Multiobjective Screening and Evaluation of Chemical Process

Ind. Eng. Chem. Res. 2001, 40, 4513-4524

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Multiobjective Screening and Evaluation of Chemical Process Technologies Volker H. Hoffmann,*,†,‡ Konrad Hungerbu 1 hler,† and Gregory J. McRae‡ Group for Safety and Environmental Technology, ETH Zurich, 8092 Zurich, Switzerland, and Chemical Engineering Department, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139

The need for integrated chemical processes that take economic and environmental issues into account simultaneously has increased considerably. In this paper a systematic method is described to generate and screen process alternatives and to identify promising technologies in early development stages. The method is based on a computerized generation of alternative ways to produce a given chemical and can identify feasible process alternatives depending on emission limits or other user input. It is proposed to evaluate the corresponding mass balances using the total annualized profit per service unit (TAPPS) and the material intensity per service unit (MIPS) as economic and environmental indicators, respectively. The method was applied to an industrial case study for the production of hydrogen cyanide. For our problem framing it could be shown that Blausaüre-Methan-Ammoniak processes are more promising than Andrussow processes according to these indicators. The most important design parameters are the type of reactor and the use of the byproducts hydrogen and ammonium salts. 1. Introduction In the design of a production process for a given chemical X, usually several chemical routes as well as a variety of technical implementations exist. One of the most important tasks of synthesis researchers and process engineers is to generate and screen chemical process alternatives and to evaluate them in order to choose the most promising one for realization. However, the notion what makes a process “most promising” depends on the objectives of the decision makers. These objectives can typically include economic profit, product quality, reliability, flexibility, safety, and environmental performance. In fact, the importance of each of the objectives to the decision makers might vary over time and is influenced by the pressure certain stakeholders are able to exert. During the past decade the pressure of environmental stakeholders has increased considerably and has led to tightened environmental regulations and higher environmental awareness in industry. This is reflected in the recent literature on chemical process design as attempts to simultaneously improve the economic and environmental performance of a process have gained a lot of interest.1 However, a major problem is the formulation of the objective functions that should be used to represent the economic and environmental objectives of the decision maker. While some authors integrate environmental issues in the form of waste treatment and waste disposal costs into an economic objective function,2,3 others propose the identification of noninferior sets using the mass of waste as an environmental indicator and profit as an economic indicator.4 † ETH Zurich. Tel: +41-1-6327145. Fax: +41-1-6321189. E-mail: [email protected] and Hungerb@ tech.chem.ethz.ch. ‡ Massachusetts Institute of Technology. Fax: +1-6172580546. E-mail: [email protected].

Special attention has been paid to approaches based on the life cycle assessment (LCA) of processes.5 In this framework not only is the environmental performance of a process itself evaluated, but also the environmental problems that arise from the production of raw materials and utilities or from the treatment of waste streams are assessed. This reflects a so-called cradle-to-gate perspective. The environmental comparison of different processes can then be based on single environmental effect categories (such as global warming or photochemical oxidation6) or on indicators that aggregate these effect classes to one performance measure.7 One of the drawbacks of the life cycle based approaches is that they require detailed inventory data. Especially during early design stages or for complex processes, the available information is usually incomplete and many parameters are uncertain. Thus, it proves difficult to conduct an LCA during the early stages of the process design. On the other hand, however, it has been recognized that decisions in early design phases are very important because they direct the further development of a process.8,9 This has led to attempts to use proxy measures to identify promising alternatives as early as possible and to avoid the development of processes that later might not be chosen for implementation.10-12 While the uncertainty whether a decision is correct is increased compared to a complete evaluation during a later stage of the process design, the benefits of a better allocation of financial resources and a shorter time to market make it worthwhile to look for such proxy measures. The goal of this research is to present a methodology for the early screening and evaluation of process alternatives under economic and environmental objectives. The aim is not to conduct a complete and detailed assessment of processes that differ only in the choice of parameter values but rather to distinguish between different technologies and to identify guiding principles

10.1021/ie001080i CCC: $20.00 © 2001 American Chemical Society Published on Web 09/21/2001

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Ind. Eng. Chem. Res., Vol. 40, No. 21, 2001

Figure 1. Three phases of process design and the selection of alternatives applying indicators.

