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(1)

Brief history of and introduction

to process control

Flavio Manenti

(2)

Evolution of control theory

• 300 BC

 A float regulator was employed by ancient Greece for the water clock of

Ktesibios

• 1769

 The first automatic feeback controller used in an industrial process was

the James Watt’s flyball governor, invented to control the speed of a steam engine

• 1868

 Mathematical analysis of a feedback system via differential equations by

Maxwell

• 1930s

 Frequency domain analysis techniques by Nyquist (1932) to explain

stability problems. Next, Bode (1964) and Black (1977)

• Prior to the 1940s

 The most of chemical processing plants were run essentially manually

(3)

• ’40s (and early ’50s)

 With increasing labor and equipment costs and with the development of

more severe, higher-capacity, higher-performance equipment and processes, it became uneconomical and often impossible to run plants without automatic control devices

 At this stage, feedback controllers were added to the plants with little real

consideration of or appreciation for the dynamics of the process itself

 Rule-of-thumb guides and experience were the only design techniques

• ’60s

 Chemical engineers began to apply dynamic analysis and control theory

to chemical engineering processes. Most of the techniques were adapted from the work in the aerospace and electrical engineering fields

 The concept of examining the many parts of a complex plant together as

a single unit

(4)

• ’70s

 Rapid rise in energy prices

 Additional needs for effective control systems. The design and redesign of

many plants to reduce energy consumption resulted in more complex, integrated plants that were much more interacting.

 The challenges to the process control engineer have continued to grow

over the years

• 1978-1979

 Richalet et al. (1978) and Cutler and Ramaker (1979) invented the Model

Algorithmic Control and the Dynamic Matrix Control, both belonging to the so-called Linear Model Predictive control family

• ’80s and ’90s

 Large diffusion and best industrial practice in oil&gas, refinery, and

petrochemical processes

From reacting to predicting

technologies

(5)

• Late ’90s, 2000s

 Increase in computational power, parallel computing

 Diffusion of the so-called nonlinear model predictive control (NMPC)

techniques

 The intrinsic nonlinear behavior of chemical processes leads to larger

tangible benefits using the NMPC

• What next?

 Real-time dynamic optimization

 Dynamic model-based scheduling and planning of the production  Receding/rolling moving horizon methodologies to handle market

uncertainties

 Business-wide and enterprise-wide process control  Global dynamic optimization

 Self-optimizing control  …

• Up to the next process control engineers/scientists

(6)

Introduction to process control

• con·trol

 transitive verb, 1. to check, test, or verify by evidence or experiments, 2. to

exercise restraining or directing influence over.

 noun, 1. an act or instance of controlling; also: power or authority to guide or

manage, 2. a device or mechanism used to regulate or guide the operation of a machine, apparatus, or system

• “There are three general classes of need that a control system is called to

satisfy:

 suppressing the influence of external disturbances,  ensuring the stability of a chemical process,

 optimizing the performance of a chemical process”

– George Stephanopoulos

• “The best way to illustrate what we mean by process dynamics and control is

to take a few real examples” – William Luyben

• Basic examples:

 Gravity-flow tank  Heat exchanger  Chemical plant

(7)

Gravity-flow tank

• Atmospheric tank

• Incompressible (constant density) liquid is pumped at a variable rate

F

0

. This rate can vary with time because of changes in the upstream

operations

h

is the height of liquid in vertical cylindrical tank

F

is the flow rate exiting the tank

• Time-dependent:

F

0(t)

,

h

(t)

,

F

(t)

• Liquid leaves the base of the tank via a long horizontal pipe and

discharges into the top of another atmospheric gravity-flow tank

F0

h

F F

(8)

Gravity-flow tank

• Steady-state conditions:

 By steadystate we mean, in most systems, the conditions when nothing is

changing with time

 Mathematically, this corresponds to having all time derivatives equal to

zero

 At steady-state, the flow rate out of the tank must equal the flow rate

into the tank:

 A certain corresponds to a given . The value of h would be that

height that provides enough hydraulic pressure head at the inlet of the pipe to overcome the frictional losses of liquid flowing down the pipe.

 The higher the inlet flow rate, the higher the liquid level will be

F0 h F F 0 FF

h

F

0 8
(9)

Gravity-flow tank

• Tank design:

 Traditionally based on steady-state considerations and models

 The design of the system would involve an economic balance between

the cost of a taller tank and the cost of a bigger pipe, since the bigger the pipe diameter the lower is the liquid height

 But when F0(t) varies, what path will be followed by the liquid level to get

to the new steady-state?

