2. 448 A.W. Westerberg / Computers and Chemical Engineering 28 (2004) 447–458
This model, as powerful as it is, is of course not a complete
model. We can only formulate design problems as mathe-
matical programming problems only when we can a priori
enumerate the design space of alternatives and only when
we can connect cause with effect through a model.
2.2. Innovation
There is another aspect to systems design that involves
innovation. What sparked the remarkable new design for
manufacturing methyl acetate that Eastman Chemicals built
in the mid-1980s (Agreda & Partin, 1983; Agreda, Partin,
& Heise, 1990; Siirola, 1995, 1996). We must have another
part of the design model, which supports creativity wherein
very different, non-routine solutions appear. We need here,
at the very least, the means to discover ways to intercon-
nect existing technology to find arrangements that are not
currently in our thinking. We have seen great strides in aid-
ing innovation that have come from a better understanding
of how to heat integrate processes, how to design separa-
tion systems and how to look at the design of reactor sys-
tems. The algorithms resulting are extremely useful when
available as canned tools. But, even more important are the
representations on which they are based. These represen-
tations can allow designers who understand them to “see”
the properties of the better designs. The designers benefit
because they can directly construct such designs and/or be-
cause they can sharply reduce the design space they must
then search. An anecdote may serve to make the point as
to how much things have changed. In the late-1970s, this
author visited a design team in industry and informed them
of the ideas of Hohmann (1971) and Linnhoff and Flower
(1978a,b), the latter just then appearing in the literature, on
how to synthesize heat exchanger networks systematically.
The next day, this team told my host that he had invited a
“madman” to visit with them (albeit a quickly vindicated
madman).
2.3. Not knowing the design space
But there is still more to systems design. We simply
do not know enough about some design problems to al-
low us the luxury of knowing its space of allowed alterna-
tives. We are not even sure of its goals, nor do we have
any idea how changing the goals might affect the design
solution found. For example, design a software system to
support collaboration or design the design department in a
multinational company. Or it is about 1942, and we have to
think how to recover enriched uranium 235. Or it is 1970,
and we want a plane that radar cannot see. We are asking
how to attack design problems that no one involved in the
project, and perhaps no one in the world, has previously
faced.
In computer science, Simon (1981) classified these design
problems as being ill-posed if we know the design space
but not our goals and wicked if we do not know anything
about the design space. We first have to convert them into
problems we have a chance to solve. We have to establish a
set of goals. We then have to discover even one, but hopefully
many, design alternative that would be the start for the space
of alternatives we wish to consider. We need to discover
how to postulate a design and test if it is meeting our goals.
Finally, we will be derelict if we do not discover what exists
that is relevant. Biegler, Grossmann, and Westerberg (1997,
pp. 10–18) discuss doing these tasks and relate it to doing a
process design.
2.4. Understanding the goals
Another aspect to design is to appreciate the true goals for
design. All design problems have multiple goals: be prof-
itable, be safe, be reliable, and so forth. We always have
to trade off our goals against each other. In addition, if we
fail to formulate our objectives properly, we will almost cer-
tainly get the wrong solutions. We may state a goal as a
constraint—e.g., we must make at least a 15% return on in-
vestment. We could easily miss a nearby design for which
the return is 20%, a design we would in principle have found
had we stated our goal to be to maximize return.
As another example consider what it takes for a new de-
sign to challenge an existing process. A new design has to
be better economically than the existing process by paying
back both investment and operating costs while the existing
process only has to cover its operating costs. Not understand-
ing this about the economics of a process can cause one to
waste time looking at new designs that are only a little better
than existing ones in a saturated market. As another example
of understanding our goals correctly, we typically choose
batch processes because they are much more flexible. As
such we can design them more quickly and compensate for
design errors by modifying how we operate them. A batch
process to produce a single chemical throughout the year is
virtually never more economical than a dedicated continuous
plant.
2.5. Failure of the design process
Finally, the design process itself can often fail. A company
can have all the right technology and miss producing the
better designs because of poor communication among design
team members.
Acting as an historian, Bucciarelli (1994) observed and
reported in detail on three product design processes, noting
just how complicated they were. He argues that design is a
social process. One can see two interpretations for this ob-
servation. First, the actual work process is social. It involves
being in meetings where one may spend inordinate amounts
of time negotiating terminology, the project goals, how to
make decisions, and so forth, with others whose backgrounds
are very different. A second way design is a social process
is that what we design reflects our personal and collective
values and knowledge bases.
3. A.W. Westerberg / Computers and Chemical Engineering 28 (2004) 447–458 449
So how should we design better design processes? As with
any artifact,2 we cannot improve the process of designing
if we have no data on how it functions and on how well it
functions. Based on experience, many companies establish
policies and procedures for many of their work processes.
Many companies have used computer-based support systems
such as Lotus Notes. Typical use of these systems is to define
a workflow that the company expects the designers to follow.
The workflow prescribes, among other things, the types and
sequence of documents the design activity should produce
and the order in which people should review and sign-off
on each.
