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Chapter 16

- Model Predictive Control

Single Loop Controllers

MPC Controller

Model Predictive Control

- Most popular form of multivariable control.
- Effectively handles complex sets of constraints.
- Has an LP on top of it so that it controls

against the most profitable set of constraints. - Several types of industrial MPC but DMC is the

most widely used form.

DMC is based on Step Response Models

- Allow the development of empirical input/output

process models. - The coefficients correspond to the step response

behavior of the process

Example of a SRM

- t i Du y(t) ai
- 0 0 1 0 0
- 1 1 0 0 0
- 2 2 0 0.63 0.63
- 3 3 0 0.87 0.87
- 4 4 0 0.95 0.95
- 5 5 0 0.98 0.98
- 6 6 0 0.99 0.99
- 7 7 0 1.00 1.00
- 8 8 0 1.00 1.00

Open-Loop Step Test for Thermal Mixer

SRM for the Thermal Mixer (Du0.05)

i ti yi ai

0 5 50.00 0.0 0.0

1 10 49.77 -0.23 -4.68

2 15 49.35 -0.65 -13.0

3 20 49.08 -0.92 -18.4

4 25 48.94 -1.06 -21.2

5 30 48.87 -1.13 -22.6

6 35 48.84 -1.16 -23.2

7 40 48.83 -1.17 -23.5

8 45 48.82 -1.18 -23.7

9 50 48.82 -1.18 -23.7

Example of SRM Applied to a Process with Complex

Dynamics

- SRM 0 0 -0.3 -0.1 0.05 0.1 0.2 0.3 . . .

Complex Dynamics

- An integrating process (e.g., a level) is modeled

the same except that the Tss is based on

attaining a constant slope (i.e., a constant ramp

rate). - An integrating variable is referred to as a ramp

variable.

SRM for Ramp Function

Using SRM to Calculate y(t)

- Given SRM 0 0.4 0.8 0.9 1.0
- y05 Du5 DTs20 sec
- Solve for y(t)
- y y0 Du SRM
- y15 50 5 y2 5 50.4 7
- y3 5 50.8 9 y4 5 5.9 9.5
- y5 5 51 10 or in vector form
- y 5 7 9 9.5 10 10 10
- y(20 s)5 y(40 s)7 y(60 s)9 y(80 s)9.5
- y(100 s)10 y(120 s)10 y(140 s)10 ...

Class Exercise

- Given SRM 0 0.4 0.8 0.9 1.0
- y05 Du-2 DTs20 sec
- Solve for y(t)

Solution to Class Exercise

- Given SRM 0 0.4 0.8 0.9 1.0
- y05 Du-2 DTs20 sec
- Solve for y(t)
- y y0 Du SRM
- y15 -20 5 y2 5 -20.4

4.2 - y3 5 -20.8 3.4 y4 5 -2.9

3.2 - y5 5 -21 3 or in vector

form - y 5 4.2 3.4 3.2 3 3 3

Calculating SRM from Step Response Data

- Remember previous equation
- y y0 Du SRM
- Solving for SRM yields
- SRM (y-y0)/Du

Example of the Calculation of SRM from Step

Response Data

Class Exercise for the Calculation of the SRM

from Step Response Data

Class Exercise for the Calculation of the SRM

from Step Response Data

Simulation Demonstration

- A thermal mixing process mixes two streams with

different temperatures (25ºC and 75ºC) to produce

a product (about 50ºC). - Consider a 10 increase (0.05 kg/s) increase in

the flow rate of colder stream. - Develop SRM from step response.

Step Test Result and SRM

Comparison for the Same Du (10)

Comparison for the Same Du (-10)

Demonstration Conclusions

- MPC model agrees closely for the same step input

change, but shows significant mismatch for a

different size of input change due to process

nonlinearity.

Nonlinear Effects

- This SRM will not accurately represent negative

changes in the MV or different magnitude MV

changes due to process nonlinearity. - Therefore, a better way to develop a SRM is to

make a number of MV changes with different

magnitude and directions and average the results

to get the SRM.

SRMs for Different Size Dus

Another Approach

- Use the average ais

Using Average ais for Du10

Using Average ais for Du-10

Using Average ais for Du5

Relation to MPC Model Identification

- Developing SRMs that represent an average

response is the basis of good MPC models.

