A Pollution Reduction Methodology for Chemical Process Simulators

4128

Ind. Eng. Chem. Res. 1996, 35, 4128-4138

A Pollution Reduction Methodology for Chemical Process Simulators Subir K. Mallick,† Heriberto Cabezas,* Jane C. Bare, and Subhas K. Sikdar U.S. Environmental Protection Agency, National Risk Management Research Laboratory, Sustainable Technology Division, Systems Analysis Branch, 26 W. Martin Luther King Drive, Cincinnati, Ohio 45268

A pollution minimization methodology was developed for chemical process design using computer simulation. It is based on a pollution balance that at steady state is used to define a pollution index with units of mass of pollution per mass of products. The pollution balance has been modified by weighing the mass flowrate of each pollutant by its potential environmental impact score. This converts the mass balance into an environmental impact balance. This balance defines an impact index with units of environmental impact per mass of products. The impact index measures the potential environmental effects of process wastes. Three different schemes for chemical ranking were considered: (i) no ranking, (ii) simple ranking from 0 to 3, and (iii) ranking by a scientifically derived measure of human health and environmental effects. Use of the methodology is illustrated with two examples from the production of (i) methyl ethyl ketone and (ii) synthetic ammonia. 1. Introduction The United States Pollution Prevention Act of 1990 (42 U.S.C. §§ 13101 to 13109) states under the section on Findings and Policy [42 U.S.C. § 13101(b)] that “The Congress hereby declares it to be the national policy of the United States that pollution should be prevented or reduced at the source whenever feasible; pollution that can not be prevented should be recycled in an environmentally safe manner, whenever feasible; pollution that can not be prevented or recycled should be treated in an environmentally safe manner whenever feasible; and disposal or other release into the environment should be employed only as a last resort and should be conducted in an environmentally safe manner.” This act gave impetus to a trend toward pollution prevention that had been building for at least a decade. In fact, this act marks at the national policy level an almost complete reversal of the waste reduction hierarchy which traditionally started with disposal as the first option. This is a shift that has been occurring in both the governmental and the industrial sectors, and it has heightened interest in pollution prevention and waste reduction methodologies such as that being presented here. Pollution prevention is often an advantageous waste minimization option because of its many benefits. First, it may result in lower operating costs due to better utilization of raw materials and energy and reduced waste treatment and disposal costs. In 1991 it was estimated that hazardous waste treatment and disposal costs had risen as much as 300% over the previous decade (Clearwater and Scanlon, 1991). Although one should note that these trends can vary over time, in that same year Lederman and Weaver reported that disposal costs varied from $100 to more than $1000/ton (Lederman and Weaver, 1991). Second, pollution prevention facilitates but does not guarantee compliance with environmental laws and regulations. The reason is simply that the less pollution generated by a manufac* Author to whom all correspondence should be addressed. Telephone: 513-569-7350. Fax: 513-569-7111. E-mail address: [email protected]. † Research Fellow, Oak Ridge Institute for Science and Education.

turing facility, the fewer the problems that will likely be encountered in complying with environmental laws and regulations. Finally, pollution prevention enhances the public image of an organization, which is generally important to consumers and suppliers, the surrounding community, regulators and political decision makers, and the public. Process design is the creative activity that leads from the identification of a need to a process that satisfies that need, subject to economic, operational, safety, environmental, and other constraints. The relative importance of these constraints has varied over the years. In traditional process design, the environmental impact of a process is often initially overlooked and attention is focused primarily upon minimizing cost. This design procedure may often lead to the production of large quantities of waste materials and pollution. In such cases, it may be possible to reduce the generation of waste and pollution by modifying the design of the process. However, the appropriate design methodologies available in the literature are in their infancy, and research activities are underway in our laboratory and elsewhere (Lederman and Weber, 1991; El-Halwagi et al., 1992; Douglas, 1992; Rossiter et al., 1993; Fonyo et al., 1994; Rossiter, 1995; Manousiouthakis and Allen, 1995) to develop such methodologies. Contrasting pollution prevention or waste minimization to the end-of-pipe treatment or disposal techniques commonly used reveals that pollution prevention may have the following advantages: (i) it may be a costreduction measure in many instances, whereas end-ofpipe pollution control generally costs money, and (ii) it can be effectively implemented by applying proper process design methodologies yielding robust environmentally friendly designs with no after the fact additions. Process simulators are widely used by industries for process analysis, design, and modification. However, as mentioned before, the appropriate design methodologies to minimize waste generation need to be developed to effectively make use of process simulators. Practicing engineers in industry are greatly in need of systematic design procedures for waste minimization. At the National Risk Management Research Laboratory of the U.S. Environmental Protection Agency, a new methodology for minimizing waste generation in manu-

S0888-5885(96)00110-8 This article not subject to U.S. Copyright. Published 1996 by the American Chemical Society

