ChE328 Process Instrumentation and Control

• Lecturer: Dr Qin Li (204:432), Dr Tonghua Zhang (204:523)

• Tutors: Faye Chong, Shahin Hosseini, Hongfei Fang

• Online teaching: Blackboard (informational)

• Textbook– Riggs, J B; Karim, M. Nazmul (2006) : Chemical and Bio-Process Control, 2nd Ed, Ferret Publishing,

Texas, USA

• References:– Marlin, T E (1995): Process Control – Designing Processes and Control Systems for Dynamic

Performance, McGraw-Hill, Inc. NY.– Seborg, D E, Edgar, T F and Mellichamp, D A (1989): Process Dynamics and Control, John Wiley and

Sons, NY.– Luyben, W L (1990): Process Modeling, Simulation and Control for Chemical Engineers, McGraw-Hill

Pub Co, NY.– The MathWorks, Inc, (1995): Matlab Version 4 User’s Guide, Student Edition, Prentice Hall.

Overview of Course Material

• Control loop hardware (Chap 2)• Dynamic modeling (Chap 3)• Transfer functions and idealized dynamic

behavior (Chap 4-6)• PID controls (Chap 7-11)• Advanced PID controls (Chap 12-14)• Control of MIMO processes (Chap 15-18)

• ChE 328 Learning Outcomes:– Understanding and modelling of the transient

behaviour of dynamic systems.– Introduction to the theory and practice of

automatic control.– Introduction to the design and implementation

of feedback control systems as well as the concept of closed-loop stability.

• CHE 326 Process Control (Detailed Unit Outline)• Introduction (Week 1)

– Introduction to process control– Control objectives and benefits– Economic justification of process control

• Dynamic Modelling of Chemical Processes (Weeks 1, 2 and 3)– Uses of process models– Process modelling principles– Types of process models

• State-space model• Transform-domain model• Frequency-response model• Impulse-response model

– Transfer function• Transfer function development• Properties• Block diagrams

• Dynamic Behaviour of Ideal Systems (Weeks 4 and 5)– Concepts of poles and zeros– Linear low order systems

• Pure gain systems• Pure capacity systems• First order systems• Lead/Lag systems

– Linear higher order systems• First order systems in series• Second order systems• Nth-order systems• Higher order systems with zeros

– More complex systems• Inverse response systems• Time delay systems• Multi-input multi-output processes

• Development of Dynamic Models from Experimental Data(Week 6)

– Process identification– Process reaction curve

• Basic Components of Control Systems (Week 7)– Feedback controllers and PID control– Control system instrumentation

• Design of Single-loop Control Systems (Weeks 8 and 9)

– Dynamic behaviour of closed-loop control systems– Stability of closed-loop control systems– Controller design based on transient response criteria

• Controller Tuning Techniques (Weeks 10 and 11)• Introduction to Frequency Response Analysis (Weeks

11 and 12)– Frequency response techniques– Controller design using frequency response criteria

Fundamental Understanding and Industrially Relevant Skills

• Fundamental Understanding-– Laplace tranforms and transfer functions (Ch 4,5)– Idealized dynamic behavior (Ch 6)– Frequency response analysis (Ch 11)

• Industrially Relevant Skills-– Control hardware and troubleshooting (Ch 2&10)– Controller Implementation and tuning (Ch 7-9)

• Constituents– Lectures– Plant visit (TBA, middle of semester)– Computer lab projects (Control Station, week3)

• Assessment Details:– Assignments ( 3) 15%– Project (Group Work) 15%– Plant Visit 10%– Test 10% (Week 7, 2009)– Examination 50%– Pass mark is 50% in all assessments

Chemical and Bio-Process Control

James B. Riggs, M. Nazmul Karim

Chapter 1

Introduction

Why do we learn Process Control as chemical engineers?

What’s going on in a chemical process?

Mass transfer

Heat transfer

Chemical Reaction

Mass balance

Energy balance

Mole balance

Steady-state? or dynamic?

A Career in Process Control

• Requires that engineers use all of their chemical engineering training (i.e., provides an excellent technical profession that can last an entire career)

• Can become a technical “Top Gun”• Allows engineers to work on projects that

can result in significant savings for their companies

A Career in Process Control

• Provides professional mobility. There is a shortage of experienced process control engineers.

