Mairodi ST MTTeknik Kontrol

Mairodi,ST.,MT.

Dosen : Mairodi, ST.,MT.Semester : GenapSKS : 3 sksBuku Referensi : Programmable Logic Controller, Penulis: James A. Rehg dan Glenn J. SartoriJumlah Peserta Total : ………...

1. Pengantar Sistem Kontrol2. Pengantar PLC3. Field Devices4. Hardware PLC5. Driver Interface PLC6. Mengkonfigurasi PLC7. Software pemrograman PLC8. Sistem Bilangan dan Sistem Memory dalam PLC9. Logika Boolean dalam Pemrograman10 Instruksi Bit10. Instruksi Bit11. timer dan Counter 12. Instruksi Matematika13. Instruksi Perbandingan/comparison

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g p14. Pengontrolan Variabel Analog Menggunakan PLC

1 Pengantar Sistem Kontrol1. Pengantar Sistem Kontrol

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Loop KontrolJenis Pengontrol:

Analog dan digitalOn-off dan PID

Controller Process+

Actuator

On off dan PIDFeedback, Feedforward dan Cascade Control

Sensor +Sensor +Transmitter

Tujuan Pengontrolan :– Menjaga/mempertahankan nilai besaran pada referensi tertentu– Mengatasi gangguan/efek perubahan pada sistem– Meningkatkan performansi sistem

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Contoh-contoh pengontrol yangContoh contoh pengontrol yang sering dipakai dalam industri :

• Programmable logic controller (PLC)Mi t ll

DIBAHAS DALAM

KULIAH INI• Microcontroller• DCS (Distributed Control System)• SCADA (Supervisory Control and DataSCADA (Supervisory Control and Data

Acquisition) System

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Definition (1)( )• Process

– A series of interrelated actions which transform materialIt covers all resources that are involved in the process andIt covers all resources that are involved in the process and

talks aboutprocess “inputs” (e.g. resources, raw material) and “outputs”

(e.g.fi i h d d t)

Raw Materials Products

Energies Outfinished product)

Process

Energies OutEnergies Out

Control– To maintain desired conditions in a physical system by adjusting

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p y y y j gselected variables in the system

Definition (2)( )• Process Control

– To maintain desired conditions in a physical systemTo maintain desired conditions in a physical system by adjusting selected variables in the system in spite of disturbances affecting the system and observation noisenoise

Corrective Action Process

KnowledgeData

Information

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Information

Daylife Example: Driving a Car

Brain:

Daylife Example: Driving a Car• Control Objective (Setpoint):

Maintain car in proper lane Brain:Control calculation

Eyes:Sensor

Maintain car in proper lane• Controlled variable:

Location on the road• Manipulated variable:

Orientation of the front wheelsOrientation of the front wheels• Actuator:

Steering wheel• Sensor:

D i ’Driver’s eyes• Controller:

Driver• Disturbance:

Steering wheel:Actuator

Curve in road• Noise:

Rain, fog

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Industrial Example #1: Heat Exchanger

• Control Objective (Setpoint):Maintain temperature

ProductStream SteamTC

Maintain temperature• Controlled variable:

Outlet temperature of product stream• Manipulated variable:

TTSteam flow

• Actuator:Control valve on steam line

• Sensor:

Feed Condensate

Thermocouple on product stream• Controller:

Temperature controller• Disturbance:Disturbance:

Changes in the inlet feed temperature• Noise:

Measurement noise

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Industrial Example #2: Liquid CLevel Control

• Control Objective (Setpoint):Maintain levelMaintain level

• Controlled variable:Fluid level in the tank

• Manipulated variable:Fluid

Fluid flow• Actuator:

Control valve on fluid line• Sensor: LC

Level transmitter on the tank• Controller:

Level controller• Disturbance:

LC

Disturbance:Changes in the inlet feed flow

• Noise:Measurement noise

LT

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Elements of Process Control Loop

• Sensor

Elements of Process Control Loop

Measure process variable• Transmitter

Convert the measured process variable into standard signalConvert the measured process variable into standard signal• Controller

Drive actuator by giving an appropriate controller output signalA t t• ActuatorAdjust manipulated variable based on the value of the controller

output signal• Process

Physical system to be controlled

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Istilah-istilah (I)Istilah istilah (I)• Control Objective (Setpoint, SP)• Controlled Variable (CV) or Process Variable (PV)• Measured Process Variable (PVm)• Controller Output (CO)• Controller Output (CO)• Manipulated Variable (MV)• Final Control Element (Actuator)• Sensor/Transmitter• Controller• Disturbance Variable (DV)• Disturbance Variable (DV)• Measurement Noise

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Goal of Process Operation24 hours process operation? Hmm… I think, to achieve

Goal of Process OperationHmm… I think, to achieve

those, we need to continuously monitor & control the process

24 hours a day,7 days a week!!!