Ind. Eng. Chem. Res., Vol. 35, No. 11, 1996 4129

facturing processes is being developed. Some preliminary work has already been done here in developing the method (Hilaly and Sikdar, 1994). The method is based on a generic pollution balance for chemical processes. The resulting algorithm is called the WAR (waste reduction) algorithm. The initial version of the WAR algorithm performs two main functions: (i) assessing waste generation in a process and (ii) providing options for waste reduction. The WAR algorithm, by defining the pollution index of a product for a process, provides a measure to assess the relative effects of process modifications. In this paper further modifications of the WAR algorithm are presented and specific examples utilizing these modifications are shown. In the initial version of the WAR algorithm, the environmental impact of all of the wastes was assumed to be equal. However, wastes, in particular chemicals, do not all have the same environmental impact. Various means of ranking chemical wastes according to their environmental impact and the implications of these various chemical ranking schemes are presented here. In the following sections, a brief outline of the WAR algorithm is given following the arguments of Hilaly and Sikdar (1994), then the proposed modifications are described, and, finally, the application of the algorithm is illustrated with examples. 2. Waste Reduction Algorithm Currently, there is no organizing principle for accounting for pollution generation in a process. Also, a suitable measure for comparing pollution generation in different processes or alternative modifications to a process is not available. The concept of pollution balance is used to address these issues and to develop the WAR algorithm. Following the work of Hilaly and Sikdar (1994) with some modification, a generic pollution balance equation for a process can be written as

d dt

∑k ∑i VkFkxi,k) ) ∑k ∑j ∑i Ij,kxi,j + ∑k Vk∑i riMWi ∑k ∑j ∑i Oj,kxi,j - ∑k ∑i Ekxi,k (1)

(

where Ij,k denotes the flowrate of input stream j to process unit k in units of mass/time, Oj,k denotes the flowrate of output stream j from corresponding process step k in units of mass/time, xi,j represents the concentration of waste component i from stream j in units of mass of i/total mass, xi,k represents the concentration of waste component i from process unit k in units of mass of i/total mass, Ek denotes the flowrate of fugitive emissions from the kth process step in units of mass/ time, Vk denotes the volume of the kth process step in units of volume, Fk is the average density in units of mass/volume, ri is the rate formation of a waste component i in units of moles of i/volume × time, and MWi denotes the molecular weight of component i in units of mass/moles. A waste material is defined as a nonproduct that is not used up in the process itself or in any subsequent process in a production facility. Although the pollution balance of eq 1 gives a fundamentally based accounting of the pollution associated with a process, it is still more useful to consider a balance equation that includes the environmental impact and toxicity of the component chemicals i. The goal of the assessment should be to reduce environmental

impact, and the magnitude of the environmental impact of different types of waste can vary greatly. For example, a kilogram of dioxin and a kilogram of waste paper do not have the same environmental impact. Introducing a measure of impact can be easily accomplished by weighting the mass fraction of each component i in eq 1 by a function ψi,l, where l can be either stream j or process unit k. The function ψi,l is a ranking of environmental impact with units of “impact”/ mass of i. The function ψi,l represents the impact of chemical i in stream j or process unit k. Note that it is necessary to designate the stream or process unit associated with ψi,l. The reason is that the same chemical i will have different impacts depending on the phase (vapor, liquid, or solid) and other process variables associated with the stream or process unit in which i is found. Adding the function ψi,l introduces a subtle but very important transformation into eq 1. Addition of the function ψi,l transforms eq 1 from a mass balance over pollution to a balance over environmental impact, a complex but useful transformation. The transformed balance equation is

d dt

∑k ∑i VkFkxi,kψi,k) ) ∑k ∑j ∑i Ij,kxi,jψi,j + ∑k Vk∑i riMWiψi,k - ∑k ∑j ∑i Oj,kxi,jψi,j ∑k ∑i Ekxi,kψi,k

(

(2)

When a process is under steady-state conditions, the accumulation term drops out from the pollution balance equation, and thus

∑k ∑j ∑i Ij,kxi,jψi,j + ∑k Vk∑i riMWiψi,k ) ∑k ∑j ∑i Oj,kxi,jψi,j + ∑k ∑i Ekxi,kψi,k

(3)

Often, the raw materials which enter a process are the products of several preceding processes. The raw materials, therefore, are already associated with waste formation. This contribution to the total waste formation in the process under analysis needs to be incorporated. If an input stream j of mass flowrate Ij,k into process unit k has already produced Wj,k mass of wastes of component i in fractions xi,j in the preceding processes, adding ∑∑∑Ij,kWj,kxi,jψi,j to both sides of eq 3 produces

∑k ∑j ∑i Ij,kWj,kxi,jψi,j + ∑k ∑j ∑i Ij,kxi,kψi,j + ∑k Vk∑i riMWiψi,k ) ∑k ∑j ∑i Oj,kxi,kψi,j + ∑k ∑i Ekxi,kψi,k + ∑k ∑j ∑i Ij,kWj,kxi,jψi,j

(4)

Commonly, chemical processes have several coproducts Pn (n ) 1, 2, ...). Using eq 4, a pollution index, Φ, is defined for the entire process by dividing the total environmental impact of the mass of all the wastes by the total mass of products.

