Brief history of and introduction
to process control
Flavio Manenti
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
• ’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
• ’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
• 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
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
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
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 F F
h
F
0 8Gravity-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% 0F
120%h
[kg/h] [m] 9Gravity-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% 0F
Not
consistent
with the real
behavior
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 11Chemical 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 12Chemical 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 13Chemical 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
• 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
• 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
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
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
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
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
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 ReductionDCS, 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
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
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
Application server
Courtesy of G. Bussani, HONEYWELL Inc
Control areas
FIELD
TECHNICAL AREA
FI
EL
D
TEC
H
N
IC
A
L A
R
EA
INSTRUMENTATION MARSHALLING DCS FUNCTIONSCourtesy of G. Bussani, HONEYWELL Inc.
Generations of DCS
Next Generation Engineering Diagnostic Web Server (Java) Archive Operation/ Alarm Engineering OPC AMS ClassicalCourtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH
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 SWCourtesy of S. Cavagnaro and S. Garramone, SIEMENS GmbH
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
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
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