for the preselection of alternatives. This work is focused on but not limited to the grass root design of continuous processes for one-stage syntheses. In the next section, a methodology for multiobjective screening of chemical process alternatives is explained. The total annualized profit per service unit (TAPPS) and the material intensity per service unit (MIPS) serve as indicators for the economic and environmental objectives of the decision makers. To clarify the proceeding, a case study on the production of hydrocyanic acid (HCN) is presented. Finally, conclusions are drawn, and directions for future research are discussed. 2. Methodology for Multiobjective Screening and Evaluation of Chemical Process Alternatives 2.1. Overview. The approach presented in this paper classifies the design process into three phases with different characteristics (Figure 1). During the early design phase, it has to be determined which process alternatives out of thousands of possibilities should be investigated in more detail (screening and preselection). In this context an alternative is defined as a certain combination of unit operations, whereas variations in process parameters are not understood as alternatives during the preselection phase. During the detailed design phase, the preselected process alternatives are developed further, which typically involves optimization and selection of process parameters. On the basis of this detailed evaluation of optimized alternatives, in the final selection phase it has to be decided which process to realize when strategic investment criteria (for example, potential actions of competitors or the government) are included in the analysis. In this paper a four-step method is presented which can help to standardize the screening and the selection decisions during the early design phase (Figure 2). It consists of the configuration of the decision space, the generation of alternatives, the evaluation of these alternatives, and the communication of the results to the decision makers. Realizing that process design is an ongoing task and reflecting its iterative nature, adapted versions of these four steps should be repeated during the detailed design phase. However, all further discussion in this paper, including the choice of indicators, will be focused on the early design phase only. Recommendations for the detailed design phase and the final selection phase are given elsewhere.13-15

2.2. Configuration of the Decision Space. At the beginning of the design process, the scope of the study should be defined and key assumptions should be stated, including the production target (e.g., the amount of chemical X per year with purity Y). A major decision concerns the definition of the relevant balance region and the identification of constraints such as site-specific factors. As advocated earlier, the balance region should, in principle, comprise the process itself, the production of raw materials and utilities, and the treatment of waste streams. This corresponds to a cradle-to-gate (LCA) approach and takes account of all environmental effects related to the production of the desired chemical. Another important aspect is the choice of a functional unit on which all calculations are based. While it is straightforward to find a functional unit for monoproduct plants, it has to be decided how the environmental burden is distributed when coproducts are generated. If possible, one of the coproducts should be identified as the main product and environmental credits should be assigned for the production of the other coproducts. Thus, the comparison of different processes can be simplified and the allocation of burdens can be avoided. If no main product can be identified (as might be the case for refinery productions of hydrocarbons), burdens are typically allocated according to the mass flow, energy content, or economic value of the products. Finally, the preferences of the decision makers should be clarified, and corresponding objectives should be deduced. To make these objectives operational, they have to be transformed into indicators, the choice of which should depend on the decision makers, the hierarchical level, and during the preselection phase the process characteristics. This is discussed in more detail in section 2.4. 2.3. Generation of Alternatives. For the generation of alternatives, a number of methods have been suggested, of which hierarchical approaches16-18 and mathematical programming techniques19-21 are among the most prominent. Mathematical programming techniques, frequently implemented as mixed-integer nonlinear problems (MINLP), can identify the optimal solution out of a number of alternatives. Usually, however, they suffer from the fact that very detailed unit operation models are necessary to obtain this optimal solution. This level of detail is rarely observed or required during early design phases when the screening rather than the optimization of alternatives is desired. Additionally, the decision maker might be interested in elucidating the potential differences among processes and the impact that a decision for or against a certain technology has on profitability. Because of the incomplete and uncertain information that is characteristic for early design stages, a complete screening and evaluation of all alternatives can provide much better guidance on this question than the identification of one single (optimal) alternative. Finally, in some cases the programming effort for MINLPs can be prohibitive for industrial applications. Hence, while MINLP techniques can be highly useful during the detailed optimization of alternatives, a different approach is adopted in this research. When alternatives are generated, each reaction step of a process can conceptually be subdivided into several stages such as reaction, purification, or waste treatment. For each of these stages, typically a number of different

Ind. Eng. Chem. Res., Vol. 40, No. 21, 2001 4515

Figure 2. Method for the multiobjective screening and evaluation of chemical processes.

unit operations exist for accomplishing the same process step. The identification of possible unit operations can be based on literature, patents, company experience, experiments, or other sources of information. Defining the process stages and identifying the unit operations span a tremendous network of potential process alternatives (compare Figure 5). Each technically feasible path through this network corresponds to one process alternative as defined in section 2.1. For a quick screening of even a large number of alternatives, a computer software has been developed which consists of a process database and a steering algorithm. The database has been specifically designed for the case study problem (HCN production) and holds 31 models of the most relevant unit operations including process parameters such as efficiencies and yields. These models can vary in the level of detail and in the accuracy of the calculations performed and can range from basic input-output calculations to in-depth simulation data. While each unit operation model is stored in a separate record in the database, note that unit operations that combine multiple process steps such as reactive distillations can also be included in the network. The different records in the database are interconnected with a vector of substances, M B i. This vector comprises all raw materials, utilities, and products entering or leaving the unit operation. The steering algorithm, on the other hand, follows an exhaustive search and can identify viable pathways through the network depending on the technical feasibility of a combination of unit operations. Criteria for excluding certain pathways are stored in an equipment matrix and can be entered by the user based on expert knowledge as well as on effluent concentrations or safety standards. Alternatives are completely enumerated, and for all feasible pathways, mass balances are calculated. This B out of all leads to input and output vectors M B in and M substances which are used or produced in each alternative (inventory data). Additionally, the size of the pieces of equipment j can be estimated depending on the material flows in order to calculate investment costs I(j). The number of alternatives depends on the number of process stages, unit operations, and forbidden combinations and can account for several thousands of alternatives.