Now comes the dilemma. Bucciarelli and others before
him make it clear that problem solving, of which design
is an instance, is anything but an organized definable se-
quence of activities. Even individual problem solvers move
back and forth rapidly and almost randomly from one part
of the problem to another. It is, therefore, not difficult to
arrive at the conclusion that design support systems can-
not be fixed in structure. They must support as the norm
the continual deviation from that which is being prescribed.
Designers will always be inventing a new document type.
They will also need to capture new kinds of information and
tie it with unexpected relationships to existing information.
If for example one creates a database with fixed schemata
and disallows changes, designers will be forced to operate
creatively around this restriction or may in fact not use the
database.
We were asked to think about designing the design process
with our colleagues at a Process Systems Engineering Con-
ference in Trondheim (Westerberg, Subrahmanian, & Reich,
1997). In this paper, we discuss converting this design prob-
lem from being wicked to being well-posed. We discuss the
characteristics we see as necessary for design support en-
vironments. We are concerned about international organiza-
tions with distributed design organization trying to function
effectively together. We postulate some goals. We point out
that there is much room for improvement as we cannot at
present capture and reuse very much of the intellectual cap-
ital created during the execution of our work processes.
2.6. In summary
As we discuss here, not all the systems aspects of a de-
sign problem have to do with mathematical programming
or abstract representations. We need to understand system
concepts whenever we strive to integrate together parts that
we understand individually. We include here determining
how we integrate a process within a company and how it
affects the company’s goals. We also include how we put
together work processes and people to carry out the design
activity. If it seems a stretch to include them, then we justify
2 An artifact is anything created by a human that does not exist in
nature, including abstract things such as design processes.
our including them because our real goal is to understand
how to design better processes.
We shall now examine in more detail the more technical
issues raised here.
3. Design as a mathematical programming problem
If we know how to enumerate all the alternatives that we
are willing to consider for a design and if we can evaluate
these alternatives using appropriate quantitative measures,
then we can in principle set up our design problem as a
mathematical programming problem. In the extreme we can
simply enumerate all the alternatives, evaluate them, and
select the best design.
There are several issues to worry about with this approach.
First, we really need to understand a problem well—it must
be routine—if we are able to enumerate all its alternatives.
Our ability to enumerate requires we have a way to represent
the space of alternatives. Such a representation may be the
one real contribution of a Ph.D. thesis—i.e., they are not
that easy to discover.
Second, to paraphrase Knuth (2001), 109 equals infinity.
The space of design alternatives is often so large it makes
109 look small. The only way we can search such a large
space is if we can take significant advantage of all the special
structure and properties it may have, such as being linear
and/or being composed of a number of parts that are sparsely
interconnected. We succeed if we can find ways to eliminate
large numbers of alternatives with one analysis, a situation
that can arise if we can discover ways to bound the objective
function values for families of alternatives.
3.1. Analysis
Advances in many areas in the past four decades have
allowed us to make considerable progress in setting up
and solving design problems as mathematical programming
problems. There continues to be progress in our ability
to simulate. In the past four decades, we have debated
whether we should base these systems on a modular or
equation-based architecture. Considerable evidence now
makes it clear the best systems have the advantages of both
approaches. We have evolved from solving 19 equations in
19 unknowns and using about as many hours of computer
time in the mid-1950s, to models involving 650 equations
in the mid 1970s (Lin & Mah, 1978; Mah & Lin, 1978), to
the solving of 100,000 equations in a minute of 500 MHz
computer time today. Both hardware and software have
improved at enormous rates over the decades. Our under-
standing of how to create and manage complexity in mod-
eling improved with the advent of object oriented concepts.
Object orientation also leads one to the possibility of auto-
matic modeling (Sargent, Vasquez-Roman, & Perkins, 1994;
Stephanopoulos, Henning, & Leone, 1990a,b), where one
might select the transport processes, state the assumptions
4. 450 A.W. Westerberg / Computers and Chemical Engineering 28 (2004) 447–458
that underlie the model and then have the system write the
equations to model it. User interfaces are much more user
friendly, allowing engineers access to very sophisticated
software. We continue to hear about CORBA,3 OLE,4 Ac-
tiveX, and, in Chemical Engineering, pdXi (Balwin, 1996),
Open Cape and Global Cape Open.5 The national labs
are cooperating on the Common Component Architecture
Forum6 project to develop software components for which
one can use a graphical interface to wire them together to
form a complex new software system.
New approaches to solving very large systems of equa-
tions robustly require the interface to pass a large amount of
information about the model (see Drud, 1997 for a listing
of such information for CONOPT). Our own work (Allan,
1997) indicates that we need even more information than
Drud lists to speed up compiling, for example. This new in-
formation is not in any of the standards for defining solver
objects.
Finally, a very difficult issue is the reuse issue for soft-
ware. How can one improve the likelihood that others
can locate and reuse the objects one creates? Reuse can
range from reusing the object as is to allowing a reason-
able end user to modify the code defining the object for
a new situation. Are these chemical engineering issues?