Class Exercise

- Using the composition mixer simulator (CMIXER),

use a 5 step input change (0.025 kg/min) to

generate a SRM for this process - First, implement the step input change
- Next, determine the model interval
- Then, calculate the SRM coefficients
- Finally, compare simulator result to MPC model

prediction for Du (10 to -10)

SRM Summary

- SRMs represent the dynamic behavior of a

dependent variable (output variable) for a

process for a unit step change in an input. - SRMs are empirical models that are flexible

enough to model complex dynamics - SRMs are linear approximations of process

behavior and can be estimated using step

responses for the process.

SISO MPC Controller

- Application of SRM for a Series of Input Changes

A Series of Input Changes

- Definition of the SRM is based on a single step

input change. - But feedback control requires a large number of

changes to the input variables. - To use a SRM to predict the output behavior for a

series of input changes, linear superposition is

assumed, i.e., the process response is equal to

the sum of the individual step responses.

Superposition of Two Step Input Changes

Superposition of Two Step Input Changes

Equation Form for Two Sequential Input Changes

- Consider the the response of a process to two

step input changes separately

Combined Response for Two Input Changes

Combined Response for a Series of 4 Input Changes

Matrix Form for Combined Response for a Series of

4 Input Changes

The Dynamic Matrix

- The Dynamic Matrix determines the dynamic

response of the process to a series of input

changes, nc, for a prediction horizon of np steps

into the future. - The dynamic Matrix is constructed from the

coefficients of the SRM - A is npnc SRM is m (this case has 4 inputs and

7 output predictions)

Zeros in Dynamic Matrix

- Where do they come from?
- Consider the first row y1 is cannot be affected

by Du1, Du2, and Du3 because y1 is measured

before Du1, Du2, and Du3 are applied. - Likewise, y2 is cannot be affected by Du2 and Du3

The Matrix Form of the Generalized Model Equation

Dynamic Matrix Example

Class Exercise

Solution to Class Exercise

SISO MPC Controller

- Prediction Vector

Previous Inputs Affect Future Response of the

Process

Previous Inputs Affect Future Response of the

Process

Prediction Vector

- The prediction vector, yP, contains the combined

effect of the previous input changes on the

future values of the output variable. - The prediction vector is calculated from the

product of the previous input changes and the

prediction matrix, which is constructed using the

coefficients of the SRM.

Process Model

- The future values of the output variable are

equal to the sum of the contribution from

previous inputs (prediction vector) and the

contribution from future change in the input

variable

Process Model

- In order to correct for the mismatch between the

predicted value of y at the current time and the

measured value, a correction vector, e, is added

to the process model equation.

Correction for Mismatch in Ramp Variable

- The error between the predicted value of y(t0)

and the measured value is applied as before e

y(t0) - yP(t0) - An unmeasured disturbance can affect the slope of

the modeled SRM - Therefore, an additional tuning factor for ramp

variables is the rotation factor (RF), which is

the fraction of the mismatch e that is used to

change the slope of the SRM of the ramp variable.

SlopenewSlopeoldRFe

SISO MPC Controller

- DMC Controller

Development of DMC Control Law

DMC Control Law

Analysis of DMC Controller

- (ATA)-1AT is a constant matrix that is

determined explicitly from the coefficients of

the SRM. - If you double the number of coefficients in the

SRM, you will increase the number of terms in the

dynamic matrix by a factor of 4 - E takes into account changes in the setpoint, the

influence of previous inputs, and the correction

for model mismatch. - A full set of future moves are determine by this

control law at each control interval.

Moving Horizon Controller

Moving Horizon Controller

- Even though the DMC controller calculates a

series of future moves, only the first of the

calculated moves is actually implemented. - The next time the controller is called (DTs

later), a new series of moves is calculated, but

only the first is applied. - If the SRM were perfect, the subsequent set of

first moves would be equal to the first series of

moves calculated by the DMC controller.

