COMPUTING AND CHEMICAL ENGINEERING

R.W.H. Sargent

Imperial College, London

ABSTRACT: The paper reviews the developments of computer—based techniques in

chemical engineering from the early days of computing to the present day. It describes
current developments and discusses the consequences for the teaching of chemical
engineering and the future development of the discipline

INTRODUCTION

Computing now permeates all science and engineering, and it would be impossible to
review the whole field of applications of computing in chemical engineering. Instead, I will
try to isolate problem areas in chemical engineering where more extended use of computing
techniques is having an influence, and to consider the impact of developments in computing
on chemical engineering.

Chemical engineering as a profession serves the process industries, and the right
place to start is a look at what has been happening to these industries, and what problems
and challenges they face today.

THE CHANGING INDUSTRY

Major changes are taking place in the process industries. The successive oil crises,
changing world economic conditions, and increased competition from developing countries
have altered the profitability of many traditional products and processes, whilst new
technology and changes in society have created new opportunities and new markets.

There is of course a continuing large—scale demand for many of the traditional

staple products, but with the intense competition in these areas, the producers will have to
make their processes more efficient. Sources of raw materials will be more varied and
market demand more uncertain, creating the need for more flexible plant, capable of
running economically at various levels of throughput on a wider range of feedstocks.
Tighter environmental constraints and tighter requirements for hygiene, health and safety
on the plant mean that processes must be more closely monitored and controlled.

The new expanding markets are in pharmaceuticals, health care and agricultural
products, where new knowledge and understanding are producing drugs with specifically
targeted action, and more specific weedkillers, fungicides and pesticides. There is
considerable growth in food processing and packaging, and in the development of
"convenience foods" with a widening range of additives for improving texture, flavour and
preservation. All these products are complex organic compounds, expensive to produce and
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requiring close control to achieve the necessary selectivity and product purity. They are
usually made in batch or semicontinuous plant, and the subsequent weighing and
packaging operations add further complexity to the production process.

Modern processes involve fewer process steps with closer integration of functions,
smaller inventories of materials, and more extensive heat integration. This means plants
with livelier response and stronger interactions which, coupled with the need for flexible
operation over a wide range of conditions and tighter control to achieve higher
performance, put great demands on the process control system. Indeed, to meet the
exacting performance standards of the speciality chemicals sector, we shall see much more
detailed control of what is going on in the plant, using more sophisticated sensors and more
on—line computation based on more detailed process models. Control issues need to be
considered much more carefully at the design stage, and all these developments are having
as much impact on design as on the process control and management systems.

Developments in microelectronics, computer hardware and networking, information

technology and data base design are providing the necessary tools for the job — more
accurate and more reliable instrumentation, distributed control systems with local
intelligence, readily linked to higher level computer systems with fast and reliable data
transmission, and ever increasing computer power through the use of VLSI and parallel
computation techniques.

These new capabilities provide an opportunity for enlarging the scope of

"plant—wide control" to integrated computer—based systems for company—wide
management and control, extending downwards from corporate strategic planning, through
operations management, to detailed monitoring and control of individual process units.

But perhaps the most significant change in the last year or so, has been a growing
realization by top management in industry that product development is not enough, and
that success, or even survival, is vitally dependent on exploiting to the full these new
sophisticated design, control and management techniques.

THE GROWTH OF COMPUTER—BASED TECHNIQUES IN CHEMICAL

ENGINEERING

Chemical engineers were among the first to exploit digital computers for their
calculations, particularly in the area of process design, and programmes for specific
calculations and the design of individual units rapidly proliferated in the fifties and sixties.

The first steps were taken towards an integrated approach to design with the
development of flowsheeting packages in the early sixties. Originally, these were merely
concerned with steady state simulation, but it was not long before their scope was extended
to deal with steady state design. Soon chemical engineers were tackling the problem of
synthesizing processes and flowsheets, and of efficient energy integration, .and these efforts
dominated research in the seventies and early eighties. At the same time, engineers in
industry were concerned with linking process design to downstream design activities -
equipment design, plant layout, pipework design, instrumentation etc. — and started to
grapple with the problem of managing the vast amounts of data associated with
engineering construction projects.

