SMART CONTROL FOR TOMORROWS
PROCESSES
The process modelling journey
70
Author – Professor R S Benson FREng
Director of Technology
ABB UK
60
Dynamic
Dynamic
solution
Sequential
40
30
models
Steady state
sequential
20
Graphical
A quotation that the author finds particularly
significant is as follows: -
10
Process Modelling
Process Control
Digital Communication
Supply Chain Performance
All four are approaching a common point that
demands smart control for tomorrows
processes.
PROCESS MODELLING
The process industries involve some form of
chemical or physical transformation. These
transformation processes are often non-linear,
poorly understood and preparing a dynamic
model is difficult. A typical example would be
an agrochemical batch reactor where the
kinetics is complex with main and side
reactions.
The process-modelling journey is illustrated
below.
modelling
techniques
0
60's
70's
80's
90's
00's
© ABB - 47
50's
This paper builds on the author’s experience of
over thirty years in the process industries and
process control learning from other people.
During that period a number of significant
trends became apparent which indicate the
direction for the future. The particular trends
selected are: -
g-proms
solution
Dynamic
INTRODUCTION
“Smart people learn from experience, wise
people learn from others”.
reconciliation
sequential
50
It was in the early sixties that Professor Roger
Sergeant at Imperial College (Reference 1), ICI
and others started to explore the potential for
using computers to solve the basic unit
operations of chemical engineering. Prior to
that graphical techniques were used to design
these unit operations such as distillation
columns. Given that the model of a distillation
column is a series of counter current equations,
this was ideally suited to the early digital
computers.
The incentive was to significantly reduce the
time and effort involved while improving the
design accuracy. Models were totally steady
state but nonetheless proved of great benefit.
The techniques used involved flow sheeting
languages that have since evolved into the
solutions now offered by companies like Aspen
and Hyprotech. A feature of these early models
is that they solve them in a sequential manner
that follows the process. E.g. solve the reactor
equations and use the outputs as the inputs to
the distillation column. While this approach felt
intuitively correct, the drawback was the
relatively slow speed of solution.
In the late seventies increasing computer power
plus research into the basic mathematics and
chemical engineering, encouraged researchers
to consider the solution of dynamic processes
models. Again Imperial College was the leader
and the result was the “Speed Up” product
(Reference 2). This was eventually licensed by
Aspen and is now a standard product that is
available on the market. One of the drivers was
At around the same time, the concept of
simultaneous equation-based solution was
developed. In this approach all the model
equations are solved simultaneously. This
brings real benefits in accuracy and speed.
A key component of any process model is
accurate physical properties data. Initially
government laboratories developed these
models. These have now transferred to private
ownership, which develop and manage the core
physical properties used by many of the
process mathematical models.
Over 20 years steady state modelling moved
from research to the norm in the industry and
the vast majority of engineers now use some
form of steady state modelling. This includes
the Process Engineering Library (PEL)
(Reference 3), which was originally developed
by ICI and is available from ABB and larger
packages available from companies like Aspen.
In the nineties the concept of combing equationbased solutions with dynamic models was
developed, again by Imperial College
(Reference 4). The result is g-proms, which is
the latest development in dynamic modelling
and flow sheeting. ABB have had an exclusive
licence for this technology in the automation
area and have worked in partnership with PSE
to develop their own version of g-proms
applicable to the process industries. The key
feature for this has been the development of a
dynamic data reconciliation module.
Complete Life Cycle Models for Multiple Uses
Control, equipment
design optimization
P l an t
data
Offlin e
On lin e
I nitial
m odel
Estim ation
Raw
plant
data
U p-todate
m odel
MPC
Lin ear
m odels
[ A,B ,C,D]
Advanced
MPC
Fitted
m odel
Data Reconciliation
P aram eter Estim ation
Plant
© ABB - 23
to improve the process control of the chemical
processes. As a generalisation, chemical
processes have considerable capacity within
them in the form of tanks, reboilers etc. This
often means that the dynamics are relatively
slow; hence it was possible to tackle dynamic
control problems using models of this type.