Should we be worrying about them? These will affect
chemical engineering modeling in major ways, and we are
in a position to make progress on them. We cannot ignore
them.
An important ingredient of commercial flowsheeting sys-
tems is their ability to carry out the laborious task of com-
puting repeatedly the physical properties for vapor, liquid
and solid mixtures of species. Two issues are immediately
important. The first is to have reliable, accurate methods for
estimating. The second is to structure these calculations to
support flowsheeting systems.
Models really have two major components in chemical
engineering design: the part that characterizes the equip-
ment and the part that characterizes the material flowing in
that equipment. Physical property estimation is crucial for
successful analysis of processes. We do pretty well on esti-
mating the properties for hydrocarbon mixtures. When we
push much beyond such mixtures, accuracy of the estimates
is still a hard question.
3.2. Optimization
Two papers in this issue discuss the progress here to
date (Grossmann, 2002) and the likely future progress
(Biegler, 2002) in mathematical programming. We shall
only highlight a few key thoughts that pertain to design and
synthesis.
3 http://www.omg.org/.
4 http://www.microsoft.com/data/oledb/default.htm.
5 http://www.global-cape-open.org/.
6 http://www.cca-forum.org/.
We not only want to analyze our processes, we also wish
to choose equipment sizes and configurations to improve
their performance. We have had considerable progress in
mathematical programming over the past four decades also.
Driven by the oil industry to schedule refinery operation, we
first saw the development of large-scale linear programming
packages in the late-1950s (where large is a relative term).
First attempts at optimizing the design and operation of
flowsheets were slow, requiring the equivalent of thousands
of flowsheet simulations. Today, sequential quadratic pro-
gramming approaches allows us to optimize the continuous
decisions, often in the time equivalent to 2–3 flowsheet sim-
ulations. Progress in handling discrete decisions through the
use of binary and integer variables has also progressed sub-
stantially. At first 10 binary variables was a large problem.
Today, taking advantage (often automatically) of special
structure, we can often accommodate thousands of binary
variables while using rigorous solving methods. When no
rigorous approach seems to be available, many articles ap-
ply stochastic optimization approaches, such as simulated
annealing and genetic algorithms to find solutions.
Envisioning that in about a decade we will be able to
compute in a minute what now takes a week on massive
parallel arrays of desktop computers, we should perhaps be
posing a new question: how can we effectively use unlimited
computing power to solve very large complex numerically
stubborn design problems? One approach gaining consid-
erable interest recently is to use cooperating systems of
autonomous software agents operating in parallel (Siirola,
Hauan, & Westerberg, 2002; Talukdar & de Souza, 1994;
Tyner & Westerberg, 2001a,b).
3.3. Heuristic approaches
When the problem is too large and we must solve it, we
must resort to the use of ad hoc methods. Often we based
these methods on observing the behavior when solving many
problems of a given type. We might observe that the oper-
ating costs for a unit are negligible for a class of problems.
Thus, we might solve future problems without considering
operating costs. In distillation of nearly ideal mixtures, we
may note that the better separation sequences always involve
the early removal of the most plentiful species. We may,
therefore, use rules to analyze a problem and to fix many of
the discrete variables a priori to reduce the size of the search
space. The ideal distillation literature started with the pos-
ing and testing of heuristics. Interestingly many of them do
work well.
Masso and Rudd (1969) were among the first to propose
finding heuristics through automatic learning when synthe-
sizing heat exchanger networks. While it proved elusive for
that particular problem, it was a very interesting idea. The
learning literature is extensive in computer science. We have
found the ideas behind the SOAR system (Newell, 1990) to
be very interesting. All learning parameterizes a model from
existing data. In our learning, we may have to pose and test
5. A.W. Westerberg / Computers and Chemical Engineering 28 (2004) 447–458 451
alternative structures for the model itself, which can give
the impression of our discovering things about the prob-
lem. Learning also involves guessing how to “variable-lize”
what we learn; for example, we may receive as input a tem-
perature with a value of 350 K and solve a problem using
it. Is the rule we are trying then to learn valid only for
that particular temperature or is it valid for any tempera-
ture? Finally, we may make mistakes in learning. To over-
come these mistakes requires that we occasionally challenge
rules we think we have learned so we can either remove or
overrule them.
We need heuristics to solve many of the problems industry
poses. Without them we need to formulate problems that are
too difficult to converge and/or too large to search. However,
we cannot guarantee the optimality of the resulting solution
when we use heuristics.
Rather than denigrating heuristics, we should observe that
they are everywhere. They are in our nonlinear solvers to
help decide which approach to try to get a difficult prob-
lem to converge. Heuristics are in our normal everyday de-
cisions when selecting the technology we will use to solve
a problem. They are there when we choose to use a form of
simplified model to characterize a batch process when de-
veloping a scheduling model for it. In other words, they are
there to aid in choosing how we should make decisions as
well as to which decisions we should make.