In the late seventies, the emphasis shifted towards a broader view of design
objectives, a recognition that plants must be designed to operate satisfactorily over a range
of conditions. This is not only a question of covering a range of feasible steady states
(flexibility), but also a matter of good control characteristics over this range, and smooth
and rapid transition from one regime to another (operability). Thus chemical engineers
became concerned with the dynamic behaviour of processes, the development of dynamic
 3

simulation techniques, and the integrated design of processes together with their control
systems.

With the recession in the process industries in the early eighties, and the resulting
fall in demand for new plants, the emphasis shifted even more to efficient operation and use
of existing plant, and their extension and adaptation by retrofitting. As techniques and
hardware have improved, so management have become more confident in the use of more
sophisticated control, in on—line optimization, and in the upwards extension of
computer—based techniques into the higher levels of operations management.

Indeed, the use of computer—based methods has been the key to systematic
development of all these techniques, and "process systems engineering" has developed hand
in hand with computer technology, taking advantage of developments in both hardware
and software. Chemical engineers have been equally involved in the development of the
underlying numerical methods, and to a large extent have led the way in extending these to
deal with the large scale nonlinear systems which dominate the process sector.

In the same way they have taken up the ideas of artificial intelligence with
enthusiasm, seeing this as a means of systematizing the use of qualitative information and
qualitative reasoning, and applying the techniques in such widely different areas as process
synthesis and design, hazard analysis, fault detection and control, and the generation of
operator aids and operator training.

CURRENT TRENDS AND FUTURE DEVELOPMENTS

In both design and control, robustness has become the key issue, and this seems to
have given rise to renewed interest in the synthesis of heat exchanger networks with a
reversion to algorithmically based techniques to produce an optimal robust (flexible,
resilient) design (1, 2, 3, 4). Gunderson and Naess (4) give a good review of recent work
and outstanding problems, and the other papers cited describe contrasting approaches, each
containing interesting ideas but without so far producing a complete solution. On the
other hand, Paules and Floudas (5) have applied the same approach successfully to the
synthesis of flexible distillation systems, though admittedly they have used the rather
artificial formulation of successive sharp splits. Kaklu and Flower (6) describe a technique
for generating superstructures allowing for more complex configurations, then solve a MILP
to obtain the optimal solution, but do not allow for flexibility in this solution, and there is
clearly scope for combining these two algorithms.

The success of these approaches results from the exploitation of a multiperiod

structure in the underlying mixed integer linear programming (MILP) problem, or clever
use of decompositions, and these same authors have been developing general algorithms for
solving mixed integer nonlinear programming (MINLP) problems (7, 8) and applying them
in a general package for flexibility analysis (9). The results on fifteen examples, presented
by Kocis and Grossmann (8), do provide convincing evidence for their contention that
MINLP problems can now be solved "quite efficiently".

The growing interest in batch processes has revived interest in some old problems,
such as the dynamic simulation of batch stills (10) and optimum operating policies for
them (11), but there is probably more scope for profit in better scheduling and on—line
management techniques. Kondili et al (12) suggest a new representation for multipurpose
batch processing systems and an optimal scheduling algorithm which removes many of the
restrictive assumptions associated with earlier algorithms, while Cott and Macchietto (13)
describe a general package for on—line management and control. The same techniques are
applicable to higher levels of operations management in both batch and continuous plant,
and we can expect a large growth of activity in this area.
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Turning to developments in control, a recent review by Morari (14) gives a good

description of the Hoo approach to robust control system design and the considerable work
based on the internal model control (IMC) structure, carried out by Moran and his
coworkers. The central idea of the H approach is to pose the control synthesis problem
oo
as an optimization problem in the frequency domain, taking explicit account of model
uncertainty. Thus a family of possible models is considered, defined as models whose
Nyquist plots lie within a certain fixed band about a given nominal model, and one seeks
the linear feed—back controller which minimizes a measure of the control system
performance, expressed as a weighted Hoo norm of the controller sensitivity over the
frequency domain, for the most pessimistic model. For a multivariable system, there is the
additional problem of an appropriate scalar measure for the sensitivity matrix, and Morari
outlines the use of the "structured singular value" for this purpose. Examples of
application of this approach to distillation column control are described by Arkun and
Morgan (15) and Skogestad and Morari (16). The difficulty is in relating the weighting
functions in the H norm and in the structured singular value to engineering specifications
oo
on the control, and so far no—one has produced any sensible guidelines on how to do this; in
the examples cited, the weightings are chosen arbitrarily.