Lin earization
Linearized
models
Decision support
Operating procedure
optimization
Many others
R econciled
plan t
i nform ati on
Yield
accounting
Soft sensing
Diagnosis and
troubleshooting
Optimization (ss +
dynamic)
Dynamic reconciliation allows the user to
ensure that all the heat and material balances
of the process are reconciled at any time
without having to wait for the process to attain
steady state. This has numerous advantages to
the user in the form of accuracy, removal of
arguments on yields etc, ability to monitor leak
detection, instrument failures, heat exchanger
efficiency as well as the option to provide a “fit
and forget” model based predictive control.
Hence, the evolution of process modelling over
forty years has led to a solution where it is
potentially possible now to model dynamically
almost any chemical process and to apply the
latest advanced control techniques on a “fit and
forget” basis. This means there is no longer
requirement for the time consuming step tests
and recalibration of the previous multi-variable
control models. It is an example where process
modelling and process control is now coming
together to the benefit of process industries.
PROCESS CONTROL
Process control has also gone through a similar
cycle over the last forty years. This is illustrated
in the diagram below.
The process control journey
70
60
Multi-variable
Statistical
50
Model based
40
predictive
DCS
Electronic
Pneumatic
controller
10
control
process
control
control
Systems
Multivariable
30
20
Parametric
control
controllers
Single loop
Single loop
0
60's
70's
80's
90's
00's
© ABB - 48
50's
In this area the industrial applications have
followed the research applications at
universities.
In the fifties the research focused on single loop
controllers with the work of Bode and Nichols
developed single loop control theory. The
process industries followed, though initially they
were inhibited by the limitations of pneumatic
controllers. Advanced control was restricted to
ratio or possibly feed forward control.
In the sixties universities such as UMIST and
others began to develop the concept of multi
variable control with the work of Professor
Alistair MacFarlane. In addition, the
development of computer control demanded the
development of the theory of sample data
control. It seems interesting to reflect that it is
only thirty years ago that control engineers such
as the author had to argue the case for using
computers to control process plants. They are
now the norm.
The key however, to multi variable control in the
process industries was the development of
model based predictive control by Professor
Manfred Morari and others in the late seventies.
In this control a form of process model is
inverted to provide feedback to the control
variables. This was initially exploited
industrially by Shell and it was in the refining
area that model based predictive control really
became established. This is increasingly the
norm for the advanced control in that area and it
is also increasingly finding acceptance in other
industries where it is required. The problem
however with model based predictive control is
that it required a dynamic model of the process.
Normally this is determined by engineers
carrying out numerous disturbance tests on the
process to develop this dynamic model. While
this proves quite effective until the process
changes significantly, it is both time consuming,
and it is difficult to know exactly how good the
model based predictive control is compared
with best possible.
In the nineties the learning from the nonprocess industries began to spread into the
process industries. One only has to go round
an electronics company or a car company to
realise that their method of control is statistical
process control. Terms like Six Sigma are
growing in importance. This is a single variant
form of control. The process industries are
usually multi-variant. University research at
Newcastle upon Tyne University and others has
developed Multi Variable Statistic Process
Control (MVSPC). This applies the concept of
statistical process control to the process
industries. This is growing in importance,
particularly in the batch processes, and will be
very significant in the first ten years of the
twentieth century.
In every case, the suppliers of process control
equipment such as ABB have followed the lead
provided by the universities, possibly with a
five-year or more gaps between the research
and the first commercial applications.
It is interesting to reflect that in the past forty
years, many of the large process companies
had large leading edge process control groups
that were able to develop and prototype
university research in industry before the
equipment suppliers developed and offered
them as standard products. Very few
companies now retain this skill and it is
increasingly the obligation of supply companies
such as ABB, to be the direct translators of
university research into industrial products.
Increasingly such companies are becoming the
first tier suppliers to the process industries.
DATA COMMUNICATION AND INDUSTRIALIT
Again, this is a history of very rapid evolution of
the last forty years. The journey is illustrated in
the figure below.