4130 Ind. Eng. Chem. Res., Vol. 35, No. 11, 1996

Φ)

∑ ∑∑O

j,kxi,kψi,j

k

j

+

i

∑ ∑E x

k i,kψi,k

k

i

+

∑∑∑I k

j

Φn ) ωn( j,kWj,kxi,jψi,j

i

∑P

n

(5)

Starting with eq 5, it is possible to define a pollution index Φs,k for any stream s with mass flowrate Fs,k which is associated with any process unit k as given in eq 6.

∑n Pn

(6)

where the sum over products n includes only those products ns associated with stream s and where the term Is,kWs,k∑xi,sψi,s refers only to previously produced wastes that are associated with stream s of process unit k. Lastly, one can also define a pollution index Φk for any process unit k as shown below in eq 7.

Φk )

∑j ∑i Oj,kxi,kψi,j + Ek∑i xi,kψi,k + ∑j ∑i Ij,kWj,kxi,jψi,j ∑ n

(7)

Pn

k

where again the sum over products n includes only those products nk associated with process unit k and where the term ∑∑Ij,kWj,kxi,jψi,j refers only to previously produced wastes which are associated with process unit k. For processes where all the coproducts are generated from a single stoichiometric chemical reaction and for multiple reaction processes where the production of each unit mass of any coproduct Pn generates an approximately equal amount of waste, it is possible to allocate the wastes generated to the different coproducts according to the molar ratio of the coproducts. Then, if ωn is the fraction of the total waste associated with the production of a unit of mass of product Pn

ωn )

1 Pn

Pn/MWn

∑n

∑k ∑j Ij,kΦj,k + ∑k Vk∑i riMWiψi,k)

Φn ) ωn(

(8)

(Pn/MWn)

where for a process with a single product ω ) 1/P. For multiple reaction processes where the generation of some products produces more waste than that of the others, eq 8 is a still useful but approximate result. Note that, in principle, it is possible to track the generation of waste through a complex reaction network, but the approach is far more complex. Multiplying the right-hand side of eq 4 by ωn gives the impact of the waste produced per unit mass of product Pn under the aforementioned assumptions. This equation defines the pollution index Φn of the product n. The definition of Φn is given by

(10)

where Φj,k is the pollution index of input Ij,k and where Φj,k is defined by

Ij,k

Fs,k Φs,k )

(9)

Using eqs 4 and 6, Φn can be redefined as

n

∑i xi,kψi,s + Is,kWs,k∑i xi,sψi,s

∑k ∑j ∑i Oj,kxi,kψi,j + ∑k ∑i Ekxi,kψi,k + ∑k ∑j ∑i Ij,kWj,kxi,jψi,j)

Φj,k )

∑i xi,kψi,j + Ij,kWj,k∑i xi,jψi,j Ij,k

(11)

The use of the algorithm is illustrated in Figure 1, which is based on the work of Hilaly and Sikdar (1994). In the proposed methodology, first a flowsheet is constructed and the material balances are carried out. The pollution indices of the overall process and different streams are calculated. A process flowsheet is selected after all the alternative flowsheets are considered. A cost analysis is carried out to evaluate the economics of the selected process. If the process is not cost effective, then other process alternatives are considered. The final decision is reached after an iterative evaluation of the pollution index and cost analysis. One could optimize the process to be the least polluting, or one could find the most desirable process given all of the other constraints such as cost. In the case of an existing process which needs modification, attention is focused upon the streams with high pollution indices. Only the waste components, which exit the process, are considered for calculation of the pollution index of a stream. Based on the values of the pollution indices of the streams, the algorithm can be used to determine which unit operations need to be considered for modification. A sensitivity analysis may be conducted to determine what important variables need to be changed. Thus, the sensitivity analysis may provide options regarding measures that can be taken. After the necessary modifications are done, cost analysis may be carried out to decide on the best option. 3. Ranking the Environmental Impact of Chemicals While nearly everyone will agree that all pollutants released into the environment do not have the same impact, not everyone can agree on the methodology by which one should calculate the environmental impacts of specific pollutants. As a result, it is important to note that there are a number of tacit assumptions built into many methodologies for estimating the environmental impact of chemicals, including those being used here. Some of these assumptions are as follows: (1) Environmental impact assessments are estimates of potential hazards, but there is no assurance that the actual impacts will closely resemble the modeled impacts. (2) The actual impacts which result from releases are difficult to calculate in a precise manner, and the ability to calculate impact assessments that more closely resemble actual impacts depends upon data quality, data uncertainty, and equations used in the impact assessment. One method of improving impact assessment quality is to conduct site-specific risk assessments


ψi,l )

Figure 1. Waste reduction (WAR) algorithm.