SISO MPC Controller

- Implementation Details

Implementation Details

- Choose DTs based on when new information is

available on the CV. E.g., analyzer update. For

temperature sensor, use DTs10-15 s. - Model horizon m Tss / DTs (normally set

m60-90 coefficients. Always set mgt30. - Control horizon nc½m
- Prediction horizon npmnc

Controller Implementation Example

- Consider a SISO DMC temperature control loop that

has an open loop time to steady-state equal to 20

minutes. - Applying the previous equations

Class Example

- Consider a SISO DMC controller applied to the

overhead product of a C3 splitter. The overhead

composition analyzer update every 10 min and the

time to steady-state for the overhead composition

for a reflux flow rate change is 7 hours.

Determine m, np and nc for this controller.

Solution

SISO MPC Controller

- Tuning

DMC Controller Tuning

- Previous DMC control law results in aggressive

control because it is based on minimizing the

error from setpoint without regard to changes in

the MV. - (ATA)-1 is usually ill-conditioned due to normal

levels of process/model mismatch. - These problems can be overcome by adding a

diagonal matrix, Q, to the Dynamic Matrix, A.

DMC Controller with Move Suppression

The Dynamic Matrix with the Move Suppression

Factor Added

Move Suppression

DMC Control Law

Effect of Move Suppression Factor

- The larger q, the greater the penalty for DMV

moves. The smaller q, the greater the penalty

for errors from setpoint.

Tmixer DMC Tuning Example Q300

Tmixer DMC Tuning Example Q100

Tmixer DMC Tuning Example Q60

Tmixer DMC Tuning Example Q30

Tmixer DMC Tuning Example Q20

Tmixer DMC Tuning Example Q10

Controller Tuning

- Reliability versus performance.
- Meeting the overall process objectives.

Checking the Controller Tuning for a Setpoint

Changes in the Opposite Direction Q30

Class Exercise SISO DMC Tuning

- Tune the DMC controller for the CMixer

SISO MPC Controller

- Testing

Controller Testing

- Use disturbance upsets to test controller

performance

Controller Disturbance Rejection Q60

Controller Disturbance Rejection Q30

Controller Disturbance Rejection Q10

Class Exercise Testing the Controller Tuning

- Using disturbance upset tests, test the

performance of your tuned DMC controller.

Model Identification The Least Squares Solution

Controller Disturbance Rejection DTs7, Q30

What if the SRM become worse? DTs11 Q30

What if the SRM become worse? DTs15 Q30

SRM Model Mismatch

- The controller still works in this case, but the

performance is penalized.

MIMO DMC Control

MIMO DMC Control

- Extension of SISO to MIMO
- Constraint control
- Economic LP
- Large-scale applications

MIMO DMC Control

- Extension of DMC to MIMO processes

Dynamic Matrix for a MIMO Process

Recall The SISO Dynamic Matrix

Example of a Partitioned Dynamic Matrix

Dynamic Matrix for a MIMO Process

A Partitioned Control Vector

An Example of a Partitioned Control Vector

Control Law for MIMO DMC Controller

Relative Weighting Factor

- The relative weighting factor, wi , allows the

application control engineer to quantitatively

rank the importance of each CV and constraint. - For each of the constraints and CVs, the control

engineer must determine how large a violation of

the constraint or deviation from setpoint

requires drastic action to maintain reliable

operation (Equal Concern Errors).

Equal Concern Errors

- ECEs are based on process experience.
- Consider a distillation column
- 0.5 psi above the differential pressure limit of

2 psi will cause drastic action to prevent

flooding. - 1 impurity levels above the specified impurity

levels in the products will require rerunning the

product. - Reboiler temperatures greater than 10ºF above the

reboiler temperature constraint will result in

excessive fouling of the reboiler.

Feedforward Variables

- Measured disturbances should be modeled as inputs

for the MPC controller to reduce the effect of

those disturbances on the CV. - FF variables are treated as inputs that are not

manipulated.

MIMO DMC Control

- Constraint Control

Constraint Control

- With DMC, constraints are treated as CVs.
- As different combinations of constraints become

active, different weighting factors, wi, are used

which are based on the equal concern errors.

Therefore, as different combinations of active

constraints are encountered, the wis

automatically determine the relative importance

of each. - The DMC controller determines the optimal control

action based on considering that the wis change

along the entire path to the setpoints.

Therefore, the entire control law must be solved

at each control interval.