The data communication and IndustrialIT control journey
70
60
IndustrialIT
OCP
50
World-wide
standards
web
40
Fieldbus
DCS
30
Systems
This led to an industry demand for a concept,
which eventually became known as Fieldbus.
The concept of Fieldbus was that anybody’s
instruments could communicate with anybody’s
computers over a single standard interface. It is
interesting to note that the author first heard of
Fieldbus in the late seventies, and it is only now
that it is becoming the standard demanded by
the industry. A gap of almost 30 years! The
evolution of this has been long and torturous,
and it may well be suspected that it was not in
everybody’s interests to make it succeed.
DDC
20
computers
0
60's
70's
80's
90's
00's
© ABB - 49
50's
On reflection, and not surprisingly, the key
driver of this whole evolution has been the
digital computer. It is only forty years ago that
the majority of process plants used pneumatic
instrumentation. While in its own way very
clever, and very simple, it was clearly very
difficult to transfer data from these instruments
to any form of central location. In fact the best
that could be done was the operators manual
recording information that may be manually
analysed by others. Rotating pneumatic
scanning valves that converted pneumatic
signals into digital signals were developed but
these were restricted to a very few applications.
It was in the late sixties that the first direct use
of computers to control process plants was
developed by ICI in the UK and Monsanto in the
US. Unfortunately all the instruments and all
the valves were pneumatic, hence numerous
analogue to digital and digital to analogue
converters had to be installed to make this
possible. Nonetheless the principle was
established. This rapidly took off through the
seventies and computer controls were installed
in many large plants, primarily to allow concepts
such as advanced control and optimisation. For
anybody who was working in that era a real
challenge was the need to communicate with
numerous different standards of interfaces with
pneumatics, electronics and other people’s
instruments.
In the early nineties the concept of the Internet
and the worldwide web arose. This came from
a totally different direction, and was developed
for the totally different need of transferring huge
amounts of data across the world using existing
spare computer capacity. It had nothing like the
standards effort of Fieldbus nor the numerous
committees that made it possible. Nonetheless
it rapidly gained acceptance as a worldwide
standard because of its ease of use, availability
and effective low cost. As a consequence of
this, suppliers are now exploring the use of the
web for communication between instruments
and computers etc, and it may eventually
replace some components of Fieldbus.
In the nineties, ABB took a fundamental review
of the whole question of information flow both
between plants and business systems and
between components across plants. The result
was the concept of IndustrialIT (Reference 3).
IndustrialIT - What does it mean?
Industrial
IndustrialIT
IT--The
Thesuccessful
successfulcombination
combinationof
of: :
Industrial
Industrial- -Tradition,
Tradition,long-term
long-termcommitments,
commitments,stability,
stability,experience,
experience,knowledge,
knowledge,......
Creativity,
speed,
innovation,
agility,
new
technology,
IT
IT - Creativity, speed, innovation, agility, new technology,......
Industrial
IndustrialIT
IT--The
TheNext
NextWay
Wayof
ofThinking
Thinking: :
© ABB Ltd. - 2
Pneumatic
10
One
Onescalable,
scalable,consistent
consistentconcept
conceptfor
forall
allaspects
aspectsof
ofautomation:
automation:
Field
Fielddevices,
devices,drives,
drives,PLC,
PLC,DCS,
DCS,OCS,
OCS,SCADA,
SCADA,safety,
safety,substation
substationautomation,
automation,
MES,
MES,maintenance
maintenancesystems,
systems,ERP
ERPintegration,
integration,BMS
BMS......
IndustrialIT - Information availability
Aspect integrator
Production order
Production
schedule
Product
specification
Operator
interaction
Stock
report
Production
report
© ABB Ltd. - 16
Quality
report
Trend
data
Aspect integrator - Information availability
Aspects
Aspect systems
Stock report
Real
Object
Model
Object
Product
specification
Microsoft
Excel
Microsoft
Word
Operating
procedures
Production
schedule
SAP, IFS . . .