0 F

h

120% 0

F

120%

h

[kg/h] [m] 9
(10)

Gravity-flow tank

• Considering the process dynamics after a step change in the inlet

flowrate, from a certain value to the maximum value considered in

designing the tank, the liquid level however exceeds the tank height:

3 3.5 4 4.5 5 5.5 0 100 200 300 400 500 600 700 800 Speed [ft/s] time [s] Volumetric Flowrate "Res.ris" u 1:2 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 0 100 200 300 400 500 600 700 800 Liquid Level [ft] time [s] Tank Level "Res.ris" u 1:3

Maximum

flow entering

the tank

120% 0

F

Not

consistent

with the real

behavior

(11)

Heat exchanger

• An oil stream passes through the tube side of a tube-in-shell heat

exchanger and is heated by condensing steam on the shell side

• The steam condensate leaves the heat exchanger through a steam

trap

• Objective: control the temperature of the oil leaving the heat

exchanger

TT TC TRAP Oil feed F, T0 Heat exchanger Steam T Thermocouple [mV signal] Trasmitter [mA signal] Controller Valve FS 11
(12)

Chemical plant

CW CW Feed tank component A Feed tank component B Feed pump CSTR Bottom cooler Feed preheater Condenser Reflux drum Re flu x Reboiler D is tilla tio n c olu m n Steam CW Overhead product Bottom product 12
(13)

Chemical plant

CW CW CW Steam Feed tank component A Feed tank component B Feed pump Overhead product CSTR Bottom Bottom cooler Feed preheater Condenser Reflux drum Re flu x Reboiler D is tilla tio n c olu m n FC FC LC TC TC LC FC TC LC PC PC 13
(14)

Chemical plant

• We introduced the minimum amount of controls that would be

needed to run this plant automatically without constant operator

attention

• Even in this simple plant with a minimum of instrumentation, the total

number of control loops is 11

• The most chemical engineering processes are multivariable

(15)

• Dynamics: time-dependent behavior of a process

 The behavior with no controllers in the system is called the openloop

response

 The dynamic behavior with feedback controllers included with the process

is called the closedloop response

• Variables

 Manipulated variables: typically flow rates of streams entering or leaving

a process that we can change in order to control the plant

 Controlled variables: flow rates, compositions, temperatures, levels, and

pressures in the process that we will try to control: • either trying to hold them as constant as possible

• or trying to make them follow certain desired time trajectory

 Uncontrolled variables: variables in the process that are not controlled  Load disturbances: flow rates, temperatures, or compositions of streams

usually entering the process

• We are not free to manipulate them

General concepts to be familiar

with

(16)

• They are set by upstream or downstream parts of the plant. The control system must be able to keep the plant under control despite the effects of these disturbances

Example: distillation column

Feed flow rate F Feed composition z

Reflux flow rate R

Reboiler duty QR

Distillate flow rate D Bottom flow rate B

CW flow rate FW

Distillate composition xD

Bottom composition xB

Level reflux drum MR

Level base MB Pressure P Tray N temperature Tray 1 temperature … LOAD DISTURBANCES MANIPULATED VARIABLES CONTROLLED VARIABLES UNCONTROLLED VARIABLES

General concepts to be familiar

with

(17)

General concepts to be familiar

with

• Feedback control

 The traditional way to control a process is to:

• measure the variable that is to be controlled

• compare its value with the desired value (called the setpoint to the controller) • feed the difference (the error, the deviation) into a feedback controller that

will change a manipulated variable to drive the controlled variable back to the desired value

 The information is thus “feed back” from the controlled variable to a

manipulated variable PROCESS FEEDBACK CONTROLLER MEASUREMENT DEVICE Disturbance Manipulated variable Controlled variable Setpoint Control valve 17

(18)

General concepts to be familiar

with

• Feedforward control

 The disturbance is detected as it enters the process and an appropriate

change is made in the manipulated variable such that the controlled variable is held constant

 Thus, we begin to take the corrective action as soon as a disturbance

entering the system is detected (instead of waiting for the disturbance, as we do with feedback control)

PROCESS FEEDFORWARD CONTROLLER MEASUREMENT DEVICE Disturbance Manipulated variable Output Setpoint Control valve 18

(19)

General concepts to be familiar

with

• Stability

 A process is said to be unstable if its output becomes larger and larger

(either positively or negatively) as time increases

 No real system really does this, since variables can move within certain

constraints

 A linear process is at the limit of stability if it oscillates, even when

undisturbed, and the amplitude of the oscillations does not decay

 Most processes are openloop stable

 All the real processes can be made closedloop unstable (if the controller

gain is made large enough)

• Thus, stability is of vital concern in feedback control systems

• The performance of a control system is the ability to control the

process tightly

 It usually increases as we increase the controller gain (within the stability

limits)

• The robustness of the control system is the tolerance to changes in

process parameters.