• Safety & Reliability• Product SpecificationProduct Specification• Environmental Regulation• Operating Constraint• Operating Constraint• Efficiency• Maximum profit• Maximum profit

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Safety and ReliabilitySafety and Reliability• The control system must provide safe operation

Alarms, safety constraint control, start-up and shutdown

• A control system must be able to “absorb” a variety of disturbances and keep the process in a goodof disturbances and keep the process in a good operating regionFeed composition upsets, temporary loss of utilities

(e g steam supply) day to night variation in the(e.g., steam supply), day to night variation in the process

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Product Specification• Quality

– Products with reduced variability

Product Specification

N C ll

– Products with reduced variabilityFor many cases, reduced variability products are in high demand and

have high value added (e.g. feedstocks for polymers)

purit

y nt

ratio

n Limit

purit

y en

tratio

n Limit

Old Controller New Controller

Time

Imp

Con

cen

TimeIm

pC

once

Product certification procedures (e g ISO 9000) are• Product certification procedures (e.g., ISO 9000) are used to guarantee product quality and place a large emphasis on process control

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Environmental RegulationEnvironmental Regulation

• Various government laws may specify that theVarious government laws may specify that the temperatures, concentrations of chemicals, and flow rates of the effluents from a process pbe within certain limitExamples:– Regulations on the amounts of SO2 that a

process can eject to the atmosphere, and on the lit f t t d t i l kquality of water returned to a river or a lake

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Operational ConstraintOperational Constraint

• All real process have constrained inherent toAll real process have constrained inherent to their operation which should be satisfied throughout the operationg pExamples:– Tank should not overflow or go dryg y– Distillation column should not be flooded– Catalytic reactor temperature should not exceed y p

an upper limit since the catalyst will be destroyed

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EfficiencyEfficiency

• The operation of a• The operation of a process should be as

i l ibleconomical as possible in utilization of raw material, energy and capitalp

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Maximizing the Profit of a Plant (1)Maximizing the Profit of a Plant (1)

• The operation of a process may many p p y ytimes involves controlling against constraintsTh l th t bl t• The closer that you are able to operate to these constraints, the more profit you can makeExample:– Maximizing the product production rate

usually involving controlling the process y g g pagainst one or more process constraints

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Maximizing the Profit of a Plant (2)

Constraint control example: A reactor temperature control

Maximizing the Profit of a Plant (2)

• At excessively high temperatures the reactor will experience a temperature runaway and explode

• But the higher the temperature the greater the product yield• Therefore better reactor temperature control allows safe operation at a

New Controller Improved Performance

• Therefore, better reactor temperature control allows safe operation at a higher reactor temperature and thus more profit

mpu

rity

cent

ratio

n Limit

mpu

rity

cent

ratio

n Limit

Time

ImC

onc

Time

ImC

onc

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The History of Process Control• 1960s Pneumatic analog instrumentation, controllers, and computing modules• 1970s Electronic analog instrumentation controllers and computing modules

The History of Process Control

• 1970s Electronic analog instrumentation, controllers, and computing modules– Direct digital control with special algorithms programmed in main frame computer

• 1980s Electronic analog instrumentation and digital distributed control systems (DCS)– Supervisory and model predictive control configured in special purpose computers

• 1990s Smart analog instrumentation, valves, and digital distributed control systemsg g y– Supervisory and model predictive control configured in special purpose computers– Neural networks, online diagnostics, and expert systems in special purpose computers– Real time optimization using model libraries in special purpose computers

• 2000s Field bus based digital smart instrumentation, valves, and control systemsDigital bus takes full advantage of smartness and accuracy of instrumentation and valves– Digital bus takes full advantage of smartness and accuracy of instrumentation and valves

– Some fast PID controllers such as flow and pressure go to the field transmitter or valve– Model predictive control, neural networks, online diagnostics, and expert systems are

integrated into the graphically configurable field bus based control systems and move to PCs

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Common Types of Control StrategyCommon Types of Control Strategy

• Manual vs. Automatic• Servo vs. Regulator• Open-loop vs. Closed-loop• Control strategies

– Feedback ControlFeedforward Control– Feedforward Control

– Cascade Control

• Single-Input Single-Output (SISO) vs. Multi-Input Multi-Output (MIMO, also known as multivariable)

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Manual vs AutomaticTemperature indicator

Should I adjusth l

Manual vs. Automatic• Manual

the valve orshould I run?– Human has to adjust the MV to

obtain the desired value of the PV based on observation and

i iEmergency cooling

prior experiences

• Automatic– The computer (or other device)– The computer (or other device)

autonomously controls the process and may report status back to a operator

23Question: Why manual override has to be included in every automatic control systems?