3.4. Evaluation support tools
Evaluation of design alternatives is itself a very interest-
ing and complex subject. There are numerous measures that
characterize a design: cost, safety, flexibility, controllability,
operability and so forth. Ideally, we would like to reduce
all such indices to a common basis such as cost, but gen-
erally we cannot. Thus, we are always faced with making
trading-offs among the various incompatible indices. In ad-
dition, we often have no good way to evaluate these indices.
How should we assess the safety of a proposed design, espe-
cially during its early stages? We might look for hazardous
chemicals or extreme operating conditions—again, is this
one or two indices?
For multiple objectives, optimization theory can only tell
us how to establish which alternatives are in the so-called
trade-off or Pareto set. Finding a solution that improves
any index for a Pareto point requires that one of the other
indices must become worse. The engineer has to make the
final selection among those designs in the Pareto set based
on personal values. We can find the Pareto set by solving a
series of optimization problems where we “extremize” one
of the objectives while setting a variety of different bounds
for the others. For example, we might minimize cost for
each of 10 problems having a flexibility index no smaller
than 1, 0.9, 0.8, . . . , 0.1, respectively. The unique solutions
we find are in the Pareto set. There are also heuristic-based
algorithms that try to find a large number of the Pareto points
simultaneously (Deb, 2001; Kalyanmoy, 2001).
3.4.1. Cost
The evaluation we all know best is to assess the eco-
nomics of a process. To assess the economic worth, we
need to estimate costs. We have methods to estimate the
cost of the equipment based on correlations developed from
past purchases (for example, Guthrie, 1974). There are com-
puter programs that characterize equipment by the number
of hours needed to fabricate it by different types of skilled
personnel and by the materials used in that fabrication. Input
to these programs are labor rates and materials costs, and
they can be surprisingly accurate. There are indices to relate
the costs in one year to those in another year. We can also
estimate labor rates, land costs, etc., for a process. Estimates
for the cash expenditures for a process design range from
plus or minus 50% down to 5% depending on the effort we
are willing to expend.
3.4.2. Economics
Given cost estimates, how should we compare the eco-
nomic worth of two alternatives? How should we compare
100 alternatives? If these are major projects involving signif-
icant portions of the resources of a company, then we have
to see how projects fit together over time to assess their im-
pact. We cannot reduce the economic worth of the project
to a single number. We need to know the cash flow for it as
it varies over time. We need to choose those projects that fit
together best to maximize their estimated present worth to
the company while not taxing its financial resources. How-
ever, we will have trouble searching over 100 projects this
way. There are numerous ways we can carry out a project
over time and thus how its cash will flow over time is not
readily fixed. Thus, the search becomes astronomical in size.
When faced with a large number of projects, we are reduced
to assessing if projects are unlikely candidates by develop-
ing a single number for them—such as breakeven time—and
eliminating those that fail to pass a hurdle rate. The point of
this brief discussion is that, even for economics—which we
know best how to handle, the merit function is not straight-
forward to create and use.
3.4.3. Other merit functions
Grossmann and his students (Grossmann & Floudas,
1987; Grossmann, Halemane, & Swaney, 1983) have devel-
oped flexibility indices and ways to solve design problems
to maximize them. Their work shows us how we must
formulate these problems. Still, many problems that in-
dustry poses will tax our ability to carry out the resulting
calculations.
To assess the safety of a process has been the subject
of considerable study (see, for example, Caceres & Henley,
1976; Powers & Lapp, 1976). One approach is to posit a
safety problem such as an explosion in the reactor and work
backward through a fault tree to find what combinations of
“basic” events might cause it. If available, one can then prop-
agate the probabilities of these basic events through the tree
to assess the likelihood we may have an explosion. Another
6. 452 A.W. Westerberg / Computers and Chemical Engineering 28 (2004) 447–458
approach looks at failures singly and together to discover
which dangerous events their occurrence could cause.
A whole literature (see, for example, work by Kramer &
Finch, 1989; Suewatanakul & Himmelblau, 1995;
Venkatasubramanian&Dhurjati, 1987; Venkatasubramanian
& Rich, 1988) now exists on the use of expert systems to
determine the common cause when a process fault occurs.
Some systems use only rules, while others examine cause
and effect models that the user supplies to the system.
There is a preliminary study to assess the toxicity of a
process by Grossmann, Drabbant, and Jain (1982). Perkins
and Wong (1985) and others have developed ways to assess
the controllability of a process.
4. Synthesis—representations for innovation
Process synthesis is very much the fun part of engi-
neering. It is where one invents the structure and oper-
ating levels for a new chemical manufacturing process.
One of the first publications on synthesis significantly
predated the use of the word (Lockhart, 1947). It asked
whether one should separate a ternary mixture by first
separating the most volatile or by first separating the
least volatile component. Rudd and co-workers introduced
the term “synthesis” in the late-1960s (Masso & Rudd,
1969). The first review on synthesis referenced 66 papers
and was by Hendry, Rudd, and Seader (1973). By the be-
ginning of the next decade, Westerberg (1980) and Nishida,
Stephanopoulos, and Westerberg (1981) wrote review arti-
cles that referenced over 130 and 190 papers, respectively.