Mechanical
drawing
AutoCAD
© ABB Ltd. - 17
To put the significance of IndustrialIT into
context is probably appropriate to remind
people that there was a time before Windows
Office when letters were typed on typewriters
and duplicates used carbon paper. That is
probably no more than thirty years ago. Within
that time typewriters have disappeared, carbon
paper is a thing for a museum, and Microsoft
Office has been the standard for all offices in
the world. ABB believes that IndustrialIT will
become the standard for communication in the
industrial world. The key and unique feature of
IndustrialIT is to recognise that every object in
the plant has information associated with it that
ABB call aspects. Consider a typical control
valve, which is an object, illustrated below.
The result from the user is a system that is
cheaper to install, whose documentation is
always up to date, and which allows
transparency of information at all levels. It is
this transparency and instant access to
information, plus the potential that it is
dynamically reconciled, that makes this system
potentially so powerful for tomorrow’s
processes.
IMPACT OF SUPPLY CHAIN
This is a much shorter but equally significant
journey as illustrated below.
The supply chain journey
70
Each object has associated with it many
aspects of information. This is all the
information that is associated with that control
valve. Quite deliberately, this is all made
available to the users in standard Microsoft or
other world leading packages. This is illustrated
below.
60
Supply chain
pressures
50
on the
process industries
40
Benchmarking
30
manufacturing
process
ERP
20
systems
World class
10
manufacturing in cars
0
60's
70's
80's
90's
00's
© ABB - 50
50's
The process industries operate in a supply
chain between raw materials supply such as oil
and final customers such as supermarkets.
Strange as it may seem this has not always
been the case. In the sixties it was a market
where demand exceeded supply in most
products, hence the only requirement was to
ensure that you could produce the product. If
available the product then it was sold and how it
was sold and the economics of sale was not
really important.
Supply chain pressures did not really impact the
process industries until the late eighties. The
impact was felt earlier in industries such as cars
and electronics. This led in the seventies to the
concepts of world class manufacturing of cars
which was first exploited in Japan and had
significant impact on the western world in the
late seventies. This led to a whole new
manufacturing paradigm with phrases such as
lean manufacture, total productive management
and others being developed into the new
industrial language.
It was only in the early nineties that it began to
be recognised that the concepts of world-class
manufacturing applied to the process industries.
The author would claim to be one of the people
that brought this thinking into the industry, and
has prepared a book on benchmarking in
process manufacturing (Reference 5), which
lays out some of the thinking behind world class
manufacturing.
A key requirement of world class manufacturing
is the recognition that you must benchmark
against world class, and that the performance
metrics refer to manufacturing performance, not
process engineering performance, the table
below gives a brief table of some of these world
class manufacturing metrics for Refining and
Petrochemicals. Similar figures apply to batch
processes.
Performance Benchmarks – Refining and Petrochemical Processes
© ABB - 6
Supply chain costs as %of sales
Manufacturing Added Value / Empl
or : Manu Added Value / Avg. Salary Cost
Customer OTIF %
Customer Complaints (%orders)
Adherence to Production Schedule
Process Capability
Product Rate
Quality Rate
Availability
Turnaround Frequency (months) *
Overall Equipment Effectiveness
Maintenance Cost as %Replacement Value
Stock Turn (process)
Supplier OTIF
Absenteeism
Training Days / Employee
Average
12%
>£200k
>3
90%
2%
70%
2
90%
90%
88%
36
75%
2.00%
20
90%
4%
5
World Class
6%
>£400k
>6
>99%
<0.01%
>98%
>3
>98%
>99%
>99%
72
>97%
<1.5%
>50
>99%
<1%
>12
To those familiar with the process industries,
measures in safety will be very familiar, but
measures such as OEE will be less familiar and
one measure in particular, supply chain costs
as a percentage of sales would probably be
totally new.
