Interactivity and automated process desi
Communications
Interactivity and Automated Process
Design
By Eric S. Fraga*, Kefeng Wang, and Abdellah Salhi
Automated process design tools are often based on the use
of optimization to identify the best process flowsheet,
typically in the early stages of design. Due to the non-linear
models that are frequently required, the resulting optimization problem is difficult to solve. Difficulties arise both in
identifying good initial solutions and in finding the global
optimum. In some cases, even feasible initial points can be
difficult to find. Furthermore, the objective function and the
feasible search space may be non-convex. As a result,
automation alone can be insufficient for the solution of
difficult process design problems. The engineer can, and
arguably should, be involved in the process of design, and tools
that encourage this direct involvement are desirable. In early
design, exploration of alternatives can be useful.
Jacaranda is a system for automated design [1]. It is based on
the use of discrete programming, in conjunction with implicit
enumeration and branch and bound techniques, to solve
process design problems with complex models. Jacaranda has
been designed to support interactivity to ensure the maximum
effectiveness from the combination of the computer and the
engineer [2]. The aim is to allow the engineer to perform the
tasks that a human can do best while ensuring that the
computationally complex and repetitive steps are undertaken
by the automated design tool. In this context, Jacaranda
provides visualization and data mining support for the
engineer [3,4].
Examples demonstrating the potential of an interactive
automated design approach are presented. These include heat
integrated separation sequence synthesis, reaction/separation
with environmental issues, and a process optimization
problem with small feasible regions. Interactivity is used to
support an iterative refinement approach to process synthesis
or to target optimization procedures to improve the quality of
the solutions obtained. Visualization is shown to be a key
underpinning technology, which also aids the engineer in
understanding the results obtained by automated design tools.
computer tools for information management and for process
and unit design, analysis and optimization. The time scales in
process development may vary significantly depending on the
type of process, but in general it takes from a few weeks to a
few months.
Fig. 1 shows the different steps involved in generating a
process design. The boxes indicate the inputs and outputs of
each step and the arrows, labelled with numbers, indicate the
steps:
1. Identify the products. This step starts with a (perceived)
market need. Although this step is not traditionally part of
conceptual process development, it arguably should be.
There may be a choice in products that meet the desired need
and the choice of products will have an impact on the final
process design. An immediate potential effect of product
choice on the process design is the type of reaction that will
be required, with corresponding effects on technology
choice and environmental impacts due to solvent use.
2. Identify the raw materials and possible processing steps
that could be used to convert the raw materials to the
desired products. This step requires interaction between
1 Automated Design and the Design Process
Conceptual process development is a team-based activity
with multi-disciplinary inputs and relies on a wide range of
±
[*]
E. S. Fraga, K. Wang, Centre for Process Systems Engineering,
Department of Chemical Engineering, UCL; A. Salhi, Department of
Mathematics, University of Essex, Great Britain.
Chem. Eng. Technol. 26 (2003) 8
DOI: 10.1002/ceat.200300001
Figure 1. The design process from market identification through to initial
process structure.
Ó 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
823
Communications
the research and development teams. For instance, in
chemical industries, the interaction will be between
engineers and chemists. Immediately, issues about multidisciplinarity arise but so do issues about scale (laboratory
versus production plant).
3. Generate alternative process designs. Most processes will
have alternatives, both for individual steps and for the
sequence of these steps. Generating these alternatives is
currently frequently based on heuristic or evolutionary
approaches whereby previous experience with similar
processes often determines the set of alternatives to
consider. Computer based approaches include expert
systems and fully algorithmic tools based on optimization
techniques. Alternative approaches include the generation
of virtual superstructure models, models that include
processing structures without explicitly describing actual
specific operating steps (an example is the use of the heat
cascade to model a heat exchanger network).
4. Select the best process design from the set of alternatives.
The selection procedure will often be based on short-cut
models of the individual steps, solved and evaluated to
yield estimates of the economics and overall performance
of the process. Some selection will have been done in the
previous step, depending on the method used for generating the alternatives. Other criteria may also be used in
process selection, including safety, operability, and environmental impact, depending on the information available
for evaluating these criteria. The need to handle multiple
criteria leads to requirements for multi-objective optimization techniques, such as pareto curve generation. Furthermore, many of the criteria are based on approximate
models so selection procedures may be required to work
with uncertainties. One approach for dealing with uncertainties is to generate multiple solutions at the conceptual
stage of design.