MIMO DMC Control Law

MIMO DMC Control

- MPC Model Identification

MPC Model ID

MIMO Model Identification

- For DMC, impulse models are used for

identification so that bad data can be sliced

out of the training data. - Sources of bad data analyzer failure, data not

recorded, process not operated in normal fashion

(by-pass opened, atypical feed to the process,

saturated regulatory controls, etc.), and large

unmeasured disturbance to the process.

Model Identification

- SRMs have the flexibility to model a full range

of process dynamics (e.g., recycle systems). - MPC controllers that use preset functional forms

for models (e.g., state-space models or a set of

preset transfer function models) can be inferior

to SRM based models in certain cases.

MIMO DMC Control

- Economic LP

Economic LP

- The LP combines process optimization with the DMC

controller. - The LP is based on economic parameters (e.g.,

product values, energy costs, etc.) and the

steady-state gains of the process. - The last term in each SRM is used to provide the

necessary gain information therefore, the LP is

consistent with the MPC controller.

Example of an LP

Economic LP and PID Control Performance

Economic LP and MPC Control Performance

MPC Control Performance (i.e., size of control

circle)

- Depends on accuracy of models (e.g., process

nonlinearity, type and size of unmeasured

disturbances, changes in the process and

operating conditions). - Depends on the performance of regulatory controls

and sensors. - Can also depend on the control MPC technology

used.

Economic LP

- Consider the case with 5 MVs and 10 control

objectives (i.e., upper and lower limits on CVs,

upper and lower limits on MVs, and upper rate of

change limit on MVs) because there are 5

degrees-of-freedom (i.e., one for each MV) the LP

will determine which 5 constraints to

simultaneously operate against. - The 5 constraints, in this case, become the

setpoints for the MPC controller. - The LP is applied each control interval along

with the MPC controller.

The LP and the MPC Controller

- Both use the steady-state gains from the SRM.
- The LP determines the setpoints for the

controller from the economic parameters and the

process gains. - The controller drives the process to the most

profitable set of constraints, i.e., keeps the

process making the most profit even though the

most profitable set of process constraints

changes with time parameters. - Balanced ramp variables are constraints for the

LP.

MIMO DMC Control

- Large-Scale Applications

Size of a Typical Large MPC Application

- MVs 40
- CVs 60
- Constraints 75-100
- There are some companies that have even larger

applications.

MPC Project Organization

MPC Project Organization

- Understand the process
- Set the scope of the MPC controller
- Choose the control configuration
- Design the Plant Test
- Conduct the Pretest
- Conduct the Plant Test and collect the data
- Analyze data and determine MPC model
- Tune the controller
- Commission the controller
- Post audit

MPC Project Organization

- Understand the process

Understand the Process

- Process understanding is the single most

important issue for a successful MPC application. - Stated another way, if you do not fully

understand the process and its preferred

operation, it is highly unlikely that you will be

able to develop a successful MPC application.

How to Develop Process Understanding

- Study PFDs
- Study PIDs
- Talk to the operators (how it really works!)
- Talk to plant engineers (how they want it to

work) - Interview plant economic planners (how much its

worth). - If available, run steady-state process simulator
- Read the plant operating manuals
- Spend time at the process on graveyards.

Be able to answer the following questions

- What is the purpose of the plant?
- Where does the feed come from?
- Where do the product go?
- How much flexibility is there for feed supply and

product demand? - How do the seasons affect the operation? Also,

feed supply and product demand?

Be able to answer the following questions

- What are the 3 or 4 most important constraints

that operators worry about? - Where is energy used and how expensive is it?
- What are the product specifications?
- Are there environmental or tax issues that affect

plant operations?

MPC Project Organization

- Set the scope of the MPC controller

Set the Scope of the Project (Very Important Step)

- What part of the plant should be included in the

controller? That is, how much of the plant

should be included in the MPC controller to meet

the project objectives? - To answer this question, you must discuss the

objectives of the project with the operations

personnel, the technical staff for that portion

of the plant, and the scheduling people. Only

then can you be sure that you are solving the

correct problem.

MPC Project Organization

- Select the control configuration

MPC Configuration Selection

- Which PID loops do you open (i.e., turn over to

MPC controller) and which do you leave closed.

That is, if you leave a PID loop closed, the MPC

controller sets the PID loop setpoint (e.g., flow

controller). - What are the MVs for the distillation columns in

the process? Should the MPC controller be

responsible for accumulator or reboiler level

control?