Supply chain costs as a percent of sales is
defined as all the costs associated with the
supply chain, excluding manufacturing, divided
by the sales revenue. All the costs include
things like sales, marketing, the information
management systems, the head office costs,
the cost of warehouse, the cost of distribution
etc. You will see from the above table that a
typical figure in the process industries is
probably in the order of 14% but we have
experienced it as high as 25%. World class it is
in the order of 5 – 6%. Companies like Dell,
Amazon and others in the leading edge markets
set world class. Note that the difference
between 5% and 14% is 9% of sales, which is
an enormous figure, which immediately impacts
a company’s bottom line. This is fundamentally
the reason why improving supply chain
performance is a high priority task in many
companies and that it has accelerated rapidly
up the agenda from a start in the nineties to
become the hot topic in the first decade of the
twentieth century.
In improving supply chain costs it should first be
remembered:“World class manufacturing is a necessary
but not sufficient condition for world class
supply chain performance”
To deliver world-class manufacturing that
delivers world leading supply chain
performance will, in many cases, require
radically different processes.
Smart control for tomorrow's processes
Tomorrow’s opportunities
The process modelling journey
•
•
•
•
•
•
•
•
Fire fighting
Poor performance, SHE, OEE, etc.
Manufacturing is a cost to the business
Internal conflict
60
reconciliation
Multi-variable
sequ ential
50
Radical improvement
Excellent manufacturing velocity etc
Supply chain is value adding
Outstanding customer service with zero stock
solution
Sequential
40
30
20
Model based
40
10
models
Electro nic
modelling
20
techniques
Step changes in
process technology
Agile
controller
60's
70's
80's
90's
00's
control
controllers
Single loop
Single loop
50's
70's
80's
90's
00's
© ABB - 48
60's
Smart control for tomorrow's processes
- Customize Agile
- Supply Chain
The supply chain journey
70
Customize Manufacture
70
60
pressures
SMART CONTROL FOR TOMORROWS
PROCESSES
All four of the trends identified are now
approaching a common point where the market
will demand smart control for the processes of
tomorrow.
manufacturing
proces s
ERP
computers
20
systems
World cla ss
10
0
50's
60's
70's
80's
90's
manufacturing in cars
0
00's
50's
60's
70's
80's
90's
00's
© ABB - 50
© ABB - 49
© ABB - 51
Be nchmarking
30
DDC
20
Pneumatic
For example world-class supply chain
performance demands that you make to order
with minimum raw material and finish goods
stock. Unfortunately many process plants were
built twenty or thirty years ago when the
philosophy was to run them steadily at a fixed
rate, or to manufacture a batch with a large
minimum batch size. The consequence of this
is that you absorb swings in the market by using
inventory. This is totally opposite to
requirements of world-class supply chain.
Incidentally, one of the reasons they were built
this way, was a lack of full understanding and
process modelling to cover the wide range of
operations. To move to world class supply
chain would demand processes that are
capable of manufacturing “to a unit of one” as
described by Manufacturing Foresight
(Reference 6). This basically says that if the
customer orders one drum of chemicals, you
make one drum of chemicals. If there are no
orders you do not manufacture. This will
require agile process manufacturing, plants that
can be run at any rate in an automatic manner
are providing perfect product. This is a natural
introduction to the title of this paper.
40
DCS
Systems
10
World Class……….’The Journey’……….Tomorrow
on the
process industries
web
Field bus
30
Measure and Act
Supply chain
50
World-widestandards
40
Improve the Core Practices
60
IndustrialIT
OCP
50
Strategy and People Capability
© ABB Eutech 2001
control
0
The data communication and IndustrialIT control journey
Gaining
Gaining
Control
Control
Pneumatic
10
50's
process
control
Systems
Multivariable
30
sequ ential
Grap hical
0
Continuous improvement
Excellent OEE etc.