The figure also shows a link back from the result of step 4,
the process flowsheet, to the start of the design process. This
backward link actually exists between any intermediate stage
in the process back to any other stage; only one back link is
shown for clarity of presentation. Back links are necessary
because problems are often not well understood at the
beginning. It is only through an attempt to solve a problem
that further understanding is gained, enabling a better
definition of the problem. For instance, a particular reaction
path may have been chosen, in step 2, to generate the desired
products. In steps 3 and 4, it may be found that this particular
reaction path leads to unacceptable environmental impact or
even an inability to generate a profit due to processing costs.
One option, in this case, would be to return to step 2 and try to
identify an alternative reaction path, one that avoids some or
all of the problems found with the first option.
Although it is desirable to avoid back links in the design
process, it is arguable as to whether this is achievable.
Assuming that it is not, then one aspect of process design is
how to make the iterative procedure more effective. This
paper describes how the Jacaranda system, a system for
824
automated process design, can be used to support this
iteration, typically using insight gained in steps 3 and 4 to
refine the problem formulation which is the input to step 3.
2 Jacaranda and Interactivity
The Jacaranda system [1] provides a generic object oriented
framework for automated design. It has been applied to a wide
range of problems including heat integrated distillation
sequencing [5], reaction/separation [6] and bioprocessing [7].
Jacaranda has been designed as a vehicle for exploring new
techniques in optimization, visualization, and data analysis for
automated design using complex non-linear models.
Jacaranda implements a graph generation and search
algorithm using a combination of implicit enumeration,
branch and bound, and discrete programming techniques.
The synthesis problem definition consists of a list of raw
materials, a list of processing technologies, and a list of desired
or acceptable products. All of these are represented by unit
models. The problem definition also includes the ranking
criteria for selecting a small set of alternatives from the
complete search graph. Jacaranda is able to generate multiple
solutions for each criterion simultaneously.
The support for interactivity is based on a combination of
features. These include easy to prepare input files, embedded
data analysis procedures, visualization of results and the
search space, and the ability to generate partial solutions [6].
The combination of these features, together with the
efficiency of the underlying graph algorithms, provides an
environment, which encourages the user to explore different
options easily and quickly. The effectiveness of these features
is highlighted through the presentation of three case studies in
the next section.
3 Case Studies
Three case studies have been selected to demonstrate the
features of Jacaranda, which provide the support necessary for
the interactive use of a synthesis tool. The first two case studies
have been presented in detail elsewhere [5,8] and only the
features relevant to interactivity in design are highlighted
here. The third case study is new and presents recent work in
the use of data mining [3,4] and visualization techniques for
targeted optimization. All three cases are based on the
iterative and interactive use of Jacaranda both for exploration
of possibilities and to identify better designs. Interactivity is a
key feature for making the most effective use of computational tools [2] and this is particularly true for the use of
automation in early design.
3.1 Case Study I: Production of Hydrogen Fluoride (HF)
One of the key problems in the use of automated design
tools is the definition of the actual design problem. In early
Ó 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
http:www.cet-journal.de
Chem. Eng. Technol. 26 (2003) 8
Communications
design, there are many unknowns, some of which make it
difficult to specify a complete problem definition for
optimization. For instance, it may not be apparent, a priori,
what types of waste streams are likely to appear in different
process alternatives. In such a case, it would be difficult to
define appropriate specifications for these waste streams,
including, for instance, the cost of processing them. The
Jacaranda system, and its precursor CHiPS [9], include the
concept of partial solutions, solutions which are not feasible
but which achieve some of the key design conditions specified
by the user [6]. Partial solutions can be used to identify streams
that would typically be considered as waste, allowing the user
to subsequently refine the problem definition to include full
specifications of these.
The use of partial solutions is illustrated in a case study for
the design of a process for the production of hydrogen fluoride
(HF). The first step in this case study is the definition of the
problem using the information immediately available. This
includes the reaction, a reactor model for this reaction, the
specification of the desired product, and the identification of
appropriate separation technologies, distillation alone initially, for purification of the product. With this problem
definition, the synthesis tool cannot identify a fully feasible
solution due to the appearance of waste streams, which do not
meet the product specification. However, partial solutions can
be identified. These solutions include partial process flowsheets, which include the reactor, one or more distillation
units, and a product tank for the desired product. Other
streams are left dangling, indicating that the synthesis
procedure cannot identify any means of further processing
these. At this point, the user is required to investigate the
partial solutions and attempt to refine the problem definition
to incorporate any information about the problem gained
from the partial solutions.
In the case study, two types of waste stream were identified:
a solid waste and a vapor waste. Two extra product
specifications, with appropriate costings, were then added to
the problem definition and the problem was solved again. This
time, fully feasible solutions were identified which included
the processing of the waste streams.
Subsequent steps in this case study included the exploration
of alternatives generated by tightening environmental constraints and through the consideration of alternative separation technologies such as absorption. These subsequent steps
are encouraged through the ease of preparation of input files
for the problem definition, as illustrated by Fraga [6].