• Is a well paid technical profession for chemical engineers.

Critical Skill Set for a Process Control Engineer

• Tune controllers.• Make control design decisions: PI, PID,

ratio, cascade, feedforward, or DMC.• Troubleshoot control loops.• Understand the terminology of the

profession

Importance of Process Control for the CPI

• PC directly affects the safety and reliability of a process.

• PC determines the quality of the products produced by a process.

• PC can affect how efficient a process is operated.

• Bottom Line: PC has a major impact on the profitability of a company in the CPI.

Safety 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 good operating region:– Thunderstorms, feed composition upsets,

temporary loss of utilities (e.g., steam supply), day to night variation in the ambient conditions

Benefits of Improved Control

Time

Impu

rity

Conc

entra

tion Limit

Old Controller

Benefits of Improved Control

Time

Impu

rity

Conc

entra

tion Limit

Time

Impu

rity

Con

cent

ratio

n Limit

Old Controller

New Controller

Better Control Means Products with Reduced Variability

• For many cases, reduced variability products are in high demand and have high value added (e.g., feedstocks for polymers).

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

Benefits of Improved Control

Time

Impu

rity

Conc

entra

tion Limit

Time

Impu

rity

Con

cent

ratio

n Limit

Time

Impu

rity

Conc

entra

tion Limit

Old Controller

New Controller

Improved Performance

Maximizing the Profit of a Plant

• Many times involves controlling against constraints.

• The closer that you are able to operate to these constraints, the more profit you can make. For example, maximizing the product production rate usually involving controlling the process against one or more process constraints.

Importance of Process Control for the Bio-Process Industries

• Improved product quality.• Faster and less expensive process validation.• Increased production rates.

Justification of Process Control• Specific objectives of process control increased product throughput increased yield of higher valued products decreased raw material cost decreased energy consumption decreased chance or excessive pollution decreased off-spec product safety extend life of equipment operability decreased production labour

What are in Process Control?

Driving a Car: An Everyday Example of Process Control

Driving a car• Control Objective (Setpoint): Maintain car

in proper lane.• Controlled variable- Location on the road• Manipulated variable- Orientation of the

front wheels• Actuator- Driver’s arms/steering wheel• Sensor- Driver’s eyes• Controller- Driver• Disturbance- Curve in road

Constraint Control – The Shower Example

• Degrees of freedom - hot tap- cold tap

• ConstraintsEquipment constraints - full off

- full on

Material constraints - scalding limit (burning)- freeze limit- rinse limit- impingement limit

• Objective functionminimum electricity costmaximum scouring effectiveness

Where is the optimum operating point?

• Control aspects

measure exit water temperature

Adjust COLD or HOT tap!!

Constraint Control Example

• Consider a reactor temperature control example for which 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 higher reactor temperature and thus more profit.

Heat Exchanger Control: ChE Control Example

TT

Condensate

Steam

Feed

TCProductStream

Heat Exchanger Control

• Controlled variable- Outlet temperature of product stream

• Manipulated variable- Steam flow • Actuator- Control valve on steam line• Sensor- Thermocouple on product stream• Disturbance- Changes in the inlet feed

temperature

DO Control in a Bio-Reactor

Air

AC

Variable SpeedAir Compressor

AT

Setpoint

DO Control

• Controlled variable- the measured dissolved O2 concentration

• Manipulated variable- air flow rate to the bio-reactor

• Actuator- variable speed air compressor• Sensor- ion-specific electrode in contact

with the broth in the bio-reactor• Disturbance- Changes in the metabolism of

the microorganisms in the bio-reactor

Logic Flow Diagram for a Feedback Control Loop

Controller Actuator Process

Sensor

CVSetpoint

Disturbance

+-uce

Comparison of Driving a Car and Control of a Heat Exchanger

• Actuator: Driver’s arm and steering wheel vs. Control valve

• Controller: the driver vs. an electronic controller

• Sensor: the driver’s eyes vs. thermocouple• Controlled variable: car’s position on the

road vs. temperature of outlet stream

The key feature of all feedback control loops is that the measured value of the controlled variable is compared with the setpoint and this difference is used to determine the control action taken.