MPC Configuration Selection

- From an overall point of view, what are the MVs

and the CVs for the MPC controller? - A challenging problem that requires process

knowledge and control experience while keeping

focused on the overall process objectives.

Reasons for Leaving a PID Loop Closed

- The PID loop may be more effective in eliminating

certain disturbance due to the higher frequency

of application. For example, flow control loops.

In addition, certain pressure, level, and

temperature PID loops should be left closed.

Situations for which a PID loop should be opened

- Loops with very long dynamic and/or large

deadtime - Process lines with two control valves in series.
- Certain levels for which allowing the MPC

controller control the level adds important

flexibility to better meet the primary objectives

of the process. For example, it can provide more

effective decoupling.

Use Good Control Engineering

- Ensure that the MVs have a direct and immediate

affect on the CVs. - Use computed MVs to reject certain disturbances,

e.g., use internal reflux control to reduce the

effect of rain storms or computed heat input for

a pumparound. - Use inferential measurements to reduce the effect

of deadtime (e.g., inferential temp control) - Use CV transformations that linearize the overall

response of the process.

Variable Transformations

- For a control valve, DPKFlow2
- For high purity distillation columns, use log

transformed compositions xlog(x) - Use different linear models depending on the

operating range.

MPC Project Organization

- Design the Plant Test

Plant Test Guidelines

- Make from 5 to 15 step tests for each MV
- The MV changes should be as random as possible to

prevent correlated data. - MV moves should be as large as possible but not

so large that it upsets the process (e.g.,

1-10). - For each MV, make a change after 1tp, 2tp, 3tp,

4tp, and 5tp, where tp is Tss/4. - Above all, remember the product specs and the

process constraints

Develop the Testing Plan

- Identify all the MVs you plan to move. Tabulate

the following data for each MV - Tag number of the MV and physical description
- Nominal value
- Range of move sizes
- Make a proposed testing sequence and indicate

when each MV is moved and by how much. - Discuss the MV move sizes with operations.

Remember the spec and constraints.

Choose Sampling Period

- Small sample period high time resolution but

increased size of data set. In general, sample

as new information become available. - For fast responding processes, sample every few

seconds. - For slow processes, sample every 5 min
- Look at the smallest expected Tss.
- Estimate sample period as Tss/50
- Compare with sensor dynamics

Make MV Changes one MV at a Time to Generate

Uncorrelated Results

Develop a Roughed-out Gain Matrix

- Using your process knowledge, determine whether

the steady-state process gain is positive,

negative, or zero for each input (MV DV)/

output (CV) pair. - Using for positive, - for negative, or 0 for

zero, construct the roughed-out gain matrix.

Inputs are listed vertically while outputs are

listed horizontally.

Roughed-out Gain Matrix Ex.

Ex. Roughed-out Gain Matrix

MPC Project Organization

- Conduct the Pretest

Pretest

- Check all the sensors, control valves and

regulatory control loops for proper operation. - Ensure that the process equipment is in proper

repair. Check material and energy balances on

the unit. Make sure that all the major pieces of

equipment are in operation. Check the feed to

the unit. - Then, apply step input changes for each MV and

compare to your roughed out gain matrix. - Reconcile any deviations from what you expected

(e.g., roughed out gain matrix, dynamic response,

etc.). This will enhance your process

understanding.

MPC Project Organization

- Conduct the Plant Test and collect the data

Testing in Optimal Operating Mode

- Because you will want your plant to operate

effectively at its optimum operating conditions,

you should, if possible, test you plant near the

optimum operating conditions to reduce the

effects of process/model mismatch. - Therefore, you will need to determine the optimum

operating conditions for your plant, e.g.,

discuss with an experienced plant engineer or use

a nonlinear process optimizer.

Plant Test Overview

- The plant test is the most important step in an

MPC project because if the MPC models do not

agree with the process, controller reliability

and performance will be poor. - If the process changes (e.g., different equipment

configuration, different feed, different

regulatory controller tuning, etc.), mismatch

between the MPC model and the process will

result, affecting controller performance.

Plant Test Check List

- Meet (5-10 min) with the operators at the start

of each shift and explain what you are doing. - Make sure that each MV move is made on time, in

the proper direction, and by the correct amount. - Make sure that a technical person is observing

entire testing period (24-7).