The second disadvantage of this approach is that it is based on a linear model, but
the use of "linearizing feedback transformations" offers the promise of extension to at least
some nonlinear systems. The idea here is to find a nonlinear transformation of the variables
which produces a linear system in the transformed variables, and as might be expected
there are stringent conditions for such transformations to exist. Nevertheless, there are
some process systems which satisfy the conditions, and a succinct review of the theory and
possibilities is given by Kantor (17).

A direct attack on the nonlinear control design problem is described by Mayne (18),
who formulates the problem as a semi infinite mathematical programme. This allows
flexibility in specification of design objectives and the simultaneous satisfaction of
constraints on stability and performance in either the time domain (such as the shape of a
step or pulse response curve) or the frequency domain (such as the shape of the structured
singular value curve as a function of frequency), thus providing a very general design tool.

Meanwhile, the optimal control approach to multivariable control has again become
popular, following its dramatic relaunch under the banner of "Dynamic Matrix Control
(DMC)" by Shell at the last Chemical Process Control Conference, CPC III (19, 20). The
main advantage claimed by Shell for DMC is its ability to handle operating constraints
directly, but as on—line computer power increases and optimal control algorithms improve,
the approach becomes increasingly attractive, since it also provides on—line optimization,
using an economic objective function. If the techniques for dealing with semi—infinite
constraints can be sufficiently improved, we have the intriguing possibility of an on—line
version of the Mayne—Polak programme described above, incorporating dynamic control
performance constraints in an on—line optimal control algorithm

DEVELOPMENTS IN NUMERICAL METHODS

As in other application areas, in the early days chemical engineers concerned

themselves little with numerical analysis, often devizing their own ad hoc methods based
on their knowledge of physical behaviour. However, as the problems tackled grew larger
and more complex, it gradually became clear that the efficiency and reliability of the
numerical methods were a real barrier to progress. Thus chemical engineers had to concern
themselves with numerical analysis, and as already stated, have in many cases led the way
in developing more powerful methods for the types of calculations which arise in chemical
engineering.
 5

Thus steady—state simulation gives rise to very large sets of nonlinear algebraic
equations. Here the work of Pantelides (21) has established the clear supremacy of
Newton's method, and his techniques for the efficient automatic generation of code for
derivatives from the code for the functions, combined with quasi—Newton methods when
the latter is not accessible, has at last given us a really robust and efficient package for
solution of these problems. And Wayburn and Seader (22, 23) have continued their work
on continuation methods, which provide a systematic means of finding all solutions to a
problem when multiple solutions arise.

Dynamic simulation involves integration of sets of differential equations, and for

years chemical engineers have struggled to cope with the large "stiff" systems typical of
chemical processes. Then, just when it had become generally accepted that Gear's BDF
method was the best all—round method for dealing with stiff systems, we have new results
from Cameron and Gani (24) on diagonally—implicit Runge—Kutta methods, with
automatic switching to explicit Runga—Kutta methods for non—stiff segments of the
trajectory. The special problems associated with higher "index" in differential—algebraic
systems have emerged, and we begin to have methods for dealing with such systems (25,
26). There is increasing interest in dynamic simulation of distributed—parameter systems,
where the use of adaptive grids (27) or moving finite elements (28) provides the robustness
and flexibility needed to deal efficiently with moving wave—fronts and other difficult
features. These techniques also seem to be proving effective for solving optimal control
problems (29, 30). Optimal design or operation leads to large nonlinear programming
problems, and in this area, Lang and Beigler (31) describe a unified algorithm combining
feasible and non—feasible path techniques, in a paper which also gives interesting results on
the relative merits of these two approaches. Design and synthesis problems also usually
involve integer variables, and we have already remarked on the progress made on general
algorithms for solving such mixed integer/nonlinear programming problems (7, 8).