3.2 Case Study II: Large-Scale Heat Integrated Process
Synthesis
The second case study is the design of large heat integrated
distillation sequences. Such problems are difficult to solve due
to their highly combinatorial nature. Traditionally, two-step
procedures are used in which the distillation sequence is
chosen and then a heat exchanger network, including process
Chem. Eng. Technol. 26 (2003) 8
http://www.cet-journal.de
integration, is generated. This approach will typically not
identify the optimal process configuration [10]. Therefore,
most recent work on heat integrated process synthesis
techniques [11] has concentrated on simultaneous approaches, generating both a unit sequence and heat exchanger
network together.
A simultaneous approach, however, is computationally
difficult. Superstructure approaches have been shown effective for small separation problems, of the order of three or four
distillation units for example. Larger problems require
targeted procedures such as Jacaranda. In Jacaranda, a
discrete approach has been implemented based on the
definition of virtual heat links. A virtual heat link defines a
stream at a given temperature and with a predefined flux,
which can be used to exchange heat within a process, both as a
source of heat and as a sink of heat. By careful choice of these
virtual streams, process flowsheets, which are amenable to
efficient energy use, can be identified. The difficulty is in the
choice of these streams as the problem grows exponentially
with the number of virtual streams defined. Without any a
priori knowledge of the search space, a large number of virtual
heat links would be required, which leads to an intractable
problem.
The alternative is an iterative approach. Jacaranda is
applied to the separation problem once without heat integration. Data mining techniques are then used to investigate the
potential for heat integration within the search graph
generated by Jacaranda. The results of the data-mining step
are a small set of heat links. The problem is then solved again,
now considering heat integration through the use of these
links. This iterative procedure has been shown to be effective.
However, for some problems the data-mining step is unable
to define good virtual heat links automatically. In these cases,
the user can interact with the system to suggest, based on a
visualization of the heating and cooling requirements, better
choices for the virtual heat links. This interaction has been
shown to be particularly effective for large processes [5].
3.3 Case Study III: Oil Stabilization Process
A difficulty that often arises in the modeling and optimization of chemical engineering processes is the problem of
finding feasible initial points. This is often due to the large
number of equality constraints imposed by physical property
estimation methods. Sometimes, however, this is also due to
the tight specifications imposed on product streams. Problems
with effect product specifications, are particularly difficult
due to the non-linear and possibly non-convex feasible regions
that result.
This third case study is an example of a process design
problem with product specifications based on desired properties instead of composition. Specifically, an oil stabilization
process aims to produce an oil product with a desired vapor
pressure at a given reference temperature. The composition of
such a stream cannot be described directly. The problem is also
Ó 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
825
Communications
difficult due to the use of flash vessels as the main separation
technology and the large number of components. The problem
considered has a two-phase 12-component feed stream. A
typical process flowsheet for this problem is shown in Fig. 2.
The discrete nature of Jacaranda means that it is possible to
miss feasible solutions entirely, depending on the discretization parameters used. If the discretizations are too coarse, no
information collected is an indication of the feasible domain
for each of the processing nodes. This information provides
the user with a view of the search space and the effectiveness
of the search performed by Jacaranda. It should be noted that
focusing on the information associated with a particular
topology would, in some cases, provide limited information
about the overall search space. However, Jacaranda has the
capability of generating a ranked list of solutions instead of
just one solution, as is the case for most optimization
procedures. The data analysis step can be applied to each of
these solutions in turn so as to ensure a more comprehensive
cover of the search space in the post-synthesis analysis.
Fig. 3 shows the parallel coordinate system (PCS) representation of the data points collated. Of interest is the
information about the temperatures used for the flash units;
the temperatures have a greater effect on the feasibility of a
solution than do the pressures of the valves and throttles. The
Figure 2. Example of an oil stabilization process.
valid product streams may be found. Fine discretizations,
however, lead to untenable computational demands, both in
time and memory, due to the non-sharp nature of the
separation technology and the large number of components.
Therefore, an iterative approach is often required whereby the
user focuses in on areas of interest.
The iterative procedure is based on using Jacaranda to
attempt the problem, generating data about the search space.
This search space is then analyzed using visualization and
cluster analysis techniques, including tools such as XGobi [12]
and recent developments by Wang et al. [13], augmented with
user interaction. The information gained from this postprocessing is then used to refine the search space in Jacaranda,
limiting in particular the operation of flash vessels to narrower
ranges of temperatures.
