Ecopetrol Dr. Carlos a. Smith

8/2/2019 Ecopetrol Dr. Carlos a. Smith

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College of Engineering University of South Florida

Carlos A. Smith, Ph.D.

BIENVENIDOS!

The Present/Future ofProcess Control


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MAIN ENTRANCE


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WALKWAY


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MAIN ENGINEERING BLDG


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ENGINEERING BUILDING


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EDUCATION


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ATHLETICS


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STADIUM


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FOOTBALL


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TAMPA


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The USF College of Engineering


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1. Challenges

2. Control3. Concluding Remarks

Outline


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Process structures are becomingincreasingly complex and interactive

mainly for performance (environmental,

quality, and energy) efficiency. The possible numbers of constraint sets is

large with interactive processes.

The more complex and interactive a

process is, the more difficult its control.

Challenges


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Control is still viewed as a black box

that solves all varieties of instruments,valves , and process problems!

Challenges


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Without a doubt, the PID controller will

remain for the foreseeable future theworkhorse at the bottom layer of

regulatory control.

PID controller is very robust.

Regulatory structure is still poorly done,

and in cases with major design orconfiguration errors.

Control


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16

Proper controller tuning is the largest,

quickest, and least expensive improvement one

can make in the basic control system to

decrease process variability. Blevins, T.L., McMillan, G.K., Wojsznis, W.K., Brown, M.W.,

Advanced Control Unleashed, ISA Press, Research Triangle Park,

NC, 2003

Yet most loops in plants are not properly tuned, and

many are in manual! Time consuming

Need expert knowledge

Involves risky open-loop tests

Control


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Without a doubt, the present and future of

process control depends on

Understanding the process is the key togood process control.

Control


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Increased computer and software technology

Allowing control algorithms to monitor the

control loop performance daily and issue

reports

Most companies will install PID controlmonitoring software and move toward

automated optimization test sequences for

tuning

The cost of any of these is about US$100-200

per loop (depending on the number of loops).

Control


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PROCESS CONTROL MONITORING

Control


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Some Statistics

As many as 75% of the control loops actually increasevariability

Causes

30% of DCS control loops are improperly configured

85% of control loops have sub-optimal tunings

15% of control valves are improperly sized

Control


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OBJECTIVES OF CONTROL MONITORING

Ensure the process variables are within

specified limits

Identify abnormal operation and diagnose the

root cause

Identify abnormal situations in time to effectcorrective action before the process is seriously

upset

Control


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Control Performance Monitoring Software

It is designed to monitorand diagnose the

performance of your PID Control Loops

It can tell you which loops

Are not tuned properly

Are oscillating, and with what periodHave valve stiction

Etc.

Control


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Does notautomatically tune or repair yourloops

Will indicate what may be wrong with the

tuning, such as too much integral action, etc.

Control


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(continued),

Allowing more capability for rigorous

dynamic simulations which can be used to

perform extensive testing and tuning (of newcontrol loops prior to going online), for

operator training, and to better understanding

of the processes. This improved

understanding is key for better control.

Control


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(continued), Allowing engineers to easily capture dynamic

data and perform model identification

necessary for tuning feedback controllers,

feedforward, decoupling, optimization, etc.

Feedforward and Model Predictive Control

(MPC) applications will increase due to the

modeling tools.

Companies will move toward MPC

monitoring to ensure MPC utilization (similar

to regular PID controllers).

Control


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(continued), Increasing the use of multivariable control

algorithms.

These algorithms are some times difficult to

justify and although possible to use in large

processes, they require well-trained individuals to

implement them

Are being designed to be scaled (up & down)

easily with minimum programming

Starting to incorporate objective functions and

can perform optimization

Control

C l


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Increased computer and software technology(continued),

Allowing us to use the tools

of Artificial Intelligence.

Fuzzy Logic

Neural Networks

Control

C l di R k


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Monitoring capabilities ofcontrol performance

Improved data analysis

Concluding Remarks

C l di R k


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Increase applications of

Feedforward Control

Model Predictive Control

Fuzzy Logic Control

Concluding Remarks


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Understanding the process is

the key to good processcontrol.

Concluding Remarks


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MUCHAS GRACIAS!

Concluding Remarks

Documents

Ecopetrol Dr. Carlos a. Smith