which are among the more accurate ways of estimating environmental impacts. Unfortunately, these are very detailed, time-consuming, and data-intensive exercises, which may not be necessary for more simplified pollution prevention efforts. Fortunately, the calculation of the pollution index, i.e., its trend with pollution reduction process modifications, does not seem to be overly sensitive to the method used in estimating chemical environmental impacts at least for the case studies in this paper. This will be more extensively discussed later. It is important, however, that the user of any of these results be made fully aware of the inherent assumptions and limitations of the environmental impact methodology being used. There are many possible impacts which can be included in impact assessment, but three common broad categories are as follows: (i) environmental health impacts, (ii) human health impacts, and (iii) resource depletion (Owens and Rhodes, 1996). The categories of environmental and human health impacts are composed of subcategories which relate to more specific exposure routes and effects; for example, in human health impacts, exposure routes can be inhalation, ingestion, or skin exposure, and effects can include carcinogenicity, acute effects, or other health effects. Combining these exposure routes and effects into a chemical ranking and scoring system is a methodology which involves valueladen weighting factors. Additionally, these subcategories may lead to secondary and tertiary effects, for example, toxic chemicals ingested by fish may lead to increased fish kills (environmental effect) and/or may lead to human health impacts upon human ingestion of the fish. Global warming may result in impacts to the environment that, in turn, may impact human health. Some factors, such as ozone depletion may have primary and secondary environmental and human health impacts. For purposes of this study, an overall equation which may be used to assess the potential environmental impacts of a specific chemical may be represented as follows:

s ∑x Rxψi,l,x

(12)

where ψi,l is again the relative potential environmental impact attributed to the chemical i, l can be a stream j or a process unit k, the sum is taken over specific potential environmental impacts as detailed below, Rx is the weighting factor placed on impact x independent of specific chemical, ψi,l,xs is the specific relative potential environmental impact of type x attributed to the specific chemical i in stream j or unit k per unit mass (e.g., impact/mass). In this equation, the specific potential environmental impacts ψi,l,xs include effects such as ozone depletion potential, global warming potential, human inhalation acute health potential, human inhalation carcinogenic potential, and aquatic environmental health potential. The potential environmental impact ψi,l,xs is a function of the stream’s physical state and the environmental fate following discharge. For example, a solid waste which is disposed of in a landfill may not be considered to have a significant impact on aquatic environmental health, but it may need to be accounted for in an environmental impact factor which relates to resource depletion, where the resource is landfill space. Effects such as this are likely to be most important when a large amount of material i is being discharged. Note, however, that both ψi,l,xs and ψi,l are properties with units of impact/mass and that the amount of material influences the total estimated environmental impact when ψi,l,xs and ψi,l are multiplied by mass flowrates in eq 4; i.e., a very small ψi,l such as one would see for a relatively benign chemical times a very large flowrate can still produce a large total environmental impact. The Society of Environmental Toxicology and Chemistry (SETAC) has described five levels of sophistication and complexity for environmental impact assessment in the document “A Conceptual Framework for LifeCycle Impact Assessment” (Fava et al., 1993). These levels range from very qualitative to quantitative sitespecific risk assessment. For comparison purposes, three levels of sophistication and complexity were conducted on the base cases. In the most basic case, “No Ranking”, the chemicals were simply determined to be pollutants, and no differentiation was made between the environmental effects of various dissimilar chemicals. This is the simplest and least accurate way of determining which streams should be reduced. This method is not recommended by the authors but unfortunately is used by many people who have no more sophisticated methods to improve waste reduction. Reducing waste using this methodology may lead to increasing environmental impacts although the mass of waste may be reduced. In the second case, “Simple Numerical Ranking”, a very simplistic “guru-type” methodology was used in which the chemical components were given scores based on their expected environmental impact. Scores ranged between zero (0) and three (3) depending on their toxicity: chemicals which were judged by the authors to be highly toxic were given a score of 3; chemicals judged to be of moderate toxicity were given a score of 2; chemicals judged to be of mild toxicity were given a score of 1; and chemicals judged to have low or no toxicity were given a score of 0. Judgments were based on the limited knowledge of the authors and have no other reference. The most useful methodology for incorporating impact assessment into the WAR algorithm utilizes toxicity,


persistence, and bioaccumulation databases. The methodology adopted for this assessment was conducted by the University of Tennessee and was determined to be the most appropriate level of detail for this analysis. Further details of this methodology follow. A study conducted by the University of Tennessee provided the basis for this simplistic impact assessment by presenting an algorithm which utilized environmental and human toxic effects data combined with exposure calculations which were based on persistence and bioaccumulation data (Davis et al., 1994). This methodology did not include factors such as ozone depletion potential and global warming potential. Future more comprehensive case studies should include these and other additional factors. The Total Hazard Value was defined in the University of Tennessee methodology (Davis et al., 1994) as

Total Hazard Value ) (Human Health Effect + Environmental Effects)(Exposure Potential) (13) where Human Health Effects, Environmental Effects, and Exposure Potential (EP) are defined by

Human Health Effects ) HVoralLD50 + HVinhalationLC50 + HVcarcinogenicity + HVother (14) Environmental Effects ) HVoralLD50 + HVfishLC50 + HVfishNOEL (15) Exposure Potential (EP) ) HVBOD + HVhydrolysis + HVBCF (16) and where HVoralLD50 is the hazard value for oral acute leathality, HVinhalationLC50 is the hazard value for inhaled acute lethality, HVcarcinogenicity is the hazard value for carcinogenicity, HVother is the hazard value for other specific effects, HVfishLC50 is the hazard value for acute fish lethality, HVfishNOEL is the hazard value for chronic fish lethality, HVBOD is the hazard value for BOD halflife (persistence), HVhydrolysis is the hazard value for hydrolysis half-life (persistence), and HVBCF is the hazard value for BCF (bioaccumulation). Inserting these results into eq 12 provides the following expression for the environmental impact of chemical i in a stream or a process unit l.