Regulator vs ServoRegulator vs. Servo

• Regulatory control Servo controlRegulatory control– Follow constant

setpoint, overcoming

Servo control• Follow the changing

setpoint

7.00 AM: 80 C…8.00 AM: 70 C…9 00 AM: 60 C

o

oo

the disturbance

75.5 C…75.3 C…75.4 C…

ooo

9.00 AM: 60 C…

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Open-loop vs Closed-loopDV

Open loop vs. Closed loop• Open-loop

PVCOProcess

Decisions

C t ll

p p– Process is controlled based

on predetermined scenarioEx.: When food is done in an

Controller

SP

oven, timers on outdoor lights

DV

PVCOP

• Closed-loop– The information from sensor

Decisions

Controller

Process– The information from sensor is used to adjust the MV to obtain the desired value of the PV

25SP

Control Strategies (1)• Feedback Control

C ti ti b d i bl (PV)

Control Strategies (1)

– Corrective action based on process variable (PV)

DV

SPSP PVFeedbackController

COProcess

AdvantageRequires no knowledge of the source or nature of disturbances, and minimaldetailed information about how the process itself works

DisadvantageController takes some corrective actions after some changes occurs in processvariable PV

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Control Strategies (2)• Feedforward Control

B d th t f di t b (DV) f df d t ll

Control Strategies (2)

– Based on the measurement of disturbance (DV) feedforward controller can respond even before any changes occurs in PV

DV

Advantage

SPPVCOFeedforward

Controller Process

AdvantageController takes some corrective actions before the process output is differentfrom the setpoint theoretically, perfect disturbance rejection is possible!

Disadvantage• Requires process model which can predict the effect of disturbance on PV• Requires process model which can predict the effect of disturbance on PV• If there are some modeling error, feedforward control action will be erroneous (no

corrective action)• Feedforward controller can be quite complex

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Control Strategies (3)Control Strategies (3)• Feedback/Feedforward Control

– Feedforward controller will adjust CO as soon as the DV is detected– If the feedforward action is not enough due to model error,

measurement error and etc., feedback controller will compensate the

DV

PVCO

difference

SPPVCOFeedforward/

FeedbackController

Process

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Control Strategies (4)Control Strategies (4)• Cascade Control

– The disturbance DV1 arising within the inner loop are corrected by the inner controller before it can affects the PV of the outer oneExample: Control valve + positioner

DV

SP

DV1Inner loopOuter loop

SP PVCOOuter FeedbackController

Inner FeedbackController

InnerProcess

OuterProcess

CO

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Control Strategies (5)Control Strategies (5)

• Feedback/Feedforward + Cascade Control

DVOuter loop

Feedback/Feedforward + Cascade Control

SP PVCOOuter FeedbackC ll Inner Feedback Inner O ter

DV1

CO

Inner loop

PVCOController Inner FeedbackController

InnerProcess

OuterProcess

CO

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SISO vs MIMOSISO vs. MIMO• Based on how many PV and MV we have in a process

DVs

SISO MIMOy p

DV

PVCOProcess PVCO Process……

……

Decisions

Controller

Process PVsCOsDecisions

Process

…………

…………

Controller

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Performances of Process Control System

• Closeness to setpoint• Short transient to one setpoint to other setpoint

1

2

y

• Short transient to one setpoint to other setpoint• Smaller overshoot and less oscillation• Smooth and minimum changes of variable

2

Smooth and minimum changes of variable manipulation

• Minimum usage of raw materials and energy1, 2

1, 2

1

2Regulator

Servo

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Istilah-istilah (II)• Manual control Servo control

Istilah istilah (II)

• Automatic control• Open-loop control

Servo controlRegulatory controlSISO controlMIMO control• Closed-loop control

• Feedback controlf

MIMO controlTransient responseOvershootO ill ti• Feedforward control

• Cascade controlOscillation

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RingkasanRingkasan

• Control has to do with adjusting manipulatedControl has to do with adjusting manipulated variables of the process to maintain controlled variables at desired values

• All control loops have a controller, an actuator, a process and a sensor/transmitterprocess, and a sensor/transmitter

• Various controller strategies can be realized to hi d i d bj ti & d tachieve desired process objectives & product

specifications

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