The presented proceeding exhibits several advantages: First, it offers the possibility of quickly generating a substantial number of process alternatives, and the calculation of mass balances is automated. Second, by exhaustively screening all feasible alternatives, the decision maker can gain valuable insights to identify key decisions that determine the performance of the process. Efforts for research and development can then be directed accordingly. Third, the framework is flexible because it can be adjusted to the specific conditions of a company and is expandable because unit operations can easily be added to or removed from the database. Fourth, the method allows identification of unusual combinations of unit operations and can thus lead to the identification of innovative and promising processes. 2.4. Evaluation of Alternatives. After generation of alternatives, the corresponding mass balances have to be evaluated. Typical objectives a decision maker might want to cover include the economic and environmental performance, product quality, flexibility, reliability, risk, and social issues. This research focuses on the assessment of the economic and environmental performance of process alternatives. When choosing indicators to represent these objectives, the decision maker has to decide whether and to which extent he accepts aggregated indicators. If all effects are integrated into one highly aggregated measure, the ranking of different options becomes particularly easy. However, major disadvantages consist of the loss of information and the problem of determining adequate weighting factors compared to applying several indicators. The following paragraphs describe how indicators for the screening and preselection of alternatives during early design stages can be derived, but it has to be noted that these indicators are not necessarily suitable for the evaluation and selection of alternatives during the detailed design phase and the final selection phase. 2.4.1. Total Annualized Profit as the Economic Indicator. In the literature on chemical engineering economics, a large number of methods are offered on how to compare the profitability of processes.22,23 However, only indicators incorporating the time value of money into the decision seem appropriate for the comparison of processes. In the economic literature the most prominent concept for this task is the net present

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value (NPV).24 Over the lifetime of the plant T, the NPV is derived as T

NPV ) -C0 +

Ct(1 + rt)-t ∑ t)1

B t,outP Bt,out - M B t,inP Bt,in Ct ) M C0 )

∑j I(j)

(1) (2) (3)

The discount rate rt in year t commonly consists of the opportunity cost of capital and a supplement for the risk involved in undertaking the project. The cash flow Ct is calculated as material flows weighted with prices for Bt,in. products P Bt,out that can be sold and input materials P The initial investment cost C0 > 0 can be estimated with standard procedures such as Lang factors or percentages of delivered equipment cost.22 The NPV concept, however, suffers from the fact that the actual value of the NPV is not clearly related to daily business variables, and it is not as easy to interpret as variables such as cost per kilogram or profit per year. If the cash flow structure of the projects consists of one initial expenditure followed by cash flows Ct over the lifetime of the plant, the NPV can be transferred into TAPPS. TAPPS is not calculated per year but per functional unit (e.g., tons of main product) by annualizing the initial investment over the expected lifetime of the plant. This yields an annuity AN > 0, with ANF as the annuity factor.24 With S as the number of functional units, TAPPS can then be calculated.

AN ) C0/

[

]

(4)

M B outP Bout - M B inP Bin - AN S

(5)

C0 1 1 ) r r(1 + r)T ANF

TAPPSProduct )

TAPPS can be understood as the potential maximum profit per unit of product. The real profit, however, will be lower if costs for labor or overhead are included in the calculation. With TAPPS, the decision process still follows the concept of discounting, but additionally the TAPPS value is more closely linked to daily business and is thus easier to understand than traditional measures. A further advantage of TAPPS over NPV is that it is more convenient to compare with environmental indicators that are typically calculated per service unit as well. 2.4.2. Material Intensity as an Environmental Proxy Measure. While a long tradition in research on economic objective functions has resulted in wellestablished indicators, measuring environmental damages caused by chemical production processes is less obvious. LCA is a comprehensive approach describing not only the environmental effects of a production process itself but also those of upstream processes.25,26 While emission-based indicators have been constructed which consolidate environmental effects into one single number (e.g., the Eco-Indicator ’9927), the weighting between effect classes has remained somewhat subjective. Additionally, these indicators apply equivalence factors for the weighting within effect classes that claim a very high accuracy. However, when processes are preselected in early design phases, this accuracy is rarely observed