Process Control Concept and Conventions

• Responsibility of Control System– Monitor process outputs by measurement– Make decisions regarding corrective actions– Implement decisions effectively on process

• Type of Control Systems– Manual control systems (human operators)– Automatic control system (computer control)

Standard Control System Structure

Typical Control Loop

Types of Feedback Controllers

• On-Off Control- e.g., room thermostat• Manual Control- Used by operators and based on

more or less open loop responses• PID control- Most commonly used controller.

Control action based on error from setpoint (Chaps 6-8).

• Advanced PID- Enhancements of PID: ratio, cascade, feedforward (Chaps 9-11).

• Model-based Control- Uses model of the process directly for control (Chap 13).

Duties of a Control Engineer

• Tuning controllers for performance and reliability (Chap 7)

• Selecting the proper PID mode and/or advanced PID options (Chap 6, 10-12)

• Control loop troubleshooting (Chap 2)• Multi-unit controller design (Chap 14)• Documentation of process control changes

Characteristics of Effective Process Control Engineers

• Use their knowledge of the process to guide their process control applications. They are “process” control engineers.

• Have a fundamentally sound picture of process dynamics and feedback control.

• Work effectively with the operators.

Operator Acceptance

• A good relationship with the operators is a NECESSARY condition for the success of a control engineer.

• Build a relationship with the operators based on mutual respect.

• Operators are a valuable source of plant experience.

• A successful control project should make the operators job easier, not harder.

Process Control and Optimization

• Control and optimization are terms that are many times erroneously interchanged.

• Control has to do with adjusting flow rates to maintain the controlled variables of the process at specified setpoints.

• Optimization chooses the values for key setpoints such that the process operates at the “best” economic conditions.

Optimization and Control of a CSTR

Feed

Steam

Product

FT

TT

TemperatureController

Flow Setpoint

Optimizer

TemperatureSetpoint

FC

Optimization Example

balances.molefromcalculatedareandLikewise,

]/exp[1

forSolving]/exp[

:AonbalanceMole

11

0

110

CB

r

AA

A

rAAA

CCQ

VRTEkCC

CVCRTEkCQCQ

CBA

−+

=

−−−

→→

Economic Objective Function

AFACCBBAA VCQVCQVCQVCQ 0−++=Φ

• VB > VC, VA, or VAF

• At low T, little formation of B• At high T, too much of B reacts to form C• Therefore, there exits an optimum reactor

temperature, T*

Optimization Algorithm

• 1. Select initial guess for reactor temperature

• 2. Evaluate CA, CB, and CC

• 3. Evaluate Φ• 4. Choose new reactor temperature and

return to 2 until T* identified.

Graphical Solution of Optimum Reactor Temperature, T*

Process Optimization• Typical optimization objective function, Φ: Φ = Product values-Feed costs-Utility costs

• The steady-state solution of process models is usually used to determine process operating conditions which yields flow rates of products, feed, and utilities.

• Unit costs of feed and sale price of products are combined with flows to yield Φ

• Optimization variables are adjusted until Φis maximized (optimization solution).

Generalized Optimization Procedure

NumericalOptimization

Algorithm

ProcessModel

EconomicParameters

EconomicFunction

Evaluation

OptimizationVariables

EconomicFunction

Value

ModelResults

Initial Estimateof Optimization

Variables

OptimumOperatingConditions

Optimization and Control of a CSTR

Feed

Steam

Product

FT

TT

TemperatureController

Flow Setpoint

Optimizer

TemperatureSetpoint

FC

Control System Hardware Elements

Sensors (measuring devices or primary elements)– Thermocouple for temp measurements– Differential pressure cells for liquid level

measurements– Gas/liquid chromoatographs for composition

measurements– Etc

Controllers (decision maker with built-in intelligence)– Pneumatic (operates on air signals)– Electronic (common in industry)– Digital computer (complex control operations)Transmitters (process info transfer)Final control elements (actually implement command

signals on the process)– Control valves (usually pneumatic)– Variable speed fans– Pumps and compressors– Conveyors– Relay switches

Other hardware elements– Transducers (signal transformations)– A/D and D/A for computer control

Other control terminologiesSet-points (target value)Regulatory controlServo controlOpen-loop control (simple timing devices)Closed-loop control