ψi,l ) RoralLD50HVoralLD50EPi + RinhalationLC50HVinhalationLC50EPi + RcarcinHVcarcinEPi + RotherHVotheriEPi + RoralLD50HVoralLD50iEPi + RfishLC50HVfishLC50iEPi + RfishNOELHVfishNOELiEPi (17) In the University of Tennessee study, environmental and human health effect hazard values were added reflecting an equal weighting for each of the factors within the human health and environmental health effects to estimate the composite effects; i.e., all of the Rx’s were set to 1. For simplicity, this weighting was adopted for this study as well. Other users of this methodology may choose to weight effects differently to reflect local, personal, and/or corporate philosophies. Environmental impacts such as aquatic effects may receive greater weighting if the effluent is directly discharged into a “wild and scenic river” which is listed as a national treasure, for example. Acute human inhalation toxicity may receive a greater weighting if the facility under assessment is located in a densely

populated area. In the above equation it may appear that the term including the oral LD50 received twice the weighting of the other terms since it is located in the equation twice. This is not a result of unequal weighting but reflects the influence that this factor would have since it is an indicator of both human effects and environmental effects. Although some releases can cross media to cause additional exposures, for this case study it was assumed that inhalation effects are the result of air releases and that aquatic effects are the result of liquid water emissions. Using this assumption, several of these factors drop out if they are not significant to the stream being assessed. In this example, water releases are multiplied by water affected hazard values (i.e., the oral LD50, fish LC50, and fish NOEL); air releases are multiplied by air affected hazard values (i.e., inhalation LC50); and air plus water releases are multiplied by the carcinogenicity and “other specific effects” hazard values. Impacts are not evaluated for streams in which insignificant impacts are expected to result. Toxicity data are not consistently available for the entire range of effects for all chemicals. When data were available through the University of Tennessee study, the hazard values calculated by them were used. For those chemicals (i.e., water, hydrogen, nitrogen, argon, and methane) without hazard values in the University of Tennessee study, it was decided that a value of zero would most closely approximate the expected hazard value for these substances for the hazard categories considered in the University of Tennessee study. Future research expanding the number of chemicals with calculated hazard values and the number and quality of the hazard values is necessary to make future impact assessments more useful. 4. Case Studies To illustrate the applicability of the algorithm, two test cases are presented. The first one is the production of methyl ethyl ketone (MEK) from secondary butyl alcohol (SBA), and the second one is the production of synthetic ammonia. Both of the case studies consist of several unit processes, such as reactors, separators, dividers, mixers, etc., and represent typical chemical process engineering problems. The material and energy balance calculations have been done using the ChemCAD III (Chemstations, Inc., Houston, TX) process simulator, although any commercial simulation package can be used. Also, the base cases for both case studies have been adopted from ChemCAD III. In the following subsections the above two case studies and the results are described. As already mentioned, the incorporation of the environmental impact of different types of wastes in the calculation of the pollution index is essential. To illustrate the point, the following three different schemes for incorporating environmental impact in the methodology have been used: (i) “No Ranking”, i.e., considering that all chemicals have the same environmental impact, (ii) “Simple Numerical Ranking” of wastes from 0 to 3 according to rough impact estimates, and (iii) “University of Tennessee” ranking of wastes according to a combined index of human health and environmental effects. The three case studies shown here contrast the implications of each of these environmental impact ranking schemes. 4.1. Production of MEK from SBA. Figure 2a shows a process flow diagram for the production of


Figure 2. Production of MEK from SBA: (a) base process; (b) modified process.

methyl ethyl ketone (MEK) from secondary butyl alcohol (SBA). Typically the process consists of a SBA dehydrogenation reactor and a subsequent MEK purification system. SBA is fed to a hydrogen scrubber where the feed SBA scrubs residual MEK from the byproduct hydrogen stream. The SBA feed is then pumped up to reaction pressure and heated to reaction temperature via a feed/product heat exchanger and heater. Once at reaction temperature, the SBA is fed to the reactor system where the highly endothermic reaction takes place. Usually this reaction is carried out in several reactors in series with intermediate reheating. From the final reactor the stream is sent through the feed/ product heat exchanger where it is partially condensed.