in the inventory data. Although emission-based indicators such as the Eco-Indicator ’99 can be very helpful for a detailed evaluation of complete inventory data, it might be questionable whether they are also suited for early decision making under incomplete information. This suggests the use of proxy measures for the environmental evaluation in early design phases. One of the broadest proxy measures is the MIPS, which has been constructed by acknowledging that it is a necessary condition for sustainable development to significantly reduce global material streams.28,29 It can serve as a screening indicator if the decision maker is interested in an approximating environmental evaluation in early design stages. MIPS is equivalent to the total material and energy throughput over the whole life cycle which is necessary to produce one service unit of product. Thus, MIPS is a purely input-based indicator and can be understood as a rucksack containing all material movements and energy usage a product causes over its life cycle. The MIPS value of one functional unit of a product can be calculated as

MIPSProduct )

M B inM B IPSin - M B outM B IPSout S

(6)

B IPSout represent the vectors of MIPS M B IPSin and M values of all input and output materials, respectively B IPSout are (data available from tables30). Entries in M nonzero only for byproducts for which environmental credit is given and zero for all other outputs such as emissions and the main product. Currently five categories of MIPS values are defined and calculated separately: material intensity in terms of abiotic resources, biotic resources, water, air, and earth movements in agriculture and forestry. For conventional chemical processes, only the categories of abiotic resources, water, and air apply. Although it might be useful to calculate all of them, typically the use of abiotic resources such as ores is most relevant. The material input of air is usually negligible, whereas the water category is dominated by the use of cooling water, with process water being negligible. However, because cooling water usually undergoes no contamination or reactions, it can be argued that the significance of the environmental damage caused by its use is small.31 On the other hand, process water is frequently contaminated and causes further material input during wastewater treatment. Thus, it is proposed here to adjust the normal procedure on how to calculate MIPS values by including process water in the abiotic category of MIPS and to use these adjusted abiotic MIPS values as an environmental proxy measure. While its simplicity and easy applicability are clear advantages of the MIPS concept, it has to be noted that the indicator is purely input-based and does not account for the release of substances to the environment. This can produce misleading results if a low-MIPS process with highly toxic emissions is preferred to a high-MIPS process without toxic emissions. To avoid this shortcoming, a more detailed analysis of processes with toxic emissions should be initiated and it is proposed here to supplement a MIPS-based evaluation by other indicators. Prioritized checklists or ABC classifications could serve as qualitative supplementary indicators.10 One should identify a list of critical chemicals, the use or emission of which has to be avoided. Examples include the emission of toxics to a nearby river as well as the


Figure 3. Schematic representation of a two-dimensional Pareto plot (ideal case of a smooth Pareto curve).

use of chemicals like chlorine, phosgene, etc. The entries in this list should be selected on scientific reasons but should also reflect the interest of company-specific stakeholders. Additionally, in some cases also quantitative measures can be applied that consist of selected emission data depending on the type of production process. For example, during fine chemical production, solvents are frequently used in considerable quantities, and the amount of volatile organic carbon (VOC) emissions could be an additional indicator supplementing MIPS to exhibit a broader picture of possible environmental damages. Besides applying these additional indicators during the early screening phase, the decision maker should also perform a full LCA during the detailed process design phase. This would include the use of emissionbased environmental indicators. Elsewhere, suggestions are given as to which indicators to choose at that stage and how to include uncertainty in the analysis.13,14 2.5. Communication of Results Using Pareto Plots. During the evaluation step, values for TAPPS and MIPS are assigned to each process alternative. While the results of multiobjective problems are often aggregated into one number and processes are ranked according to that number, it is suggested here to communicate the results to the decision makers in a two-dimensional Pareto plot as shown in Figure 3. As discussed earlier, processes characterized by high TAPPS and low MIPS values are most desirable. In Figure 3, for example, process A dominates process C because it is better in both dimensions. However, no relation of dominance can be established between processes A and B because each of them is better than the other in one dimension but worse in the other dimension. In fact, in the ideal case all nondominated processes form a so-called Pareto curve (noninferior set) that represents the best technological options. Solutions above the curve are infeasible at the current technological level, while processes below the curve are inferior. In reality, though, the form of the curve might vary significantly; e.g., it can just consist of one optimal point that dominates all other solutions. This graphic representation serves two purposes: On the one hand, it shows the position of all alternatives with respect to both objective functions. Each of these