Plant Test Check List

- Make real-time plots of the data and explain all

the behavior that you see. - Identify abnormal periods and make notes

significant unmeasured disturbances, instrument

failures, utility outages. The MPC model should

not be trained using data collected during these

periods. - Talk to the operators about the process.
- Buy pizza, donuts, ice cream and barbeque for the

operators

Using Roughed-out Gain Matrix

- During the testing phase, make sure that the

plant results agree with your roughed-out gain

matrix. - If not, you will need to explain the difference.

That is, your process knowledge needs to be

improved a bit or something is not working

properly (e.g., an analyzer is off-line)

Unmeasured Disturbances

- Training an MPC controller on data collected

during periods with unmeasured disturbances will

reduce the accuracy of your model and reduce the

effectiveness of the overall project. - Some unmeasured disturbances are inevitable, but

you must strive to keep them to an absolute

minimum.

Examples of Unmeasured Disturbances to be Avoided

- Back flushing a condensor
- Changes in by-pass streams
- Product grade changes
- Changes in the controller settings for the

regulatory controllers. - Any change in the process operating conditions

not modeled in the controller or process

equipment changes

Identified Periods with Unmeasured Disturbances

- Slice out the data from those periods so that

this data is not used to train the MPC model

therefore, the identified model will not be

corrupted.

MPC Project Organization

- Analyze the data and develop the MPC model

Obtain the MPC Process Models

- Use the accepted plant test data with the MPC

model identification software to develop each of

the input/output SRM models.

Evaluate Process Models

- For each input/output pair, plot the SRM on a

small graph. - Arrange the SRM models into a matrix similar to

the roughed-out gain matrix. - Review the results to ensure that you have models

that make sense. - Use the statistical tools from the MPC model ID

software to evaluate each SRM

Evaluate Process Models

- Examine the difference between the plant test

data and your models (residual). - Plot the residual for positive and negative MV

changes. This will help identify the degree of

process nonlinearity. Also, it will help

evaluate CV transformations that behave more

linearly.

Plant Economics

- The LP will require incremental costs (feed

costs, utility costs) and incremental revenues

(product values) - For previous distillation example, you will need

the incremental cost of steam (/lb), the

incremental cost of feed (/lb), and the

incremental value of both products (/lb).

MPC Project Organization

- Tune the controller

General Approach to Tuning

- Use the SRMs of the process (MPC model) off-line

as the process to develop the preliminary

settings for the controller. - Three areas that you need to get right
- Economics
- Constraints
- Dynamics

Testing Economics

- Run plant/controller simulations (off-line) with

different combinations of constraints to ensure

that the controller pushes the plant in the

correct direction. - Run test cases to see which LP constraints are

operative.

Constraints

- Check the equal concern errors for each

constraint to ensure that the proper relative

weighting factors for each combination of active

constraints is used. That is, when constraint 1

and 5 are active, what is the relative importance

of the CVs and constraints. - Also, use the plant/controller simulation

(off-line) to test the relative weighting.

Dynamics

- Select the move suppression factors for each MV

using off-line simulations of the plant. Run the

simulations a number of times for setpoint

changes and unmeasured disturbances to get the

MSFs right. Operators are a good source for

letting you know how much the MVs should move in

certain situations and how fast the process

should move to CVs, but remember that they are

most familiar with how the plant used to operate.

Operator Training

- In some states, it is a legal requirement.
- Typically operators receive one to two hours of

training with MPC. - Nevertheless, better operator training increases

your chance for success.

Watchdog Timer

- The controller should run every minute 24-7
- Otherwise, a DCS alarm should sound.
- The most common approach is to use a watchdog

timer (a small program running on the DCS) to

communicate with the MPC controller. If the

watchdog fails to receive a healthy response from

the controller after an appropriate period of

time, the watchdog forces each MV into a fallback

(shed) position and writes an alarm to the

operator.

MPC Project Organization

- Commission the controller

General Approach

- Now it is time to install the controller in the

closed loop in the process. - Since the controller will directly affect the

plant operation, it is essential to proceed

methodically and with caution. - Use a checklist and be observant

Commissioning Checklist

- Make sure that the operators have been trained
- Install control software, database, and other

real-time components. - Check controller database
- Control CVs, DVs, and MVs match raw DCS inputs

exactly - Controller models, tuning parameters, etc. match

off-line versions exactly

Commissioning Checklist (cont.)