The following is an example of this iterative procedure
applied to the oil stabilization problem. The first step is to
solve the problem using a coarse discretization with all
variables allowed to vary over their full domain. In the first
instance, the aim is to find feasible points so the objective
function is the minimization of the deviation from the desired
vapor pressure of the oil product. The result of this first step is
a feasible solution with objective function value ±3.65e8 $/y,
the annualized profit including both capital and operating
costs.
The post-synthesis data analysis processes the data generated by the search procedure and collates all the data that is
applicable to the solution actually obtained. A solution is a
sub-graph embedded in a much larger graph, which represents
the search space. The search graph will contain a number of
sub-graphs, which represent structures topologically equivalent to the best solution found. These sub-graphs are extracted
from the data collected by the search procedure and the
differences between the nodes in the graphs are analyzed. The
826
Figure 3. Parallel co-ordinate system representation of initial search space for
oil stabilization problem.
operating temperatures for the first two flash units (variables
T1 and T2) are restricted to the upper half of their respective
domains. In comparison, the operating temperatures for the
second pair of flash units (variables T3 and T4) more fully
cover their domains. From this analysis, solving the problem
can be considered again, now restricting the variables to
smaller domains, as suggested by the visualization. It is
interesting to note, at this stage, that the visualization
procedures can also provide more information about the
search space than just domains of interest. For instance, Fig. 4
shows the values of the operating temperatures for the 3rd and
4th flash units for the feasible points found. A clear inverse
linear relationship is seen between these two variables, a
relationship that is also discernible in Fig. 3. This type of
relationship was previously used to target post-synthesis
optimization using stochastic optimization [13].
The second attempt can use less discrete points as each
value considered is more likely to provide useful solutions. In
fact, due to the data recording performed by Jacaranda, the
number of discrete levels used may have to be reduced to
ensure that memory requirements do not exceed the resources
available. Fig. 5 shows the PCS representation of the search
Ó 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
http:www.cet-journal.de
Chem. Eng. Technol. 26 (2003) 8
Communications
The first case study is a simple example of the application of
the user's own insight into possible alternatives, using the
automated design tool for verification and calculation. The
second case study illustrates how the design procedure can
provide information that the engineer can use to make
decisions about the search space. The third case study
demonstrates more targeted data analysis procedures, which
not only provide information about alternative solutions with
the same topology but may also highlight relationships
between the different design parameters.
The aim of the Jacaranda system is to provide the engineer
with an easy to use tool, which encourages exploration of the
different alternatives available for a specific design problem.
By enhancing the solution procedure with data collection
capabilities and by providing automated analysis and visualization procedures, the engineer can easily identify key
aspects of the search space. These aspects can then be
incorporated into the problem definition as the basis for the
iterative use of the design tools.
Figure 4. Operating temperatures for flash units 3 and 4 in initial oil stabilization
process.
Acknowledgment
The authors gratefully acknowledge support from the
Engineering and Physical Sciences Research Council of the
United Kingdom, grant number GR/M83643.
Received: April 1, 2003 [ECCE 1]
References
Figure 5. PCS representation of second attempt search space in reduced
domain.
space for the second attempt. The bounds on the variables are
based on the reduced domain identified in the first step. It is
seen that the second attempt has made full use of the search
domain for the flash unit operation temperatures. The
objective function value is now ±3.72e8 $/y, an improvement
over the first attempt.
At this point, further restriction of the search space can be
performed. Alternatively, the user can decide that the
structure obtained is suitable for more detailed analysis, fixing
the structure and optimizing design and operating parameters.
4 Discussion
Automated design procedures can provide the basis for an
interactive design procedure. The engineer can use the design
tools for exploration of the search space, guiding the software
to find better solutions. This paper has illustrated this through
three cases studies.
Chem. Eng. Technol. 26 (2003) 8
http://www.cet-journal.de
[1] E. S. Fraga, M. A. Steffens, I. D. L. Bogle, A. K. Hind, An ObjectOriented Framework for Process Synthesis and Simulation, in Foundations of Computer Aided Process Design (Eds: M. F. Malone, J. A.
Trainham, B. Carnahan) AIChE Symp. Ser. 2000, 96, 446.
[2] P. Wegner, Comm. ACM 1997, 40 (5), 80.
[3] P. Adriaans, D. Zatinge, Data Mining, Addison Wesley Longman,
Harlow 1996.
[4] R. Agrawal, G. Psalia, Active Data Mining, in Proc. of the 1st Int. Conf.
on Knowledge Discovery and Data Mining, KDD95, AAAI Press,
Menlo Park (CA) 1995, 3.
[5] E. S. Fraga, K. I. M. McKinnon, A Scalable Discrete Optimization
Algorithm for Heat Integration in Early Design, in Scientific Computing
in Chemical Engineering, II. Simulation, Image Processing, Optimization, and Control (Eds: F. Keil, W. Mackens, H. Voû, J. Werther),
Springer, Berlin 1999, 306.