alternatives is characterized by a “fingerprint” code, which contains the information of which unit operations the process consists. The fingerprint can be analyzed in order to find patterns in the positions of the points and helps to identify key decisions during process development that determine the performance of a process. This is of considerable importance because decisions made during early design phases strongly influence how the process is further developed and inferior decisions can only be corrected with great financial effort. This aspect will be explained in more detail in section 3.5. On the other hand, usually several decision makers are involved in the selection of a process alternative. In many cases, these decision makers exhibit different sets of values and prefer different objective functions. The computer tool can be used for “online” variations of the objective functions in order to show the effects of different preferences. Additionally, the influence of parameter variations can be identified quickly when database entries are changed. Consequently, the Pareto plot can be used as a communication tool enabling the decision makers to discuss the implications of their decisions and thus aids in coming to more reliable decisions. It has to be noted, however, that it is an inherent problem of using preliminary indicators not to include inferior processes in and not to exclude good processes from further development stages. The quality of this selection is limited by the selection of unit operations included in the study, the choice of objective functions, and the underlying calculations and estimations. To improve the selection process, it is suggested to run sensitivity analyses to detect the influence of changes in parameter values. Additionally, using several indicators for the environmental performance can help to capture a wider range of effects, which might prohibit the further development of a process. Thus, the robustness of a selection decision can be improved. However, it has to be realized that the selection decision depends to some extent on the subjective preferences and value judgments of the decision maker regarding the tradeoffs between economic and environmental objectives and between old and new technologies. Ultimately, the selection decision implies a tradeoff between, on the one hand, the risk to invest in innovative technologies that exhibit a high potential as well as a risk of failure and, on the other hand, the certainty to restrict design efforts to standard technologies with limited but well-known performance. 3. Case Study: Industrial Production of HCN 3.1. Overview: Characteristics of HCN. To demonstrate the method, a case study has been developed for the production of HCN in close cooperation with several European manufacturers. HCN has been chosen because of its industrial relevance as a raw material for the production of adiponitrile, methyl methacrylate, and others32 and because process data and information is readily available in the open literature. Currently, there are different technologies applied in industry, each of them exhibiting different advantages and disadvantages. 3.2. Configuration of the Decision Space. The scope of the case study is the preselection of alternative processes for the production of 50 kton/year of HCN with a purity of 99.5%. Major constraints include the emis-

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Figure 4. General flowsheet for the production of HCN showing nine principal process steps.

sion of HCN in wastewater (10 ppm according to Swiss legislation) and safety hazards associated with the polymerization of HCN under basic conditions. It is assumed that the usual facilities such as steam, cooling water, fuel gas, and electricity are provided on site. The decision maker is considered to be interested in maximizing the economic profit of the production process while minimizing its environmental impact. One ton of HCN (99.5%) is chosen to be the functional unit, and environmental credits will be given to byproducts, if possible. 3.3. Generation of Alternatives. The production of HCN can generally be described as a nine-level procedure33,34 as shown in Figure 4. Most industrial processes use ammonia and methane as raw materials for the reaction (level 1). Unreacted ammonia is removed (2/ 3), and the gas mixture is fed into an absorber in which HCN is absorbed in water (4). The HCN/water mixture is distilled and produces HCN at the desired purity (4) as well as wastewater that has to be treated (9). The off-gases from the HCN absorber can be led through an NaOH absorber to produce NaCN (5) and through a pressure swing adsorption or a methanation unit to generate hydrogen (6) while the remaining off-gases are combusted (8). Depending on the way the excess ammonia is removed (2/3), it can be used to produce ammonia salts (7) or can be recycled to the reactor. A unit operations network was constructed (Figure 5), and the corresponding unit operations were modeled and stored in the process database. The relevant information was, in part, taken from the open literature and patent sources and, in part, provided by experts from HCN manufacturing companies. Five different reactors were considered: The BMA reactor consists of a large number of tubes coated with a layer of Pt catalyst. Inside the tubes HCN is produced according to the endothermic reaction (R1), while heat is provided by the combustion of fuel gas outside of the tubes.35 The

CH4 + NH3 ) HCN + 3H2 (∆HR ) 252 kJ/mol) (R1) Andrussow reactor, on the other hand, generates HCN according to the exothermic reaction (R2) on a Pt gauze catalyst in one single tube.36 While the Andrussow

reactor is operated with air as the source of oxygen, it is also possible to use oxygen-enriched air instead37 (labeled And/O2 in Figure 5). In the Fluohmic reactor,

CH4 + NH3 + 1.5O2 ) HCN + 3H2O (∆HR ) -474 kJ/mol) (R2) propane reacts with ammonia according to eq R3 in an electrically heated fluidized bed of activated carbon.38