Manufacturing is value adding
Focus upon customers
predictive
DCS
Steady state
Parametric
control
Stat istical
50
g-pr oms
solution
Dynamic
Distributed
World Class
Traditional
Dynamic
Dynamic
© ABB - 47
•
•
•
•
70
60
Virtuous
Reactive
The process control journey
70
Proactive
The prime driver is the market as represented
by the supply chain. The final customers will
require just in time delivery in full of products at
all time with zero defects. This has been
recognised in the UK Foresight exercise that
recently produced a report called Manufacturing
2020:We can make it! (Reference 6). The
report highlighted five significant themes for the
future, which are summarised in the figure
below.
Manufacturing 2020
Foresight
Making the future work for you
T Manufacturing to a unit of one
T Distributed manufacture, design for Agility, link customer <->
manufacturer
T Value chain co-management
T Full Service propositions, shared technology development
T An environmentally and socially sustainable future
T Zero safety and environmental arising. Plants with limited volumes
T Technology and innovation
T b2b comes of age, information and knowledge key
T An educated constantly re-skilled flexible workforce
T Partnerships with Universities, learning for life, electronic learning
36 ©Copyright 2000 ABB Ltd
Manufacture to a unit of one is the key flexibility
driver. One interpretation is that the chemical
plant has to be designed to be able to produce
a batch size equal to the smallest order size,
even to the case where the order size may be
one drum of chemicals. This demands
manufacturing processes with the following
characteristics.
Design for minimum inherent
volume and capacity.
Build quality into the whole process
from beginning to end such that Six
Sigma performance is achievable.
To meet these requirements will demand a
wider spectrum of manufacturing processes
from the ever-larger continuous plants at the
source of feedstock through to the distributed,
intensified and small plants that work on a
made to order basis at the point of use.
Throughout the spectrum the requirements will
be for “smart operations” where the control,
and the knowledge, must be used to match the
process output and quality to an ever more
variable demand. Concepts like intensification
at the University of Newcastle, the flexible batch
plant at the University of Imperial College and
pipe less plant built by ICI and Mitsubishi
provide further options for tomorrow’s
processes. Hence, it is possible now to design
a process for chemical manufacture that is
agile. Note that the closer you are to the final
consumer the greater the need for agility. The
converse is that the farther away you are from
the consumer in companies such as oil supply
and raw materials processing the less the need
for agility and the more the processes such as
cat crackers and olefin plants will remain
unchanged. There is however and interesting
development occurring in the concept of gas to
liquids technologies which would allow the
hydrocarbon liquids to be made from gas,
potentially in the long term at the point of use
which could significantly change the nature of
the supply chain.
The rate of adoption is presently inhibited by a
belief that the process industries in the future
will look like the process industries of the past.
This is difficult to accept given the pressures
that are coming from the supply chain. Driving
down supply chain costs as a % of sales from
15% to 5 to 7% is just too large an incentive.
The pressure for tomorrow’s processes to be
agile will require that they have to be designed
to exploit the full potential of process control.
That includes control of: The actual chemical processes
The energy sources such as
electricity
Safety, health and environment
Management of the
maintenance
Operation of the supply chain
All the control will be in the context of multivariable statistical process control, as the
products will go straight to the customers
without any laboratory testing or final product
storage.
These “smart operations” will have the ability to
make to order. This leads to the concept that
the orders will arrive directly at the plant. The
plant will purchase the raw materials as
required, produce the products and ship it to the
final customers. This basically says that the
plant is managing the supply chain. Hence, no
longer will you require this vast centralised
supply chain software being developed by many
well known names but the actual supply chain
management will become a distributed supply
chain management managed by the plant. This
is a trend recognised by ABB and described as
Real-Time Enterprise Integration.
Shaping the Industry Trends
1995
Relationship
Management
Multiple, Proprietary
Software Platforms
2000
200x
IndustrialIT from ABB
Collaborative
eBusiness
Enterprise
Management
Traditional ERP
Systems
Plant
Management
Manufacturing
Execution
Systems
Automation
and Control
Traditional Control
& Instrumentation
Devices
Wired Connections
Integrated
Business
Processes
Real-Time,
Plant
Automation
Enterprise
Device to
Integration
Intelligent
Devices
© ABB Eutech 2001
Smart sensors
Totally accurate mathematical
models
Application of multi-variable
statistical process control to monitor
the operational process.