[6] E. S. Fraga, Chem. Eng. Res. Des. 1998, 76 (A1), 45.
[7] M. A. Steffens, E. S. Fraga, I. D. L. Bogle, Biotechnol. Bioeng. 2002, 68
(2), 218.
[8] D. M. Laing, E. S. Fraga, Comput. Chem. Eng. 1997, 21(Suppl.), S53.
[9] E. S. Fraga, K. I. M. McKinnon, Chem. Eng. Res. Des. 1994, 72 (A3), 389.
[10] N. Nishida, G. Stephanopoulos, A. W. Westerberg, AIChE J. 1981, 27
(3), 321.
[11] I. E. Grossmann, J. A. Caballero, H. Yeomans, Korean J. Chem. Eng.
1999, 16 (4), 407.
[12] D. F. Swayne, D. Cook, A. Buja, J. Comput. Graph. Stat. 1998, 7 (1).
[13] K. Wang, A. Salhi, E. S. Fraga, Cluster Identification Using a Parallel
Coordinate System for Knowledge Discovery and Non-linear Optimization, in Eur. Symp. on Computer-Aided Process Eng. ± 12 (Eds:
J. Grievink, J. van Schijndel), Elsevier, Amsterdam 2002; Comput.Aided Chem. Eng. 2002, 10, 1003.
Ó 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
827
Interactivity and Automated Process
Design
By Eric S. Fraga*, Kefeng Wang, and Abdellah Salhi
Automated process design tools are often based on the use
of optimization to identify the best process flowsheet,
typically in the early stages of design. Due to the non-linear
models that are frequently required, the resulting optimization problem is difficult to solve. Difficulties arise both in
identifying good initial solutions and in finding the global
optimum. In some cases, even feasible initial points can be
difficult to find. Furthermore, the objective function and the
feasible search space may be non-convex. As a result,
automation alone can be insufficient for the solution of
difficult process design problems. The engineer can, and
arguably should, be involved in the process of design, and tools
that encourage this direct involvement are desirable. In early
design, exploration of alternatives can be useful.
Jacaranda is a system for automated design [1]. It is based on
the use of discrete programming, in conjunction with implicit
enumeration and branch and bound techniques, to solve
process design problems with complex models. Jacaranda has
been designed to support interactivity to ensure the maximum
effectiveness from the combination of the computer and the
engineer [2]. The aim is to allow the engineer to perform the
tasks that a human can do best while ensuring that the
computationally complex and repetitive steps are undertaken
by the automated design tool. In this context, Jacaranda
provides visualization and data mining support for the
engineer [3,4].
Examples demonstrating the potential of an interactive
automated design approach are presented. These include heat
integrated separation sequence synthesis, reaction/separation
with environmental issues, and a process optimization
problem with small feasible regions. Interactivity is used to
support an iterative refinement approach to process synthesis
or to target optimization procedures to improve the quality of
the solutions obtained. Visualization is shown to be a key
underpinning technology, which also aids the engineer in
understanding the results obtained by automated design tools.
computer tools for information management and for process
and unit design, analysis and optimization. The time scales in
process development may vary significantly depending on the
type of process, but in general it takes from a few weeks to a
few months.
Fig. 1 shows the different steps involved in generating a
process design. The boxes indicate the inputs and outputs of
each step and the arrows, labelled with numbers, indicate the
steps:
1. Identify the products. This step starts with a (perceived)
market need. Although this step is not traditionally part of
conceptual process development, it arguably should be.
There may be a choice in products that meet the desired need
and the choice of products will have an impact on the final
process design. An immediate potential effect of product
choice on the process design is the type of reaction that will
be required, with corresponding effects on technology
choice and environmental impacts due to solvent use.
2. Identify the raw materials and possible processing steps
that could be used to convert the raw materials to the
desired products. This step requires interaction between
1 Automated Design and the Design Process
Conceptual process development is a team-based activity
with multi-disciplinary inputs and relies on a wide range of
±
[*]
E. S. Fraga, K. Wang, Centre for Process Systems Engineering,
Department of Chemical Engineering, UCL; A. Salhi, Department of
Mathematics, University of Essex, Great Britain.
Chem. Eng. Technol. 26 (2003) 8
DOI: 10.1002/ceat.200300001
Figure 1. The design process from market identification through to initial
process structure.
Ó 2003 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
823
Communications
the research and development teams. For instance, in
chemical industries, the interaction will be between
engineers and chemists. Immediately, issues about multidisciplinarity arise but so do issues about scale (laboratory
versus production plant).