A computer-based search of chemical abstracts for the
terms “chemical engineering process synthesis” finds 466
relatively recent contributions (most of which are those
intended).
The majority of the synthesis literature assumes we are
constructing our processes using well-defined unit opera-
tions and that the problem is one of finding the best config-
uration among an astronomically large number of possible
alternative ways to interconnect them. Major contributions
have given us representations and algorithms to synthesize
homogeneous systems, such as systems built of heat ex-
changers (Hohmann, 1971; Linnhoff & Flower, 1978a,b), of
distillation columns (Hendry & Hughes, 1972; Thompson
& King, 1972) and of reactors (Achenie & Biegler, 1986;
Balakrishna & Biegler, 1992, 1996; Chitra & Govind,
1985a,b; Glasser, Hildebrandt, & Crowe, 1987). The early
work of Siirola, Powers, and Rudd (1971) followed by that
of Mahalec and Motard (1977a,b) showed us the potential
of using artificial intelligence concepts to configure entire
flowsheets. We shall highlight the major contributions for
each of these topics.
Lately, we see a growing discussion about constructing
our processes at a more fundamental level—that is, by think-
ing of our processes as combinations of transport processes.
If this approach successfully develops, then it will lead to
designing not only the processes but also the unit operations
themselves that should form the basis of these processes.
We shall discuss these ideas in more detail when we discuss
the synthesis of reactive distillation processes.
4.1. Different approaches
Biegler et al. (1997, pp. 33–35) enumerate the following
approaches to synthesis: total enumeration of all the alterna-
tives in an explicit space, a coordinated search in the space of
design decisions, evolutionary methods, superstructure opti-
mization, targeting, problem abstraction, and combinations
of these. We have already mentioned some of these above
when we discussed advances in mathematical programming,
especially in the handling of binary variables, which corre-
spond to the discrete decisions that allow us to select which
types of equipment to use and to select how to configure
that equipment.
4.2. Generating alternatives
The first real issue in synthesis is how to represent the
design space of alternatives. The wrong representation can
make the problem impossible to formulate and/or solve. The
right may make formulation straightforward and may allow
one to see how to solve efficiently. (Try enumerating all the
alternatives for the exchanger networks one could propose
to exchange heat between three hot and three cold streams
where each stream can exchange at most one time with any
other stream. You should get hundreds of alternatives. See
Biegler et al. (1997, pp. 32–33) for a matrix representation
that allows this enumeration.) The enumeration can be ex-
plicit, as with those described above or it may be implicit in
the form of an algorithm to generate the alternatives.
Formulating a superstructure within which are all the al-
ternatives one wishes to allow for a class of problems is one
approach to use. Designing the form of the superstructure
is often a major contribution. One does not want the super-
structure to have the same substructure embedded in it in
multiple ways as then one has guaranteed the optimization
problem has multiple local optima. Agrawal (1996) shows
that a very subtle difference in the proposed superstructure
for heat integrated distillation increases the design space in
interesting and important ways over the superstructure orig-
inally proposed by Sargent and Gaminibandara (1976).
The message: It is crucial to get the representation right.
The right representation can enhance insights. It can aid
innovation.
4.3. Search
The second issue in synthesis is how to search among the
enormous number of alternatives, as we have mentioned ear-
lier. Often search is the solving of a mixed (also contains real
variables) integer (some of the variables to be solved for are
integer) nonlinear program (MINLP). Using targeting as in
7. A.W. Westerberg / Computers and Chemical Engineering 28 (2004) 447–458 453
heat exchanger network synthesis or reactor synthesis, one
may impose some very strong constraints that will eliminate
major portions of the design space quickly. Using abstrac-
tion, one may make high level decisions that remove major
portions of a space. An example is to decide to separate the
alkenes from the alkanes first. Abstraction almost always
dramatically reduces the size of the search space, with the
drawback that one may throw out the best solution. To use
it successfully one must have correct intuition about which
decisions are the important ones to make first. As we shall
discuss shortly, this type of decision ordering was a part of
decision making in the AIDES system (Siirola et al., 1971),
and it is central to the Douglas (1988) hierarchical approach
to design.
4.4. Advances in synthesis
4.4.1. Heat exchanger networks synthesis (HENS)
The problem is to invent a heat exchanger network that
allows one to recover and reuse process heat. The base
problem is to invent a heat exchanger network having the
least annualized cost that can exchange heat among n cold
streams and m hot streams plus utilities. For this base prob-
lem know the flowrates and inlet and outlet temperatures
for all process streams. Hohmann (1971) and Linnhoff and
Flower (1978a,b) provided the remarkable observation that
one could compute the minimum utility requirement for this
problem without inventing the network. Subsequent work
showed how to target the likely number of exchanges and
even the capital investment required, again without design-
ing the solution to the problem. These targets dramatically
reduce the space of alternatives over which one needs to
search.