The mixture of MEK, hydrogen, and unconverted SBA is cooled further and then sent to a separator where the hydrogen is flashed off. The hydrogen is further scrubbed and the liquid phase is fed to a MEK purification system. To see the overall environmental impact for this test case, in different ranking methods, the following numerical values (Table 1) for each chemical component in the system have been used. Figure 3 shows the result of the overall environmental impact calculations for different ranking methods. This process was modified from the base process to reduce the waste generation and consequently to see the overall environmental impact. The modified process is shown in Figure 2b. In the modified process, the bottom


Figure 3. Overall environmental impact: production of MEK from SBA. Table 1. Ranking of Chemicals in the Production of MEK from SBA no ranking

simple numerical ranking

1.0 1.0 1.0 1.0

1.0 2.0 0.0 1.0

sec-butyl alcohol methyl ethyl ketone water hydrogen

Univ. Tennessee ranking air water 1.0 20.0 0.0 0.0

0.0 3.7 0.0 0.0

Table 2. Flow Summary (kg/h) of Input Output Streams for MEK Production from SBA (a) Base Process stream 1 stream 2 stream 14 stream 13 stream 12 state

liquid

sec-butyl alcohol 3362.16 methyl ethyl 0.00 ketone water 8.17 hydrogen 0.00

gas

gas

liquid

liquid

18.72 0.01

0.94 71.31

2670.12 13.05

2.61 567.18

0.46 18.21

7.64 0.01

0.00 0.00

0.08 0.00

(b) Modified Process (50% Recycle) stream 1 stream 2 stream 14 stream 12 stream 18 state

liquid

sec-butyl alcohol 3362.16 methyl ethyl 0.00 ketone water 8.17 hydrogen 0.00

gas

gas

liquid

liquid

168.57 1.20

0.86 64.35

4.69 980.55

2123.84 10.38

1.63 28.97

6.48 0.01

0.06 0.00

0.00 0.00

(c) Modified Process (100% Recycle) stream 1

stream 2

stream 14

stream 12

state

liquid

gas

gas

liquid

sec-butyl alcohol methyl ethyl ketone water hydrogen

3362.16 0.00 8.17 0.00

1116.59 10.87 3.41 60.46

0.83 58.73 4.72 0.02

10.62 2093.71 0.05 0.00

product from the MEK finishing column, which is mostly SBA, was mixed with the feed. The flow summary of input and output streams for all three processes are given in Table 2a-c. The results of the overall environmental impact calculations for two different modifications are shown in Figure 3. In one case, 50% of the bottom product from the MEK finishing is recycled, and in the other case 100% is recycled. It is observed from Figure 3 that, for both of the modified processes, there is a substantial reduction in the overall environmental impact as measured by each of the three ranking methods. For example, installing a recycle stream with a 50% recycle reduces the pollution index by approximately 50% according to the no-ranking and

the simple numerical ranking methods and by 40% according to the University of Tennessee ranking (Davis et al., 1994). Increasing the recycle rate to 100% of the waste stream will further reduce the pollution index from the base case by 88% for the no-ranking and the simple numerical ranking method and by 55% for the University of Tennessee ranking. Note that the three ranking methods agree on the trends but not on the magnitude of the pollution reduced in each case. Last, note that using a recycle rate of 100% in a real process would likely be ill-advised because of the possibility of accumulating impurities in the system and that it is presented here for illustrative purposes only. 4.2. Production of Synthetic Ammonia. Figure 4a shows a process flow diagram for the production of synthetic ammonia from synthesis gas (nitrogen and hydrogen). This is not the only process for ammonia production but still represents a good example for purposes of this study. Ammonia syntheses are based on the overall reaction of nitrogen and hydrogen producing ammonia. The 3:1 hydrogen-nitrogen mixture is raised to high reaction pressure by means of several compressors. Various methods of temperature regulation are used to control the temperature. The product ammonia is recovered by means of refrigeration and handled as an anhydrous liquid under modest pressure. After the ammonia is removed, the remaining gases are too valuable to discard, so they are fed back to the reactor system. To regulate the concentration of inerts in the system, a fraction of the product gas is removed (purged) continuously. To see the overall environmental impact for this test case, in different ranking methods, the following numerical values (Table 3) for each chemical component in the system have been used. The results of the overall environmental impact calculations for different ranking methods are shown in Figure 5. In the base process, the purge ratio is fixed to 0.1, where the purge ratio is the ratio of flow of stream 17 to flow of stream 16. This process was modified to reduce the waste generation and consequently to estimate the potential environmental impact. Two different modifications have been studied. In the first modification, the purge ratio is decreased drastically, which eventually decreases the amount of waste. In the other modification, the purge stream is flashed and the bottom product from the flash drum is mixed with the product stream. The modified process is shown in Figure 4b. The top product from the flash drum is wasted. This reduces the amount of waste dumped. Again, this modification reduces the overall environmental impact drastically. The flow summaries of input and output streams for all three processes are given in Table 4a-c. The results of environmental impact calculations for modified cases are shown in Figure 5. For both of the modifications, there is substantial reduction in the overall environmental impact as measured by each of the ranking methods. For example, reducing the purge ratio 5-fold reduces the pollution index by approximately 67% according to the noranking, by 74% according to the simple numerical ranking method, and by 81% according to the University of Tennessee ranking (Davis et al., 1994). Reducing the purge ratio and installing a flash separator at the waste stream will further reduce the pollution index from the base case by 70% for the no-ranking, by 84% for the


a

b Figure 4. Production of ammonia from synthetic gas: (a) base process; (b) modified process. Table 3. Ranking of Chemicals in the Production of Synthetic Ammonia

nitrogen argon hydrogen methane ammonia

no ranking

simple numerical ranking

1.0 1.0 1.0 1.0 1.0

0.0 0.0 1.0 1.0 3.0

Univ. of Tennessee ranking air water 0.0 0.0 0.0 0.0 10.5

0.0 0.0 0.0 0.0 68.4

simple numerical ranking method, and by 93% for the University of Tennessee ranking. Again note that the three methodologies did agree on the trends but not on the magnitude of these reductions.