C3H8 + 3NH3 ) 3HCN + 7H2 (∆HR ) 634 kJ/mol) (R3) For comparison, an ideal BMA reactor with 100% yield was also taken into consideration. Note that in each of the different reactor types it is not possible to reach complete conversion because the feed substances partly decompose to the elements and partly remain unreacted. This necessitates several purification steps. In the second process step, unreacted ammonia has to be removed from the gas mixture in order to avoid polymerization of HCN. The following five unit operations were modeled: Absorption of the gas mixture with sulfuric acid or nitric acid with formation of the corresponding ammonium salts,39 recycling of ammonia either with phosphoric acid40 or zeolites,41 and the donothing option when no ammonia is present in the gas mixture. To isolate HCN from the gas mixture, three different absorption/distillation units were taken into consideration. While all of them operate at ambient pressure, they differ in the amount of HCN that remains in the off-gas stream after the absorption column. Additionally, a separation by cooling was computed in order to identify the performance of a hypothetical ideal process. Depending on how much HCN is left in the gas stream leaving the absorber, it can be worthwhile to install an NaOH absorber in which HCN is converted into sellable NaCN. The remaining gas stream can be used as a fuel gas in the case of the BMA reactor, to produce pure hydrogen in a pressure swing adsorption, or be fed into a methanation unit. The composition after


Figure 5. Unit operations network for generating alternative pathways: 72 000 potential alternatives and 1244 feasible alternatives.

that process step determines whether the gas has to be combusted or can be released to the environment if legal constraints are met. Depending on the method of ammonia removal in process step 2, the ammonia can be regenerated or the ammonia salt can be sold in liquid or solid form or can be landfilled. Regarding the wastewater treatment, the most common alternatives include HCN removal with chlorine or sodium hypochlorite, but besides ferrous sulfate treatment, recently other methods such as ozone, air, or hydrogen peroxide treatment have become available.42 The resulting network is shown in Figure 5. To limit the computational effort, certain pathways through the network were excluded according to the following considerations: Unit operations for removing ammonia from the reaction gases are only necessary if the amount of ammonia is above several hundred ppm. For safety reasons, small sulfuric acid absorbers are considered necessary in the case of ammonia recycling with zeolites to guarantee the complete removal of ammonia and to prevent polymerization. Isolation of HCN by cooling is only possible if no water is present. Using the off-gases for heating the reactor is only feasible in the case of the BMA reactor, and it is only allowed to release the off-gases to the environment if emission limits on HCN and H2 are met. Installing a methanation unit is only considered if CO is present in the gases and if the hydrogen concentration is high. Omitting the wastewater treatment stage is only possible if no wastewater is emitted from the HCN-isolation stage or if ammonium sulfate or nitrate is sold in aqueous solution. When these exclusions were taken into account, it was possible to reduce the number of pathways from 72 000 to 1244 and the corresponding mass balances were generated. 3.4. Evaluation of Alternatives. For calculation of the values for TAPPS for each of the mass balances, the

Table 1. Economic and Environmental Data for Selected Substances substance

price

MIPS

methane ammonia hydrogen cyanide sulfuric acid platinum steam electricity

0.14 $/kg 0.175 $/kg 1.2 $/kg 0.08 $/kg 14 000 $/kg 0.007 $/kg 0.04 $/kW‚h

1.2 kg/kg 3.6 kg/kg n/a 0.5 kg/kg 3.2 × 108 kg/kg 0.4 kg/kg 4 kg/kW‚h

relevant prices for products and raw materials43,44 and utilities23 were taken from the open literature. Information of investment costs was based on data from industry and calculated by applying the capacity ratio method, Lang factors, and the Marshall and Swift equipment cost index.22 The plant lifetime was estimated to be 20 years, and the discount rate was set to 15%. MIPS values were computed using the MIPS factors supplied by the Wuppertal Institute,30 and environmental credit was given for production of hydrogen, sodium cyanide, ammonium nitrate, and steam. However, no credits were assigned to the production of ammonium sulfate because it is mostly considered as waste and rarely produced on purpose. Typical values for prices and MIPS values of some substances can be found in Table 1. Additionally a checklist was built to detect effects that are not incorporated in the MIPS evaluation. The list was comprised of the categories critical outputs, critical inputs, and other considerations. Outputs that could make a further investigation necessary include HCN emissions in general, nitric oxides from the reactors and the combustion units, ammonium sulfate from the crystallization unit, and heavy metals, chlorinated hydrocarbons, and ferrous sulfates from the wastewater treatment unit. Chlorine and sodium hypochlorite were

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Figure 6. Pareto plot showing all feasible alternatives. Four process families depending on the choice of reactor technology.