3. Generate alternative process designs. Most processes will
have alternatives, both for individual steps and for the
sequence of these steps. Generating these alternatives is
currently frequently based on heuristic or evolutionary
approaches whereby previous experience with similar
processes often determines the set of alternatives to
consider. Computer based approaches include expert
systems and fully algorithmic tools based on optimization
techniques. Alternative approaches include the generation
of virtual superstructure models, models that include
processing structures without explicitly describing actual
specific operating steps (an example is the use of the heat
cascade to model a heat exchanger network).
4. Select the best process design from the set of alternatives.
The selection procedure will often be based on short-cut
models of the individual steps, solved and evaluated to
yield estimates of the economics and overall performance
of the process. Some selection will have been done in the
previous step, depending on the method used for generating the alternatives. Other criteria may also be used in
process selection, including safety, operability, and environmental impact, depending on the information available
for evaluating these criteria. The need to handle multiple
criteria leads to requirements for multi-objective optimization techniques, such as pareto curve generation. Furthermore, many of the criteria are based on approximate
models so selection procedures may be required to work
with uncertainties. One approach for dealing with uncertainties is to generate multiple solutions at the conceptual
stage of design.
The figure also shows a link back from the result of step 4,
the process flowsheet, to the start of the design process. This
backward link actually exists between any intermediate stage
in the process back to any other stage; only one back link is
shown for clarity of presentation. Back links are necessary
because problems are often not well understood at the
beginning. It is only through an attempt to solve a problem
that further understanding is gained, enabling a better
definition of the problem. For instance, a particular reaction
path may have been chosen, in step 2, to generate the desired
products. In steps 3 and 4, it may be found that this particular
reaction path leads to unacceptable environmental impact or
even an inability to generate a profit due to processing costs.
One option, in this case, would be to return to step 2 and try to
identify an alternative reaction path, one that avoids some or
all of the problems found with the first option.
Although it is desirable to avoid back links in the design
process, it is arguable as to whether this is achievable.
Assuming that it is not, then one aspect of process design is
how to make the iterative procedure more effective. This
paper describes how the Jacaranda system, a system for
824
automated process design, can be used to support this
iteration, typically using insight gained in steps 3 and 4 to
refine the problem formulation which is the input to step 3.
2 Jacaranda and Interactivity
The Jacaranda system [1] provides a generic object oriented
framework for automated design. It has been applied to a wide
range of problems including heat integrated distillation
sequencing [5], reaction/separation [6] and bioprocessing [7].
Jacaranda has been designed as a vehicle for exploring new
techniques in optimization, visualization, and data analysis for
automated design using complex non-linear models.
Jacaranda implements a graph generation and search
algorithm using a combination of implicit enumeration,
branch and bound, and discrete programming techniques.
The synthesis problem definition consists of a list of raw
materials, a list of processing technologies, and a list of desired
or acceptable products. All of these are represented by unit
models. The problem definition also includes the ranking
criteria for selecting a small set of alternatives from the
complete search graph. Jacaranda is able to generate multiple
solutions for each criterion simultaneously.
The support for interactivity is based on a combination of
features. These include easy to prepare input files, embedded
data analysis procedures, visualization of results and the
search space, and the ability to generate partial solutions [6].
The combination of these features, together with the
efficiency of the underlying graph algorithms, provides an
environment, which encourages the user to explore different
options easily and quickly. The effectiveness of these features
is highlighted through the presentation of three case studies in
the next section.
3 Case Studies
Three case studies have been selected to demonstrate the
features of Jacaranda, which provide the support necessary for
the interactive use of a synthesis tool. The first two case studies
have been presented in detail elsewhere [5,8] and only the
features relevant to interactivity in design are highlighted
here. The third case study is new and presents recent work in
the use of data mining [3,4] and visualization techniques for
targeted optimization. All three cases are based on the
iterative and interactive use of Jacaranda both for exploration
of possibilities and to identify better designs. Interactivity is a
key feature for making the most effective use of computational tools [2] and this is particularly true for the use of
automation in early design.
3.1 Case Study I: Production of Hydrogen Fluoride (HF)
One of the key problems in the use of automated design
tools is the definition of the actual design problem. In early
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design, there are many unknowns, some of which make it
difficult to specify a complete problem definition for
optimization. For instance, it may not be apparent, a priori,
what types of waste streams are likely to appear in different
process alternatives. In such a case, it would be difficult to
define appropriate specifications for these waste streams,
including, for instance, the cost of processing them. The
Jacaranda system, and its precursor CHiPS [9], include the
concept of partial solutions, solutions which are not feasible
but which achieve some of the key design conditions specified
by the user [6]. Partial solutions can be used to identify streams
that would typically be considered as waste, allowing the user
to subsequently refine the problem definition to include full
specifications of these.
