Constants. Install Tuning

Constants

Measure Baseline Variability. Compare Auto/Man Variability

Conduct Open Loop Bump Tests

Is loop health acceptable? No - Repair/replace equipment

Yes

Are process dynamics acceptable? No - Reconfigure / re-engineer loop to upgrade dynamics

Yes


Summary – Achieving “Good” Loop Performance

Design in good process dynamics. Unnecessarily high process gains, Dead-Times and time constants will undermine control loop performance

Position the sensors correctly to minimize Dead-Time, sensor noise

Select the control valve carefully. Do not oversize

Do not apply excessive filtering

Maintain the loop in good health. If the sensor or the control valve is flawed, then the loop performance will be poor

Ensure that the sensor is properly calibrated

Minimize backlash and stiction in the control valve

Use a structured, scientific tuning method. The Lambda tuning method is preferred because it is relatively fast and robust.

Select Lambda values that support the process objectives and minimize variability in the key processes

Ensure that the tuning is robust - able to deal with a range of operating conditions without causing the process to cycle

what you will learn in this seminar - pronamics control … tuning for first order processes...

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