5. Discussion Figure 3 gives the overall pollution index for a process flowsheet for the manufacture of methyl ethyl ketone, and Figure 5 gives the overall pollution index for a process flowsheet for the manufacture of ammonia. The base flowsheet and two consecutive process modifications were considered for each of the two processes, and three different ranking methods for chemical environmental impact were used for each of the two processes including the base flowsheet and its two modifications. The pollution index is a relative measure that can be used to compare any two flowsheets provided the same chemical environmental impact method is used in both cases. In general, the lower the pollution index within


Figure 5. Overall environmental impact: production of ammonia from synthetic gas. Table 4. Flow Summary (kg/h) of Input Output Streams for Ammonia Production from Synthesis Gas (a) Base Process with Purge Ratio ) 0.1 stream 1

stream 19

stream 17

state

gas

liquid

gas


33334.11 603.39 7196.21 805.56 0.00

187.43 176.67 13.52 112.08 30453.99

5060.26 428.01 1120.31 699.78 3696.12

(b) Base Process with Purge Ratio ) 0.02 stream 1

stream 19

stream 17

state

gas

liquid

gas


33334.11 603.39 7196.21 805.56 0.00

217.67 404.46 16.99 351.26 38001.02

1162.77 199.60 281.06 446.82 856.21

(c) Modified Process with Purge Ratio ) 0.02 stream 1

stream 19

stream 17

state

gas

liquid

gas


33334.11 603.39 7196.21 805.56 0.00

217.67 404.46 16.99 351.26 38521.59

1162.77 199.67 281.06 447.09 335.29

a given impact method, the lower the potential environmental impact. For example, one can compare a base flowsheet to its modifications as has been done in this paper or one can compare the impact of two entirely different processes relative to each other. However, there is no valid scientific reason for the existence of a baseline value for the pollution index, and it must, therefore, always be used as a relative measure for comparing alternatives. The ranking criteria for chemicals presented in this paper provide approximate rankings for different chemicals according to their potential environmental impact for those effects that were considered. However, the list of effects is not all inclusive, and other effects should be evaluated in the future. Other more generic problems with a ranking method such as this one include the unavailability of information for many of the chemicals, including detailed toxicology data, and the incomparability of data from various sources, from various species tested, and with variations on testing method. Note, however, that the pollution reduction methodology could be used with other chemical envi-

ronmental impact methodologies as needed. Of the three ranking criteria considered, the University of Tennessee study (Davis et al., 1994) provides the most comprehensive and scientifically valid alternative. It is suggested that either this chemical ranking method, another similar method, or even a more sophisticated and comprehensive method be used to calculate the pollution indexes. In the two cases studied, the pollution index as shown in Figures 3 and 5 decreased with pollution reducing modifications to the flowsheets for all three chemical ranking methods. Although this agreement may not, in general, exist for all cases, one would expect to find consistency in the trend of the pollution index in many situations. The exception could be when one component has a potential environmental impact (ψi,l) so high that it dwarfs the contributions from all the others in the calculation of the pollution indexes (eqs 5-11). The fact that the pollution index was reduced with reasonable pollution prevention changes to the flowsheets indicates that, with some care and thought, this process design methodology can be used even if the chemical environmental impact method is approximate. It is rather important to realize that here the accuracy of the chemical environmental impact method is not as critical as in other applications. The reason is that the objective is simply to reduce the environmental impact of the flowsheet rather than to try to very accurately measure chemical impacts for regulatory or other purposes. Note, however, that quantitative environmental impact assessment is a science which is still in its infancy, that it is likely to improve with time, and that these improvements can be incorporated into this methodology as they become available. Also note that there can, of course, be legitimate discussions about the usefulness of doing any impact assessment which is not a sitespecific quantitative environmental impact assessment, but the fact remains that, without a preliminary evaluation of the possible impacts of various pollutants in the environment, it would be difficult to apply a pollution reduction methodology. It is important to bear in mind that the objective is to reduce the impact on human health and the environment and that this methodology allows the user to focus on the changes that will likely reduce the greatest environmental impacts. The uncertainty in the calculation of the pollution indexes shown in Figures 3 and 5 will vary with the method used to rank the chemicals according to environmental impact. For the first method, all chemicals are given a rank of 1 which is equivalent to no ranking. The resulting pollution index is based only on the mass flowrates from the process simulator which are calculated with high accuracy. It is suggested that this pollution index value will be accurate to at least three significant figures. For the second method, each chemical is given a rank of 0, 1, 2, or 3. This ranking is then used with the mass flowrates from the process simulator to calculate the pollution index values. The principal source of uncertainty will then be the ranking which is accurate to about one significant figure, and it is, therefore, likely that the resulting pollution index values will not be accurate to more than one significant figure. For the third method, the University of Tennessee study (Davis et al., 1994) is used. This ranks the environmental impact of chemicals according to a reasonable scientific methodology. These chemical ranks are again used with the mass flowrates from the process simulator to calculate the pollution index values. Since measuring