There is still much to do on specific techniques, but as our problems increase in size
and complexity we face more stringent requirements on the software implementation of
these techniques. In the last two or three years we have rapidly graduated from "large"
problems involving a few hundred variables to problems involving several tens of
thousands, and industry already has in view problems implying hundreds of thousands.
Certainly this has put the spotlight on the issues of error propagation and the exploitation
of "structure" in terms of the pattern of occurrences of variables in the equations, but it
has also emphasized the issues of input description and error checking, and the selective
presentation of results and error diagnostics in a form which is helpful to the user. The
greater the flexibility provided for the user in formulating his problems, the greater the
scope for ill—posed problems, and the greater the need for the system to provide help and
guidance. At the same time, the greater the degree of integration and automation of the
techniques, the greater is the need for automatic diagnostic and error—correction
procedures. As we have seen above, the wide variety of types of behaviour encountered in
large complex systems is indeed giving rise to self—learning, adaptive techniques, which
choose options or adjust parameters to suit the problem characteristics as they evolve
during the solution, and modern software packages for design and simulation (like
SPEEDUP, 21) contain sophisticated diagnostic and user—support systems.

However, the major new development in numerical methods at the present time is
the study of ways of exploiting parallel computer architectures. Most of the basic
algorithms used in simulation and design exhibit some parallelism, and reformulation to
exploit this structure is forcing a fundamental re—examination of all our techniques. The
fruits of these efforts will be a vastly improved capability of handling large complex
problems, and hence the possibility of using more detailed and realistic models.

There is also a great deal of parallelism in the operations involved in information

and database management, and we can look forward to greatly improved systems for
 6

dealing with the large structured databases for mixed types of information, which must be
at the core of any kind of comprehensive integrated design system.

Beyond the algorithms and procedures exhibiting true parallelism, there are many
more with broadly parallel structures, but with various kinds of limited interaction
between the branches, and there is currently much vigorous research on the development of
methodologies for efficiently implementing these on parallel computer systems.

This type of structure represents quite closely that of a design team cooperating on
the design of a process, especially when the work is being carried out on a network of
work—stations linked to a common database. We can therefore expect research in this area
to produce benefits in terms of methodologies for better and more effective coordination of
cooperative activities of this kind.

DEVELOPMENTS IN COMPUTING SCIENCE

Undoubtedly the most dramatic impact of Computer Science on chemical

engineering over the last five years has been the popularization of "expert systems".

It is an obviously appealing idea to encapsulate the accumulated knowledge and

experience of experts in a computer programme, so that this expertise can be widely
disseminated, and is available at will to those of lesser capabilities. Thus the publicity
given to the success of "Mycin", an early medical diagnostic expert system, generated high
expectations and feverish activity.

The hype now seems to be dying down, and people have a better measure of the
possibilities and limitations of the technique. Simple rule—based expert systems lend
themselves well to tasks such as equipment selection, or operator instructions. It would be
unfair to categorize such systems as merely computerized catalogues or operator manuals,
for the possibility of incorporating guidelines and logic make these systems much more
useful. There are now many expert systems of this type in regular use in industry.

However, it is now realized that construction of "knowledge—bases" and "query

systems" is not easy, that the users' interpretation of the "answers" is often not what was
intended, and that, above all, it is not easy to capture an expert's knowledge, since for
every rule he can enunciate he usually subconsciously uses extra information and a host of
provisos and exceptions. Garrison et al (32) give a thoughtful review of the current state of
the art, giving an optimistic view of its promise, but warning that current IKBS technology
is still in a rudimentary state, and emphasising that "the role of expert systems ... should
be a means of disseminating technology, and not a means of facilitating the implementation
of ad hoc solutions of limited applicability". Perhaps other industrialists would disagree
with this as a short term objective, but it must surely be the target for the longer term
development of expert system methodology.