The use of partial solutions is illustrated in a case study for
the design of a process for the production of hydrogen fluoride
(HF). The first step in this case study is the definition of the
problem using the information immediately available. This
includes the reaction, a reactor model for this reaction, the
specification of the desired product, and the identification of
appropriate separation technologies, distillation alone initially, for purification of the product. With this problem
definition, the synthesis tool cannot identify a fully feasible
solution due to the appearance of waste streams, which do not
meet the product specification. However, partial solutions can
be identified. These solutions include partial process flowsheets, which include the reactor, one or more distillation
units, and a product tank for the desired product. Other
streams are left dangling, indicating that the synthesis
procedure cannot identify any means of further processing
these. At this point, the user is required to investigate the
partial solutions and attempt to refine the problem definition
to incorporate any information about the problem gained
from the partial solutions.
In the case study, two types of waste stream were identified:
a solid waste and a vapor waste. Two extra product
specifications, with appropriate costings, were then added to
the problem definition and the problem was solved again. This
time, fully feasible solutions were identified which included
the processing of the waste streams.
Subsequent steps in this case study included the exploration
of alternatives generated by tightening environmental constraints and through the consideration of alternative separation technologies such as absorption. These subsequent steps
are encouraged through the ease of preparation of input files
for the problem definition, as illustrated by Fraga [6].
3.2 Case Study II: Large-Scale Heat Integrated Process
Synthesis
The second case study is the design of large heat integrated
distillation sequences. Such problems are difficult to solve due
to their highly combinatorial nature. Traditionally, two-step
procedures are used in which the distillation sequence is
chosen and then a heat exchanger network, including process
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integration, is generated. This approach will typically not
identify the optimal process configuration [10]. Therefore,
most recent work on heat integrated process synthesis
techniques [11] has concentrated on simultaneous approaches, generating both a unit sequence and heat exchanger
network together.
A simultaneous approach, however, is computationally
difficult. Superstructure approaches have been shown effective for small separation problems, of the order of three or four
distillation units for example. Larger problems require
targeted procedures such as Jacaranda. In Jacaranda, a
discrete approach has been implemented based on the
definition of virtual heat links. A virtual heat link defines a
stream at a given temperature and with a predefined flux,
which can be used to exchange heat within a process, both as a
source of heat and as a sink of heat. By careful choice of these
virtual streams, process flowsheets, which are amenable to
efficient energy use, can be identified. The difficulty is in the
choice of these streams as the problem grows exponentially
with the number of virtual streams defined. Without any a
priori knowledge of the search space, a large number of virtual
heat links would be required, which leads to an intractable
problem.
The alternative is an iterative approach. Jacaranda is
applied to the separation problem once without heat integration. Data mining techniques are then used to investigate the
potential for heat integration within the search graph
generated by Jacaranda. The results of the data-mining step
are a small set of heat links. The problem is then solved again,
now considering heat integration through the use of these
links. This iterative procedure has been shown to be effective.
However, for some problems the data-mining step is unable
to define good virtual heat links automatically. In these cases,
the user can interact with the system to suggest, based on a
visualization of the heating and cooling requirements, better
choices for the virtual heat links. This interaction has been
shown to be particularly effective for large processes [5].
3.3 Case Study III: Oil Stabilization Process
A difficulty that often arises in the modeling and optimization of chemical engineering processes is the problem of
finding feasible initial points. This is often due to the large
number of equality constraints imposed by physical property
estimation methods. Sometimes, however, this is also due to
the tight specifications imposed on product streams. Problems
with effect product specifications, are particularly difficult
due to the non-linear and possibly non-convex feasible regions
that result.
This third case study is an example of a process design
problem with product specifications based on desired properties instead of composition. Specifically, an oil stabilization
process aims to produce an oil product with a desired vapor
pressure at a given reference temperature. The composition of
such a stream cannot be described directly. The problem is also
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difficult due to the use of flash vessels as the main separation
technology and the large number of components. The problem
considered has a two-phase 12-component feed stream. A
typical process flowsheet for this problem is shown in Fig. 2.
The discrete nature of Jacaranda means that it is possible to
miss feasible solutions entirely, depending on the discretization parameters used. If the discretizations are too coarse, no
information collected is an indication of the feasible domain
for each of the processing nodes. This information provides
the user with a view of the search space and the effectiveness
of the search performed by Jacaranda. It should be noted that
focusing on the information associated with a particular
topology would, in some cases, provide limited information
about the overall search space. However, Jacaranda has the
capability of generating a ranked list of solutions instead of
just one solution, as is the case for most optimization
procedures. The data analysis step can be applied to each of
these solutions in turn so as to ensure a more comprehensive
cover of the search space in the post-synthesis analysis.