Independently discovered by Umeda, Harada, and Shiroko
(1979) and Linnhoff and coworkers (Linnhoff et al., 1982;
Townsend & Linnhoff, 1983), the grand composite curve is
one of the most insightful representations in all of process
synthesis. This curve directly indicates the hottest tempera-
tures at which one can remove heat from and the coldest at
which one can inject it into a process using utilities. With
this curve, one can see where best to extract work from heat
in combined cycles. One can also visualize where to place
heat pumps for subambient heat recovery.
Duran and Grossmann (1986) provide a mathematical pro-
gramming formulation to allow one to embed the minimum
utility cost heat exchange within a superstructure model for
an entire process. This model allows one to select the other
units and their configuration, subject to there being a heat
exchanger network that will use the minimum utilities. One
has to be careful in solving the resulting problem, as it will
likely have local optima.
Much work followed these early insights—e.g., to ac-
count for materials of construction of the exchangers, to
account for differing heat transfer coefficients, to account
for other than pure counter-current exchangers, to recover
heat in batch processes where heat generation and recov-
ery are functions of time and to allow for flexible process
operation. An important review article on heat exchanger
network synthesis is by Gundersen and Naess (1988).
4.4.2. Separation trains
As we noted earlier, Lockhart (1947) was one of the first
to publish a paper on synthesis. He asked if one should sep-
arate the more or the less volatile species first when sharply
separating a three component mixture into three pure com-
ponent products. During the 1970s, much work appeared
on developing insights into finding the better separation
sequences for separating relatively ideal mixtures of n com-
ponents, generally into n relative pure single component
products. The first contributions were on how to represent
the design space (as a tree of alternative decisions).
Since columns require high temperature heat in their
reboilers and release low temperature heat from their con-
densers, the problem of finding the best heat integrated
sequences was also popular. The heat cascade diagram rep-
resents a column as a box whose width is the heat degraded
in the column and height the temperature difference between
the reboiler and condenser. We can draw it on the same tem-
perature versus heat diagram we use for heat exchanger net-
work synthesis, giving us a tool to visualize the alternatives
(Andrecovich & Westerberg, 1985). We can also represent
the use of side strippers and enrichers and intercoolers and
heaters on this same diagram (Biegler et al., 1997). Blend-
ing this representation with that for the grand composite
curve allowed Linnhoff, Dunford, & Smith (1983) to show
where one should not attempt to integrate a column into a
process.
The separation of azeotropic homogeneous and hetero-
geneous mixtures is another area in which very significant
contributions exist. Since the early-1960s, researchers in the
former Soviet Union have led in making important funda-
mental contributions to this topic. Fortunately, many of their
works are available as English translations. Poellmann and
Blass (1994) and Petlyuk (1998) highlight many of these
contributions. The Russian work emphasized analysis and
synthesis techniques based on looking at the separation
trajectories in composition space. The literature in the west
was spearheaded by Doherty and Perkins (1978, 1979) and
then by Doherty and his students. We too (Wahnschafft,
LeRudulier, & Westerberg, 1993; Wahnschafft&Westerberg,
1993) have worked in this area, specifically on synthesis
methods. We now can teach methods to students to design
distillation-based separation processes for ternary mixtures
involving components that display azeotropic and heteroge-
neous behavior. The key is again the representation, which,
as just noted, is based on developing trajectories on a trian-
gular composition diagram. The residue diagram shows the
liquid composition trajectory that will occur in a packed
distillation column while a distillation diagram shows the
composition points that occur in a plate column. These
diagrams show that so-called residue or distillation bound-
aries divide the space, and experience demonstrates that it
8. 454 A.W. Westerberg / Computers and Chemical Engineering 28 (2004) 447–458
is difficult to cross these boundaries using distillation. Thus
these diagrams give insights into how to carry out these
separations.
The latest twist in separation system synthesis is to in-
clude reaction in a column. Motivated by the previously
mentioned enormous success of Eastman Chemicals in 1984
in designing an entirely new process for the manufacture
of methyl acetate (Agreda & Partin, 1983; Agreda et al.,
1990) interest in reactive distillation exploded. The process
manufactures methyl acetate and recovers it as a pure prod-
uct in a single reactive, extractive, two feed, two product
column. The species involved display azeotropic behavior,
which made the previous processes very complex, as they
comprised a reactor followed by a complex separation train.
Early contributions to reactive distillation were additions to
the simulators that allowed one to simulate such columns.
Balashov and Serafimov (1980) proposed their “static
analysis” approach for designing a reactive column. Doherty
and his students (Barbosa & Doherty, 1988a,b; Buzad &
Doherty, 1994, 1995; Ung & Doherty, 1995) showed that one
could transform reactive distillation problems into a space
that made them look like a normal distillation problem. This
work argued that reaction could destroy azeotropes, and,
when destroyed, one could get past them in a single column.