Meanwhile, in the words of Stephanopoulos, in the preface to a special issue of

"Computers and Chemical Engineering" devoted to Artificial Intelligence (33)", current
research has outgrown simple—minded rule—based systems". This issue in fact gives a very
good picture of current developments in this general area, containing papers in the areas of
fault diagnosis, control, design, synthesis of operating procedures and systems for
qualitative and order—of—magnitude reasoning. There is also one paper, by Hoskins and
Himmelblau, on the possible use of artificial neural networks in chemical engineering. This
is a technique which is at present attracting wide interest as a new, and apparently very
effective technique for dealing with complex pattern—recognition problems, illustrated in
the above paper by application to fault detection.
 7

There is indeed a general trend away from purel qualitative rule—based systems
towards the use of a combination of empirical knowledge so—called "compiled knowledge")
and quantitative simulation based on mechanistic mode s ("deep knowledge"). For me,
this is a reassuring trend, for I believe that real progress must be based on a clear
formulation of the problems, and on improved understanding of the mechanisms involved
in the systems with which we have to deal; there can be no short—cut by blindly following
half—understood precepts gleaned from "experts" or by using purely qualitative reasoning.
I wholly endorse the words of Stephanopoulos (34), when he says "one should strive to
articulate, represent and utilize all forms of available knowledge".

MODELLING, PROCESS SYSTEMS ENGINEERING AND CHEMICAL

ENGINEERING

This representation, or modelling, of the system we wish to study, design or control

is thus crucial to the success of our efforts.

In spite of increases in computer power and improvements in numerical methods, we

are still a long way from allowing ourselves the luxury of using a single detailed model of
our systems for all purposes. A necessary part of the modelling process is therefore the
identification of the significant features of the behaviour for the purpose in hand, and the
construction of a model which adequately reproduces these features without irrelevant
complexities.

Today, much can be achieved by simulation, comparing results from a given model
with those from various simplifications of it, as illustrated for example by the work of
Pirkle et al. (35) on the comparison of one and two dimensional models of fixed—bed
reactors. At some point, the model behaviour has of course to be compared with
experiment, and Rippin (36) has recently given a good review of the state of the art in
statistical experiment planning and model assessment techniques.

However, before these techniques can be applied, the model has to be generated.
Leaving aside the simple general—purpose linear models used in control (state—space
models, ARMA models), this requires a consideration of the physical processes involved,
and the issues are discussed by Hofmann (37) and in my own contribution to the PSE'82
Symposium in Kyoto (38).

Now in recent years, as the scope of computer—based techniques has expanded, and
the techniques themselves have become more sophisticated, there has emerged the concept
of the "process systems engineer", specialized in these techniques. Many would argue that
such a person is concerned only with the use of models in design, control, operations
planning, etc., and that the generation of such models is the province of the traditional
chemical engineer, aided possibly by physicists and chemists. Some might go a little
further, and admit to an interest in the general methodology of modelling, such as
statistical parameter fitting, the design of experiments for optimum parameter
determination or model discrimination, and model reduction techniques.

However, the methodology and the individual studies are inextricably mixed, and it
is impossible to carry out the one without an understanding of the other. Indeed, I would
go further, and claim that the modelling process is nowadays the essential route by which
we acquire our understanding of the physical and chemical processes we study. We first
analyse the process into component mechanisms which are fully understood, and hence for
which we already have adequate models, and then ensure that their interconnections are
correctly represented in the model. We are then in a position to simulate the behaviour of
the complete system and compare the results with experiment; significant discrepancies
will lead us back to a reexamination of the elementary mechanisms, or of the modelling of
their interactions.
 8

Of course, production of the model is not an end in itself; it is merely a means to

enable us to design, control or manage the system modelled. All this is the province of
process systems engineering, and it would be difficult to identify areas of chemical
engineering not included in this activity.
Thus process systems engineering is not just a specialized area for mathematically
minded chemical engineer's — it is the very core of the discipline of chemical engineering
itself.
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 9

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