- Turning controller on the prediction mode
- Each MV configured correctly in the DCS?
- Correct shed mode for each MV?
- MVs out of cascade (controller cannot change)?
- Controller turned on the prediction mode?
- The size of the calculated DMV moves make sense?
- The CV predictions make sense?

Commissioning Checklist (cont.)

- First-time MV check
- Use conservative controller settings
- Reduce difference between upper and lower limits

on MV (or rate limits) - Make sure controller is running
- Put MVs into cascade one at a time.
- Ensure that the calculated MV change is applied

to DCS setpoint.

Commissioning Checklist (cont.)

- Close the loop
- Ensure that the controller is running
- Turn ON the controller master switch
- With MVs clamped, put one or two MVs into

cascade - Relax the MV limits somewhat and check for good

controller behavior - Add more MVs gradually, ensuring good control

performance.

Final Checks

- Ensure that the standard deviations of the CVs

decrease and that the MVs are not moving around

too much. - Ensure that the LP is driving the process in the

correct direction.

MPC Project Organization

- Post audit

Post audit

- Collect post commissioning data for the key CVs

and compare to pre-project data. - Ensure that the standard deviations of the key

CVs have decreased. - Evaluate whether the upper and lower limits on

the MVs are still set correctly. - Ensure that the controller is running the process

at the economically best set of constraints most

of the time.

Advantages of MPC

- Combines multivariable constraint control with

process optimization. - A generic approach that can be applied to a wide

range of processes. - Allows for more systematic controller

maintenance. - Depends on the particular process

MPC Offers the Most Significant Advantages

- For high volume processes, such as refineries and

high volume chemical intermediate plants. - For processes with unusual process dynamics.
- For processes with significant economic benefit

to operated closer to optimal constraints. - For processes that have different active

constraints depending on product grade, changes

in product values, summer/winter operation, or

day/night operation. - For processes that it is important to have smooth

transitions to new operating targets.

Application Example

- C3 splitter in an ethylene plant (reboiler duty

is free therefore, run at maximum reboiler duty

and apply single-ended control.) - DCS 70 of cost 35 of inc. benefit
- AdPID 10 of cost 60 of inc. benefit
- MPC 10 of cost 3 of inc. benefit
- RTO 10 of cost 2 of inc. benefit

Application Example

- Reformer. Control is easy since it only involve

inlet temperature controllers. - DCS 70 of cost 35 of inc. benefit
- AdPID 10 of cost 30 of inc. benefit
- MPC 10 of cost 5 of inc. benefit
- RTO 10 of cost 30 of inc. benefit

Application Examples

- FCC Unit. Optimal constraints change with

economics, operating conditions, etc. Finding the

optimal riser temperature requires RTO (yield

model and nonlinear problem). - DCS 70 of cost 35 of inc. benefit
- AdPID 10 of cost 10 of inc. benefit
- MPC 10 of cost 35 of inc. benefit
- RTO 10 of cost 30 of inc. benefit

Limitations of MPC

- It is a linear method therefore, it is not

recommended for highly nonlinear processes (e.g.,

pH control). - It does not adapt to process changes. If the

process changes significantly, the MPC model must

be re-identified. - For certain cases, the economic LP may not be

accurate enough. For these cases, it may be

necessary to add nonlinear optimization on top of

the LP.

Different Forms of MPC

Primary Commercial MPC Software

- ABB
- Aspentech DMCplus
- Fisher-Rosemount
- Foxboro
- Honeywell RMPCT
- Yokogawa

- MPC software based on models with specified

dynamic characteristics can be inferior to SRM

based approaches.

Conclusions

Conclusions

- The challenges of this course
- A very difficult area with considerable

mathematics and industrial practice. - Only two days
- Wide variety of backgrounds of the students.

Conclusions

- You should now be familiar with MPC terminology

and technology and the major issues associated

with the commercial application of MPC. - Are you an MPC expert? Not yet! But with more

study and a lot more experience, you can become

one.

Conclusions

- This is not the end.
- It is not even the beginning of the end.
- But it is, perhaps, the end of the beginning
- (Sir Winston Churchill, Nov. 10, 1942)