Fig. 3 shows the parallel coordinate system (PCS) representation of the data points collated. Of interest is the
information about the temperatures used for the flash units;
the temperatures have a greater effect on the feasibility of a
solution than do the pressures of the valves and throttles. The
Figure 2. Example of an oil stabilization process.
valid product streams may be found. Fine discretizations,
however, lead to untenable computational demands, both in
time and memory, due to the non-sharp nature of the
separation technology and the large number of components.
Therefore, an iterative approach is often required whereby the
user focuses in on areas of interest.
The iterative procedure is based on using Jacaranda to
attempt the problem, generating data about the search space.
This search space is then analyzed using visualization and
cluster analysis techniques, including tools such as XGobi [12]
and recent developments by Wang et al. [13], augmented with
user interaction. The information gained from this postprocessing is then used to refine the search space in Jacaranda,
limiting in particular the operation of flash vessels to narrower
ranges of temperatures.
The following is an example of this iterative procedure
applied to the oil stabilization problem. The first step is to
solve the problem using a coarse discretization with all
variables allowed to vary over their full domain. In the first
instance, the aim is to find feasible points so the objective
function is the minimization of the deviation from the desired
vapor pressure of the oil product. The result of this first step is
a feasible solution with objective function value ±3.65e8 $/y,
the annualized profit including both capital and operating
costs.
The post-synthesis data analysis processes the data generated by the search procedure and collates all the data that is
applicable to the solution actually obtained. A solution is a
sub-graph embedded in a much larger graph, which represents
the search space. The search graph will contain a number of
sub-graphs, which represent structures topologically equivalent to the best solution found. These sub-graphs are extracted
from the data collected by the search procedure and the
differences between the nodes in the graphs are analyzed. The
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Figure 3. Parallel co-ordinate system representation of initial search space for
oil stabilization problem.
operating temperatures for the first two flash units (variables
T1 and T2) are restricted to the upper half of their respective
domains. In comparison, the operating temperatures for the
second pair of flash units (variables T3 and T4) more fully
cover their domains. From this analysis, solving the problem
can be considered again, now restricting the variables to
smaller domains, as suggested by the visualization. It is
interesting to note, at this stage, that the visualization
procedures can also provide more information about the
search space than just domains of interest. For instance, Fig. 4
shows the values of the operating temperatures for the 3rd and
4th flash units for the feasible points found. A clear inverse
linear relationship is seen between these two variables, a
relationship that is also discernible in Fig. 3. This type of
relationship was previously used to target post-synthesis
optimization using stochastic optimization [13].
The second attempt can use less discrete points as each
value considered is more likely to provide useful solutions. In
fact, due to the data recording performed by Jacaranda, the
number of discrete levels used may have to be reduced to
ensure that memory requirements do not exceed the resources
available. Fig. 5 shows the PCS representation of the search
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Chem. Eng. Technol. 26 (2003) 8
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The first case study is a simple example of the application of
the user's own insight into possible alternatives, using the
automated design tool for verification and calculation. The
second case study illustrates how the design procedure can
provide information that the engineer can use to make
decisions about the search space. The third case study
demonstrates more targeted data analysis procedures, which
not only provide information about alternative solutions with
the same topology but may also highlight relationships
between the different design parameters.
The aim of the Jacaranda system is to provide the engineer
with an easy to use tool, which encourages exploration of the
different alternatives available for a specific design problem.
By enhancing the solution procedure with data collection
capabilities and by providing automated analysis and visualization procedures, the engineer can easily identify key
aspects of the search space. These aspects can then be
incorporated into the problem definition as the basis for the
iterative use of the design tools.
Figure 4. Operating temperatures for flash units 3 and 4 in initial oil stabilization
process.
Acknowledgment
The authors gratefully acknowledge support from the
Engineering and Physical Sciences Research Council of the
United Kingdom, grant number GR/M83643.
Received: April 1, 2003 [ECCE 1]
References
Figure 5. PCS representation of second attempt search space in reduced
domain.
space for the second attempt. The bounds on the variables are
based on the reduced domain identified in the first step. It is
seen that the second attempt has made full use of the search
domain for the flash unit operation temperatures. The
objective function value is now ±3.72e8 $/y, an improvement
over the first attempt.
At this point, further restriction of the search space can be
performed. Alternatively, the user can decide that the
structure obtained is suitable for more detailed analysis, fixing
the structure and optimizing design and operating parameters.
4 Discussion
Automated design procedures can provide the basis for an
interactive design procedure. The engineer can use the design
tools for exploration of the search space, guiding the software
to find better solutions. This paper has illustrated this through
three cases studies.
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