Communications
Interactivity and Automated Process
Design
ByEric S. Fraga*,Kefeng Wang, andAbdellah 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 optimiza-tion 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.
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
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
Figure 1.The design process from market identification through to initial process structure.
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the research and development teams. For instance, in chemical industries, the interaction will be between engineers and chemists. Immediately, issues about multi-disciplinarity 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 generat-ing the alternatives. Other criteria may also be used in process selection, including safety, operability, and envi-ronmental impact, depending on the information available for evaluating these criteria. The need to handle multiple criteria leads to requirements for multi-objective optimiza-tion techniques, such as pareto curve generaoptimiza-tion. Further-more, many of the criteria are based on approximate models so selection procedures may be required to work with uncertainties. One approach for dealing with uncer-tainties 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
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 computa-tional 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)
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 initial-ly, 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 flow-sheets, 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 con-straints and through the consideration of alternative separa-tion technologies such as absorpsepara-tion. 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
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 approach-es, generating both a unit sequence and heat exchanger network together.
A simultaneous approach, however, is computationally difficult. Superstructure approaches have been shown effec-tive 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 integra-tion. 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 optimiza-tion 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.
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 discretiza-tion parameters used. If the discretizadiscretiza-tions are too coarse, no
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 post-processing 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 gener-ated 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 equiva-lent 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
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) repre-sentation 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
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 3rdand 4thflash 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
Figure 2.Example of an oil stabilization process.
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.
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 visual-ization 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.
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]
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Figure 4.Operating temperatures for flash units 3 and 4 in initial oil stabilization process.