Control System Symbols for Process and Instrumentation Diagrams

Sample Identification Letters

Symbol First Letter Succeeding Letter A Analysis Alarm C Conductivity Control D Density E Voltage F Flowrate I Current Indicate L Level M Moisture (humidity) P Pressure or Vacuum T Temperature Transmit V Viscosity Valve

Typical Actuator Symbols

Typical Sensor Symbols

Examples of Control Loops

`

Feedback Control Loop

Illustrative Example

Steam

TT

TC

C Product

LC

LC

LC

TT TC

Fresh BFeed

Fresh AFeed

PT

Steam

Illustrative Example

Steam

TT

TC

C Product

LC

LC

LC

TT TC

Fresh BFeed

Fresh AFeed

PT

LS

PC

(FB)sp

×R/F

S

×

L/F

Steam

Overview• All feedback control loops have a

controller, an actuator, a process, and a sensor where the controller chooses control action based upon the error from setpoint.

• Control has to do with adjusting flow rates to maintain controlled variables at their setpoints while for optimization the setpoints for certain controllers are adjusted to optimize the economic performance of the plant.

Pneumatic Controllers - Phase I

• Introduced in the 1920’s• Installed in the field next to the valve• Use bellows, baffles, and nozzles with an

air supply to implement PID action.• Provided automatic control and replaced

manual control for many loops

Pneumatic Controllers - Phase II

• Transmitter-type pneumatic controllers began to replace field mounted controllers in the late 1930’s.

• Controllers located in control room with pneumatic transmission from sensors to control room and back to the valve.

• Allowed operators to address a number of controllers from a centralized control room.

Pneumatic Controller Installation

T

F2

T2

Thermocouplemillivolt signal

TransmitterPneumaticController

3-15 psig

Tsp

Air

F1

T1

Thermowell

3-15 psig Air

Electronic Analog Controllers

• Became available in the late 1950’s.• Replaced the pneumatic tubing with wires.• Used resistors, capacitors, etc. to implement

PID action.• Out sold pneumatic by 1970.• Allowed for advanced PID control: ratio,

feedforward, etc.

Analog Controller Installation

T

F2

T2

Thermocouplemillivolt signal

Transmitter4-20 maElectronic

AnalogController

3-15 psig

4-20 ma

Tsp

I/PAir

F1

T1

Thermowell

Computer Control System

• Based upon a mainframe digital computer.• Offered the ability to use data storage and

retrieval, alarm functions, and process optimization.

• First installed on a refinery in 1959.• Had reliability limitations.

Supervisory Control Computer

PrinterVideo DisplayUnit

InterfacingHardware

AnalogControl

Subsytem

AlarmingFunctions

Supervisory Control Computer

Data StorageAcquisition

System

...

Distributed Control System- DCS

• Introduced in the late 1970’s.• Based upon redundant microprocessors for

performing control functions for a part of the plant. SUPERIOR RELIABILITY

• Less expensive per loop for large plants.• Less expensive to expand.• Facilitates use of advanced control.

DCS Architecture

Process Transmitters and Actuators

Data Highway(Shared Communication Facilities)

LocalConsole

LocalControl

Unit..............

DataStorage

Unit

HostComputer

SystemConsoles PLC

LocalControl

Unit

LocalConsole

Current Approach

• DCS’s in wide use and continuing to replace analog controllers.

• Model predictive control, particularly DMC, is the standard for advanced control with over 3000 applications world-wide.

• DMC is used to control multi-unit processes while maximizing the process throughput, i.e., controlling against limiting constraints.

DMC

• DMC is a multivariable controller.• DMC uses step response models, i.e., it can

model complex dynamics.• It is a time horizon controller, i.e., it

predicts process behavior in to the future.

Multivariable Controller

y1(s)

y2(s)u2

u1 G11(s)++

G21(s)

G12(s)

G22(s)

++

MultivariableControllery2,sp

y1,sp

Time Horizon Controller

-10 -5 0 5 10Time

y

Past Behavior

Future Prediction

Setpoint

-10 -5 0 5 10Time

u

Past Inputs

Future Inputs

Process Optimisation

• Determine the set of setpoints for the controllers that maximise profit

• Optimisation is now where process control was 20 years ago

• Larger applications (e.g., refinery-wide optimisation) are on the horizon

• Enterprise-wide optimisation is in the future.