Direct link between the real time
plant and the business systems.
Given what has been said earlier, the ability to
provide a totally accurate dynamic model will be
possible with developments such as g-proms.
-
HW / SW Islands
Gateway Integration
Full Integration!
This has major consequences for the costs of
the supply chain in that it is no longer necessary
to have large IT departments, HR departments,
Accounts departments etc at some central
location and incurring overheads, the whole
supply chain will become green.
ACKNOWLEDGEMENTS
The supply chain is a dynamic system where
the capacities are warehouses, distribution,
order processing times etc. It is possible to
develop a dynamic model of the supply chain.
In other words, control over the supply chain is
a process control problem. Hence, one would
anticipate that tomorrow’s smart control would
be applying all the techniques of single loop;
multi-variable model based predictive and multivariable statistical process control to the
concept of the supply chain.
One consequence will be to raise the
benchmarks of world-class process plants
closer to those of the leading software
companies of today. Suggested targets are
given below. (Reference 7)
Tel: +44 (0) 1642 372379
Fax: +44 (0) 1642 372111
Benchmarks for a flexible Chemical manufacturing
Poor
m hrs / Reportable injuries
0.01
Good
0.1
W orld class
Today
OTIF %
Custom er C om plaints ( % of orders )
Production rate
Quality rate
Availability %
OEE
Process capability ( CpK )
Stock turn
Manufacturing velocity
Supply chain costs as % of sales
World class
Tomorrow
>1
>10
Environm ental incidents
Professor Roger Benson FREng is Technology
Director of ABB UK and Program Manager for
Refineries and Petrochemicals. He has been the
DTI nominated Judge of the UK Best Factory
Award for the last 6 years. He is Visiting
Professor at Imperial College, London, and the
Universities of Newcastle and Teesside. He
wishes to acknowledge the contribution of all
his colleagues in ABB, in particular Sharon
Golightly who prepared the manuscript.
40%
6%
60%
45%
70%
20%
99.9%
0.01%
99%
99%
96%
94%
>99%
<0.1%
>90%
>99%
>95%
>85%
0
>99.9 %
<0.0004%
>99%
100%
100%
>99%
0.6
4
0.1%
22%
1.5
19
1%
12%
>2
>25
>8%
<9%
>6
>50
>25%
<6%
10%
0.8%
<1%
<1%
Added value per em ployee
£10k
£200k
£400k
> £2000k
© ABB Eutech 2001
Absenteeism
This presents a very interesting research
challenge for the process control community.
ABB are already researching all these areas as
a leading first tier supplier.
Internet: roger.benson@gb.abb.com,
References
1
INTEGRATED DESIGN AND
OPTIMISATION OF PROCESSES,
Prof R W H Sargent, The Chemical
Engineer, 46,12, CE424 (1968)
2
“SPEED-UP” IN CHEMICAL
ENGINEERING DESIGN, Prof R W H
Sargent, A W Westerberg, Trans.
Instn.Chem. Engrs, 42,T190
3
Process Engineering Library, www.abb.com
4
PSE, www.pse.com
5
BENCHMARKING IN THE PROCESS
INDUSTRIES
Prof. M Ahmed, Prof. R .S. Benson.
Published by the I Chem E in June 1999,
ISBN 0 85295 411 5
6
MANUFACTURING 2020: WE CAN MAKE
IT; http://www.foresight.gov.uk/
7
FLEXIBLE MANUFACTURING IN THE
CHEMICAL INDUSTRY, Invited paper to be
presented at the 6th World Congress for
Chemical Engineers, Melbourne, Australia,
23 - 27 September 2001
CONCLUSIONS
Tomorrow’s processes will be agile. The
technology exists to develop, control and
manage these processes. It will be a form of
smart control that not only applies to the
processes but also applies to the
electrical/energy supplies and to the whole
supply chain. This is an interesting challenge,
which ABB is vigorously taking on at this time
based on its unique IndustrialIT platform.