Figure 7. Pareto plot for all BMA processes (detail of Figure 6). Three branches depending on the use of off-gases.

considered to be potentially the most critical inputs. Other considerations regard the safe operation of the plant with potential hazards evolving from crystallization of ammonium nitrate, using flammable gas mixtures to feed the reactor, and from the polymerization of HCN under basic conditions. 3.5. Communication of Results Using Pareto Plots. To communicate the results to the decision makers, a series of Pareto plots were drawn (Figures 6-10). This allowed identification of the “best” processes as well as the key decisions that determine the performance of a process. Figure 6 displays a plot of all 1244 feasible alternatives. It can be seen that no smooth Pareto curve exists but that clusters, or process families, are formed depending on the type of reactor. Accordingly, the choice of a reaction technology is the first and most important key decision to be made because it affects all subsequent unit operations. This finding reflects the differences in the underlying chemistry or, more precisely, in the yields on the raw materials and emphasizes the prime importance of choosing the “right” chemical pathways. The family of ideal processes is economically clearly better than all other families but environmentally only

slightly better than the BMA processes. The BMA family dominates the Andrussow family environmentally and is also economically preferable for certain pathways. The family of Fluohmic processes exhibits a very high material intensity due to the consumption of electricity (high MIPS value) and is also economically not preferable to the BMA process or the Andrussow process. Therefore, it was omitted from the further analysis, and only the BMA and Andrussow families were explored in more detail. It should be noted that the environmental performance within the Andrussow family varies considerably compared to those of the other families, as does the economic performance of the BMA family. Additionally, it is interesting to observe that Pareto curves exist within all families; i.e., once a process family has been chosen by determining a reactor type, there is no single dominant alternative, but tradeoffs have to be made between the economic and environmental performance of the alternatives. Also note that the usual practice of applying standard costing tools such as NPV for comparing processes would also favor the BMA process over Andrussow and Fluohmic processes. Choosing a measure of environmental performance as a second objective function,


Figure 8. Pareto plot for all BMA processes with H2 production and crystallization (detail of Figure 7). Technological advancements leading to a “win-win situation”.

Figure 9. Pareto plot for all Andrussow processes (detail of Figure 6). Two areas depending on the use of off-gases.

however, adds significant information, which can be used to evaluate and improve the processes. Figure 7 illustrates the evaluation of about 550 BMA processes and elucidates that the BMA process family is split into several branches. The key decision determining the performance of the branches is the use of the hydrogen-rich off-gases (unit operation level 6 in Figure 5). The methanation of the gas is dominated by the use as a fuel gas for the reactor and by the combustion. While the production of hydrogen is more profitable than all other alternatives, its environmental performance is relatively poor because of the extensive use of electricity in the compressors of the pressure swing adsorption unit. One of these branches, the set of all BMA processes producing hydrogen, is depicted in Figure 8 (only processes with crystallization of ammonium salts are

shown). It can be seen that the performance of the processes varies depending on which technology is chosen for removing ammonia from the reaction gases. During the past decades, technology has advanced from absorbing ammonia in sulfuric acid to regenerating it using phosphoric acid or zeolites, while using nitric acid as an absorbent has remained problematic because of hazards evolving from the formation and potential explosion of ammonium nitrate salts (see checklist in section 3.4). Figure 8 illustrates that the development of these new technologies implies a “win-win situation” in which both objective functions are improved at the same time. The curves in the figure show the approximate position of a set of Pareto alternatives that result from varying the operating parameters of the absorption/distillation units. In fact, the upper right point within each technology corresponds to generating

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Figure 10. Pareto plot for all Andrussow processes with combustion and crystallization (detail of Figure 9). Technological advancement leading to a “win-lose situation”.

significant amounts of NaCN as a byproduct, the desirability of which depends on the market conditions. In contrast to that, the lower right points result from operating the absorber at minimal losses of 1 ppm of HCN in the off-gases. Accordingly, a byproduct strategy should be chosen for NaCN, if it can be sold in corresponding quantities. Figure 8 also indicates that the application of different wastewater treatment technologies does not affect either of the objective functions and leads to eight almost identical process evaluations that cannot be distinguished at this point of the analysis. Thus, choosing a wastewater treatment option is not relevant at this stage of the process design and should be excluded from the analysis until emission-based environmental indicators are applied. This also reflects the fact that several inputs and outputs of the wastewater treatment units were considered to be potentially critical when building the checklist for effects not covered by MIPS. When the Andrussow process family is analyzed, similar conclusions can be drawn. As Figure 9 shows, the use of the off-gases is again a key decision, although the differences in the performances of the branches are smaller in this case. Figure 10 compares the performance of the traditional Andrussow reactor with the oxygen-enriched reactor (off-gases are combusted, and ammonium salts are crystallized). It can be seen that changing the source of oxygen from air to oxygen-enriched air slightly increases the economic profits measured as TAPPS while worsening the environmental performance considerably (MIPS values increase because of the high electricity consumption for the upstream production of oxygen). In contrast to this technological advancement that leads to a “winlose situation”, within both the air process and the oxygen-enriched process a win-win situation exists on how to remove ammonia (as explained for the BMA process in Figure 8). If the decision maker is interested in choosing an Andrussow process, however, a closer investigation should be performed on how the use of oxygen-enriched air affects the risk profile of the processes (see checklist).