Hauan and Lien (1998) realized that one could study re-
action, separation and mixing as additive vectors in com-
position space. The directions for the vectors are properties
of the species only (and for mixing, the composition of the
other mixture) while the lengths are based on the design
and operating decisions made. We can readily understand
reactive azeotropes as points where these three processes
cancel each other vectorially. Cancellation comes from hav-
ing the right directions to allow cancellation and the right
design and operating decisions to give one the right vector
lengths. Hauan, Ciric, Westerberg, and Lien (2000) then dis-
covered that all reaction vectors for a given reaction point at
a fixed reaction difference point whose position in compo-
sition space we compute using only the stoichiometric co-
efficients for the reaction. We can now combine sketching
of residue curves for distillation with the reaction difference
point and quickly sketch behavior for both on a composition
diagram (Lee, Hauan, Lien, & Westerberg, 2000a,b; Lee,
Hauan, & Westerberg, 2000; Lee & Westerberg, 2001).
4.4.3. Reactors
Two important recent books on azeotropic and reactive
distillation are by Stichlmair and Fair (1998) and Doherty
and Malone (2001). Synthesis of reaction pathways became
a very active and productive research area in chemistry
(see, for example, Corey & Wipke, 1969; Ugi & Gillespie,
1971) and chemical engineering (Agnihotri & Motard,
1980; Govind & Powers, 1977; Prickett & Mavrovouniotis,
1997a,b). The main approach was to embed the rules we all
learned in organic chemistry into the computer. The prob-
lems solved included finding all pathways that could lead to
a particular molecule and finding all molecules that one can
produce from given starting materials. Thousands of path-
ways resulted, making the assessment of each the real prob-
lem. Thermodynamics allows one to rule out unfavorable
reactions, but one really needs to estimate the kinetics to
assess each commercially, an elusive problem even now and
even with being able to do molecular dynamic calculations.
Thus, these systems prefer reactions where one knows ex-
perimentally that the reactions take place at favorable rates.
Given a reaction pathway, one had to design one or more
reactors to carry out the reactions for that pathway. Early
work here (Chitra & Govind, 1985a,b) proposed optimizing
superstructures of reactors containing both stirred tanks and
continuous plug flow reactors in all possible configurations.
Such problems will typically have local optima, making this
approach difficult.
Again, representation gives us some real insights into this
problem. Glasser et al. (1987) asked a different question.
They wondered if one could compute all possible composi-
tions one could reach given a set of reactions and their rate
equations. This is like asking to find all the squares on a chess
board that one can reach using a knight using only one step,
then using two steps, and then three steps, etc. Reachability
as an approach dates back to work by Horn (1964). Glasser
et al. discovered one could derive properties of the reachable
region. It is connected; indeed, it is convex. A point at the ex-
treme cannot have a rate vector that points outside the region.
There can be no rate vector outside the attainable region that
points back into it. Originally, this work only considered
isothermal processes, but they have shown that it is straight-
forward to extend it to non-isothermal conditions. Also they
are able to argue that one can reach the extreme points of
the attainable regions using combinations of the differential
side stream, stirred tank and plug flow reactors only.
In a recent excellent article, Feinberg (2002) reviews the
theoretical results available for attainable regions for reac-
tor/mixer and reactor/mixer/separator systems.
4.4.4. Complete flowsheets
The goal for all of process synthesis is to discover the best
complete flowsheet to accomplish a chemical-manufacturing
goal. Interestingly, one of the very early synthesis problems
was just this one. Siirola et al. (1971) proposed the AIDES
system to synthesize complete flowsheets using, among
other things, means/ends ideas from artificial intelligence. A
very significant contribution of this work was to order the de-
sign decisions into a hierarchy. Their system expected as in-
put the possible reaction paths it might use. Given a reaction
path, they selected the separation tasks, then the heating and
cooling tasks and finally the pressure changing tasks. Equip-
ment design followed based on the tasks selected. Mahalec
and Motard (1977a,b) proposed their Baltazar system,
where they used the unification principle from propositional
calculus to develop alternative reaction paths, followed by
separation tasks and so forth. Their approach differed in
that all operators (reaction, heating, pressure changing, etc.)
could reappear at any time when developing a solution.
9. A.W. Westerberg / Computers and Chemical Engineering 28 (2004) 447–458 455
Douglas (1988) produced a design text in Chemical En-
gineering based on his hierarchical decomposition approach
to process synthesis. The essence of his approach was to put
the decisions into an order, much like the previous work. His
decision ordering was in levels: (1) choosing between con-
tinuous and batch, (2) selecting the raw materials and prod-
ucts, (3) selecting the reactor based on reaction selectivities,
(4) designing the vapor and liquid separation systems, and
(5) designing the heat recovery system. He wrote only the
constraints that each level imposed on the design and used
these to eliminate regions of space that could not meet these
constraints. For example, level 2 only imposes reaction sto-
ichiometries and selectivities. Reaction paths selected had
to produce products that are more valuable than the raw ma-
terials. Many of his decisions were in the form of rules so
the decision making could be quite quick. However, being
rule based, it does not assure getting the best decisions.