 It decreases when a small change will make the system unstable

(20)

Certain basic considerations

• First:

 The simplest control system that does the job is the best

 Complex elegant control systems look great on paper but soon end up on

“manual” in an industrial environment

 Bigger is definitely not better in control system design

• Second:

 You must understand the process before you can control it

 The use of complex controllers do not lead to overcome ignorance about

the process fundamentals

 Learn how the process works before you start designing its control

system

(21)

Course program

Outlier Detection Robust methods Linear/nonlinear Regressions Performance Monitoring Yield Accounting Soft sensing Data Reconciliation Mathematical Modeling Dynamic Simulation Model Predictive Control Optimization Model Reduction

DCS, OTS, Plantwide control, Soft sensing, process transients,

grade/load changes Solvers

Planning Scheduling Dynamic optimization Distributed predictive control Nonlinear Systems Optimizers Differential systems Stiff systems ODE,DAE,PDE,PDAE Efficiency Decisions Raw Data Parallel Computing Uncertainties Optimal production Optimal grade changes Multi-objective Real-time optimization High accuracy Reliable process control Production improvement Economy Just in time Market-driven Logistics Corporate Supply Chain 21

(22)

Distributed control system (DCS)

• The information is transferred from the field to the control-room (and

decisions are sent back to the field) through different steps:

 Instrumentation installed by the field

• Thermocouples, flow and pressure measurements, analyzers…

 Field barriers (junction boxes)  Fieldbus

• Redundancy, ring, switches

 Marshalling (technical room)

• Cabinets, racks, I/O modules, CPU slots, redundancy, internal redundancy

 Servers

• OPC, web-server, operations/alarm, engineering, diagnostics, alarm management, archive/historian, HMI, synchronization

 Clients (Software)

• Data processing, steady-state simulators, dynamic simulators, soft sensing, data reconciliation, maintenance scheduler, advanced process control, model predictive control, process optimization, dynamic optimization, supply chain management, production accounting, blending, operator training simulators, performance monitoring, enterprise resource planning, movement tracking

(23)

Distributed control system (DCS)

• The general architecture:

CPU

Fieldbus

BUS

BUS

OS Server ROUTE Control

BATCH Server CAS

Engineering / Maintenance Station Client Station WEB Server IT Industrial Ethernet Office LAN (Ethernet)

WEB Client OS LAN (Ethernet) Filedbus Fieldbus CPU CPU CPU CPU CPU

FIELD

CLIENT

SERVER

Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH

(24)

Application server

Courtesy of G. Bussani, HONEYWELL Inc

(25)

Control areas

FIELD

TECHNICAL AREA

FI

EL

D

TEC

H

N

IC

A

L A

R

EA

INSTRUMENTATION MARSHALLING DCS FUNCTIONS

Courtesy of G. Bussani, HONEYWELL Inc.

(26)

Generations of DCS

Next Generation Engineering Diagnostic Web Server (Java) Archive Operation/ Alarm Engineering OPC AMS Classical

Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH

(27)

Simpler and safer architecture

Field Integrated SIS Controls Integrated IEC 61850 Dual Path and Media Redundant Enterprise Level Automation Built-in Web Server OPC Server AMS Integrated Java / xml Thin Clients Remote Thin Clients Control Room Remote Clients OPC System SW Web Server (Java) System SW Operation/ Alarm Data System SW Engineering Data System SW Data System SW Diagnostic Archive Data System SW AMS Data System SW

Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH

(28)

IT and security

• Wireless infrastructures

• Secure access (local commissioning, diagnostics, and maintenance;

cyber security; intrusion detection and prevention)

Thin Clients Application Server Automation Server IO modules HMI level DMZ Terminal Server Firewall Proxy Module Access Points Firewall/Router Wireless Clients Application HMI level Intranet Internet Automation DMZ Siemens Remote Service Firewall/ VPN Router Thin Client Firewall VPN Tunnel Terminal Server OPC Tunnel

Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH

(29)

Service and maintenance

Field Engineer On standby for all

events

Online communication

Know-how as if we were there

Spare parts logistics The key to increase availability

Regional service centers

Your local task force

at your site Remote expert Center Experts available any time

• 24 hours a day • 365 days a year • Wherever needed

Courtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH

(30)

Cutting edge solutions

• DCS are continuously and fast evolving (i.e. new trends in

control-room and field operator training)

Courtesy of M. Rovaglio, INVENSYS ltd – SCHNEIDER ELECTRIC GmbH

References

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