4. Conclusions and Outlook In this paper a systematic method has been presented for the generation, screening, and evaluation of chemical process technologies, when economic and environmental objectives are pursued. For generation of alternatives, a computer software was built that allows one to quickly calculate a large number of mass balances while being easily expandable to integrate different unit operation models. The software is also adjustable because it can evaluate process alternatives according to different indicators, depending on what the objectives and hence the preferences of the decision makers are. It has been advocated that TAPPS and MIPS can serve as indicators when estimating the performance of processes based on incomplete data in early design stages. However, because MIPS is a purely input-based indicator, it should be accompanied by additional measures such as checklists to prevent a low-MIPS process with highly toxic emissions from being preferred to a nontoxic high-MIPS process without further investigations. With MIPS and TAPPS as indicators, the method can be used to illustrate the location of different technological options in a two-dimensional Pareto plot, and the key decisions of major importance for the performance of a process can be determined. The methodology has been applied to a case study on the production of HCN. When about 1250 alternatives were evaluated according to the indicators presented here, clusters or families of processes were formed. The location of the clusters was found to depend on key decisions regarding, in the order of decreasing importance, the choice of the reactor, the use of hydrogenrich off-gases, and the technology of ammonia removal. Making the right decisions regarding these key issues proves to be much more important than, for example, specifying the operating conditions of the purification system. The question as to which alternative to choose for further development is also influenced by decisions of strategic relevance such as whether a company needs pure hydrogen for downstream uses or which business channels exist to sell ammonium salts. Generally speaking, for our problem framing the BMA process family


seems to be more promising than the Andrussow family with respect to TAPPS and MIPS and should, according to these proxy measures, be preferred for further development during the detailed engineering phase. The method and the results presented so far are based on deterministic values for process parameters and weighting factors. To avoid the corresponding limitations, future research should try to integrate uncertainty into the decision process. This could be done by first analyzing the cost structure of the alternatives, running sensitivity analyses on key parameters, and performing an uncertainty analysis with Monte Carlo simulations. Currently, a project is underway in which different flowsheet models for the BMA and Andrussow processes are developed and the effects of including parameter uncertainty are studied. In a next step, it should be clarified to which extent the results depend on the specific problem context such as availability of utilities or on strategic issues such as the potential market for byproducts. This could provide valuable insights for clarifying in which way it is possible and meaningful to apply the concept of best available technologies to chemical processes. To come to a decision as to which HCN process to install, the most promising alternatives of the early design phase should be investigated in more detail during later design phases, and a set of new indicators that are adequate for these phases should be identified. Acknowledgment The authors are very grateful for innumerous contributions to the case study provided by Dr. Gerhard Holze, Lonza Group, and for many helpful discussions with partners at Degussa AG, BASF AG, and DuPont Corp. They also thank Dr. Ulrich Fischer for valuable comments on earlier drafts of this paper and two anonymous reviewers for suggesting a number of important improvements. This research was in part sponsored by the Alliance for Global Sustainability, AGS. Literature Cited (1) Cano-Ruiz, J. A.; McRae, G. J. Environmentally Conscious Chemical Process Design. Annu. Rev. Energy Environ. 1998, 23, 499-536. (2) van der Helm, D. U.; High, K. A. Waste Minimization by Process Modification. Environ. Prog. 1996, 15 (1), 56-61. (3) Dantus, M. M.; High, K. A. Economic Evaluation for the Retrofit of Chemical Processes through Waste Minimization and Process Integration. Ind. Eng. Chem. Res. 1996, 35, 4566-4578. (4) Ciric, A. R.; Huchette, S. G. Multiobjective Optimization Approach to Sensitivity Analysis: Waste Treatment Costs in Discrete Process Synthesis and Optimization Problems. Ind. Eng. Chem. Res. 1993, 32, 2636-2646. (5) Azapagic, A. Life cycle assessment and its application to process selection, design and optimization. Chem. Eng. J. 1999, 73, 1-21. (6) Pistikopoulos, E. N.; Stefanis, S. K.; Livingston, A. G. A Methodology for Minimum Environmental Impact Analysis. AIChE Symp. Ser. 1995, 90, 303, 139-151. (7) Kniel, G. E.; Delmarco, K.; Petrie, J. G. Life Cycle Assessment Applied to Process Design: Environmental and Economic Analysis and Optimization of a Nitric Acid Plant. Environ. Prog. 1996, 15 (4), 221-228. (8) Spriggs, H. D. Design for Pollution Control: Screening Alternative Technologies. Environ. Prog. 1996, 15 (2), 69-72. (9) Heinzle, E.; Hungerbu¨hler, K. Integrated Process Development: The Key to Future Production of Chemicals. Chimia 1997, 51, 176-183.

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Received for review December 18, 2000 Accepted July 3, 2001 IE001080I

Multiobjective Screening and Evaluation of Chemical Process

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