Daichendt and Grossmann (1998) developed optimiza-
tion approaches—based on developing bounds—to make
the decisions for the hierarchical decomposition method of
Douglas.
One overriding message in all of the above methods is that
the problem became manageable only when one imposed an
order on the decisions to make. That ordering is largely based
on making decisions first at abstract levels—eliminating ma-
jor parts of the design space—and then at more and more
refined levels.
In each of the above, there exists a computer program
to illustrate the ideas. If one can convert the decision mak-
ing into a program, then one has a routine design problem
because the program must explicitly or implicitly state the
design space. These systems can be very powerful and can
lead to automatic design for well-understood problems.
4.5. Transport/thermo view
We have based chemical engineering design and opera-
tion largely on thinking of processes as interconnected unit
operations. Unit operations first appeared as a concept about
1915. The methyl acetate manufacture process developed by
Eastman Chemicals seems to be outside this way of think-
ing. It broke with tradition by not thinking about design as
the configuration of known unit operations but rather as a set
of tasks that one needed to accomplish in equipment yet to
be selected, and it led to better than 80% reductions in costs
rather than the 5% improvements we seem to find with the
more traditional thinking. We see further evidence of this dif-
ferent thinking when we then examine the work by Lien and
coworkers described above on combining separation, mixing
and reaction. Both seem to be thinking more about construct-
ing processes by configuring transport processes. This type
of thinking is also in the automatic model generation work
by Sargent et al. (1994), Stephanopoulos et al. (1990a,b),
and Nagel, Han, and Stephanopoulos (1995). Their work
allows a modeler to create variations of conventional unit
operation models by combining transport processes.
Our hypothesis then is that we might find future design
methods based on thinking in transport space that will give us
significant process improvements. Rather than using existing
unit operations, we will be indirectly inventing new ones
tailored for the design at hand. Perhaps, with time, we will
simply settle on a new set of more versatile unit operations
and return to the unit operation approach for design with
much improved results.
4.6. Retrofit
The last topic we discuss on design itself is that of retrofit
versus grassroots design. We have argued (Grossmann,
Biegler, & Westerberg, 1987) that retrofit design must be
more complicated than grassroots design as the retrofit
design space includes doing a grassroots design as an al-
ternative. Actually, the retrofit problem is in principle com-
binatorially much, much larger than a grass roots design.
In retrofit design we not only enumerate the design alter-
natives, but then, for each alternative, we have to decide
which of the existing equipment we can reuse and where.
Of course, we can tame the problem by limiting how much
of a process we are willing to redesign or by limiting the
budget to be a small fraction of the cost of a new plant.
Many capital intensive projects in the last two and half
decades have been retrofit designs. The research community
should spend more time investigating how to approach these
problems. We can and should argue, of course, that what we
learn to aid grassroots design also gives insights needed for
retrofit design. However, the assigning of tasks to existing
equipment leads to a different type of analysis. We need
to know if the equipment can function successfully when
it is markedly away from its normal operating range. For
example, will the assigned column weep when it is much
taller and wider than we need? We should note that flexible
operation of new designs also leads to these same modeling
issues, so we need some common capabilities in modeling
for both.
5. In closing
We have looked at the impact of PSE on the design of
chemical processes. The impact became possible with the
advent of computing. Desktop computing has attained “giga”
rates for both instructions executed per second and bit trans-
fer over our inter- and intranets and giga sizes for fast mem-
ory. In June of 2000, IBM announced7 a 12.3 teraflop (1012)
computer, over 1000 times faster than the fastest PCs at that
time. What we will be able to do (beside playing chess)
with this power is still largely unexplored and likely not yet
even in our thoughts. One thought is that we are moving
7 http://www.idg.net/idgns/2000/06/29/USDeptAwaitsIBM123Teraflop
Supercomputer.shtml.
10. 456 A.W. Westerberg / Computers and Chemical Engineering 28 (2004) 447–458
from computers being super computers to computers being
information processors.
We tried to show that design is multi-faceted. Some de-
signs are routine, and one can fully automated the process
to perform them. However, for others we are not able to
conceive of the design space and thus must support in-
novation by designers. When we can propose the design
space, we can often formulate our problem in mathematical
programming terms. However, even for these problems, we
struggle with formulating valid merit functions with which
to compare designs. Currently, we have great difficulty in
solving such problems, but we will learn how to do that. For
example, given enough cycles on a computer, we can auto-
matically scatter all sorts of starting points and approaches
to solving, picking the better ones from the results, rather
than limiting ourselves to trying only one.
The views throughout this paper are limited and heavily
biased with the author’s understanding of these issues. As
with any overview, there are unintentional and almost cer-
tainly key omissions and misunderstandings, and, for these,
sincere apologies are extended.
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