the environmental impacts of chemicals is a rather difficult art, it is likely that these rather than the mass flowrates will be the chief contributors to uncertainty. Further, it is difficult to accurately estimate the uncertainties involved in the measurements to perform precise propagation of error analysis. It is, therefore, likely that these pollution index values are accurate to no more than one significant figure. It is worth noting that, even under these very conservative assumptions, one can still discern significant differences in the pollution index between the base flowsheet and its modifications within any given chemical ranking method. The pollution balance methodology proposed in this paper is a convenient means of measuring wastes produced in a process and of comparing process options for a product. The method is also useful for systematically determining necessary process modifications in an existing plant. Methods of process optimization and process synthesis already exist in the literature; the presented algorithm provides an organizing principle to perform various operations systematically to achieve the objective of waste or pollution minimization. The above case studies were analyzed assuming no control devices. In reality, control devices could be added to reduce the final environmental impact. The pollution reduction methodology presented may help to minimize, but is unlikely to completely eliminate, the need for control devices. A cost analysis should also be done to determine the comparative environmental costs and financial costs associated with both the prevention and control technologies. Finally, the presented case studies should be recognized only as examples and not suggested designs for actual chemical processes. 6. Conclusions Process simulation can play a vital role in pollution prevention. At present, simulation packages in the market are inadequate to assist in designing more environmentally benign processes. These simulators need to be augmented with appropriate modules to deal with the various aspects of pollution prevention. To this purpose, the concept of an environmental impact pollution balance was introduced for minimizing pollution in various processes. A waste reduction algorithm, WAR, was formulated based on the environmental impact balance equation. The effectiveness of the WAR algorithm has been demonstrated through two illustrative examples using three separate methods for ranking the environmental impact of chemicals. The most sophisticated of these impact methodologies included data from toxicity, persistence, and bioaccumulation databases. The assessments, however, can and should be expanded and refined in the future. Although, it is worth noting that in all cases very significant reductions, ranging from 40% to 93%, were achieved in the pollution index for the process flowsheets with simple modifications. These reductions represent major decreases in the potential environmental impact of the process flowsheets. At present, the WAR algorithm and a process simulator run separately with sequential interaction between them. Future plans include incorporating the WAR algorithm into a commercial process simulator, so that the implementation of the algorithm will be more convenient. Also, future work is aimed at including more comprehensive impact assessment methodologies, incorporating process optimization schemes, and comparing with end-of-pipe pollution control technology.

Last, it has to be pointed out that the WAR algorithm offers only a systematic procedure to guide a design engineer in handling the task of pollution prevention. It does not innovate a new process but helps the innovator (a design engineer) in selecting pollution prevention measures. Acknowledgment This research is supported in part by an appointment to the Research Participation Program at the National Risk Management Research Laboratory of the U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and NRMRL/U.S. EPA. Additionally, this research is supported by in kind software (chemical engineering process simulation tool) contributions from Chemstations, Inc., under a Cooperative Research and Development Agreement (CRADA) between the U.S. Environmental Protection Agency and Chemstations, Inc. Nomenclature Ek ) flowrates of fugitive emissions from process unit k Fs ) mass flowrate of stream s in process unit k Ij,k ) flowrate of input stream j to a process unit k Oj,k ) flowrate of output stream j from a process unit k χi,j ) concentration of waste component i in stream j χi,k ) concentration of waste component i in process unit k ri ) rate of formation of a waste component i MWi ) mollecular weight of component i Vk ) volume of process unit k Fk ) average density of the components process unit k Wj ) mass of wastes produced by unit mass of Ij in the preceding process Pn ) mass of coproduct n produced Rx ) weighting factor placed on impact x independent of a specific chemical ωn ) fraction of the total pollution attributed to a unit mass Pn of a product n Φ ) pollution index for an entire process Φk ) pollution index of process unit k Φn ) pollution index of the product Pn Φj,k ) pollution index of input Ij,k Φs,k ) pollution index of a stream s in process unit k ψi,j ) index of environmental impact for component i in stream j ψi,k ) index of environmental impact for component i in process unit k ψi,l ) relative potential environmental impact attributed to specific chemical i in stream l ψi,l,xs ) specific relative potential environmental impact of type x attributed to specific chemical i in stream l EP ) exposure potential HVoralLD50 ) hazard value for oral acute lethality HVinhalationLC50 ) hazard value for inhaled acute lethality HVcarcin ) hazard value for carcinogenicity HVother ) hazard value for other specific effects HVfishLC50 ) hazard value for acute fish lethality HVfishNOEL ) hazard value for chronic fish lethality HVBOD ) hazard value for BOD half-life (persistence) HVhydrolysis ) hazard value for hydrolysis half-life (persistence) HVBCF ) hazard value for BCF (bioaccumulation)

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Received for review January 30, 1996 Revised manuscript received August 13, 1996 Accepted August 13, 1996X IE9601108 X Abstract published in Advance ACS Abstracts, October 15, 1996.

A Pollution Reduction Methodology for Chemical Process Simulators

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