Pollution Prevention through Solvent Selection and Waste Minimization

To prevent pollution in chemical manufacturing processes, concepts respectively for solvent selection and waste minimization have been developed and ...
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Ind. Eng. Chem. Res. 2002, 41, 4534-4542

Pollution Prevention through Solvent Selection and Waste Minimization Ulrike Krewer* and Marcel A. Liauw Lehrstuhl fu¨ r Technische Chemie I, University of Erlangen-Nuremberg, 91058 Erlangen, Germany

M. Ramakrishna, M. Hari Babu, and K. V. Raghavan Indian Institute of Chemical Technology, Uppal Road, Hyderabad 500007, India

To prevent pollution in chemical manufacturing processes, concepts respectively for solvent selection and waste minimization have been developed and improved. The first concept selects solvents for a given process that exhibit good environmental behavior in addition to good performance. For the performance test, relative and absolute solubility calculations of solventsolute combinations are performed, and reactivity is estimated. The acceptable solvents are tested for important environmental characteristics such as global warming, ozone depletion, risk of fire and explosion, BOD5, and toxicity. Estimations of the risk of fire and explosion were made by implementing a model for solvent storage in the calculation of Dow’s F&E index. The second concept helps minimize waste during manufacturing. Steinbach’s concept of the balance yield BA is slightly varied and set into software. Both concepts are applied via software to manufacturing processes for NMSM. They identify the solvent DMSO as the most suitable solvent for the new process and highlight the most productive process, as well as the weak points of each process. 1. Introduction Most branches of the chemical process industries reacted to environmental regulations enacted by governments with the application of “end-of-pipe” technologies to their processes to get them to fall below pollution thresholds. Today, these technologies are very widespread, common, and elaborated. Applying end-of-pipe technologies to lower environmental pollution has disadvantages and is not sustainable. Instead of preventing waste or pollution, this method usually leads the pollution, which previously was spread over the industries’ surroundings, to single locations, where the harmful material is then concentrated. Energy, additional material, and effort have to be invested in these procedures, leading to a rise in capital expenditures, whereas at the same time, other waste is produced, e.g., highly toxic filters, chemicals for water treatment, and the global-warming gas CO2 through energy consumption. It is obvious that a better approach to improving environmental conditions and reducing capital expenditures is to reduce waste and energy consumption through prevention, i.e., to avoid producing substances that have to be removed in subsequent processes. Environmental impact assessment (EIA) in general is used to estimate and evaluate the impact of development on the environment, including the environmental issues in the form of an ecological impact assessment (EcIA), as well as social and economic issues.1 In this work, we concentrate on the ecological aspects, i.e., the EcIA. Small and middle-sized companies are known to * To whom correspondence should be addressed. Present address: Max-Planck-Institute for Dynamics of Complex Technical Systems. Phone: +49-391-6110-390. Fax: +49-3916110-593. E-mail: [email protected].

be much less optimized toward pollution prevention than big companies. The reason for this is that the former have a less automated and less expensive production than the latter. For England’s East Midlands, Phillips et al. report significant barriers to the institution of waste minimization programs especially from small companies.2 Similar observations of the great environmental impacts of small companies have been made, for example, for China or Brazil.3,4 Obviously, scientific pollution prevention procedures must be developed especially for this sector. Modularly structured concepts promise to show an improved acceptance in this sector. In contrast to detailed (and complex) environmental assessment methods such as life cycle assessments (LCAs; see other articles in this issue), the structure of environmental assessment methods for small companies should be reduced to the essential features. In this paper, we present two modularly structured concepts containing sophisticated theories for solvent selection and waste minimization. With them, chemical process industries and related research institutes can examine processes in development or production for their degree of optimization toward pollution prevention. For a convenient application, the corresponding software requires only a few basic input data. For concepts focusing less on the ecological aspects of production and chemistry itself but instead concentrating on ecological and economic aspects of process design, we refer to other articles in this issue. 1.1. Case Study: Production of NMSM. To demonstrate the two methods and their effects, we apply them to an existing process. Because the production of pharmaceuticals is known for its excessive consumption of raw materials, especially solvents, during manufacturing, which arises from the complicated approaches involved, we chose the production of the intermediate

10.1021/ie020037n CCC: $22.00 © 2002 American Chemical Society Published on Web 07/10/2002

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NMSM (1-methylamino-1-methylthio-2-nitroethylene) for the drug Ranatidine. Ranatidine is known as a histamine blocker or an H2-receptor blocker and is used to treat ulcers and heartburn. It is produced on a small scale in batches and uses the following chemical reactions

CH3NO2 + CS2 + 2KOH f (KS)2CdCHNO2 + 2H2O (1) (KS)2CdCHNO2 + CH3NH2 + (CH3O)2SO2 + H2O f CH3OH + H2S + K2SO4 + C4H8N2SO2 (2) Because this chemical path is very material-consuming, the Indian Institute of Chemical Technology in cooperation with the manufacturing company developed a completely different chemical reaction to obtain the drug intermediate

CH3NCS + CH3NO2 + KOH f O2NCHdC(SK)(NHCH3) + H2O (3) O2NCHdC(SK)(NHCH3) + (CH3O)2SO2 f C4H8N2SO2 + KCH3SO4 (4) Laboratory-scale experiments of this process showed an increase in productivity. For application of the solvent selection concept to these processes, material and basic process data are needed. A further requirement is the setup of a solvent database containing necessary solvent material data for which we developed a small, representative, and extensible database that includes these data. The solvents chosen are among the most frequently used solvents, and material data have been obtained from refs 5 and 6. These solvents belong to different chemical classes, e.g., organic, inorganic, alcoholic, and chlorinated, so that the most important effects such as nucleophilicity or electrophilicity are covered. Certainly, the solvents are not the best choices from an environmental point of view because of their well-known hazardous effects, and innovative new solvents would make better selections as solvents for improving processes. However, for the sake of both demonstrating the impact of the economically most important solvents and proving the concept, we decided to analyze and present explicitly these solvents. The chosen solvents are acetone, benzene, N-butyl acetate, 1-butanol, 1,2-dichloroethane, dimethyl sulfoxide, ethanol, 2-ethylhexanol, hexane, methanol, methylethyl ketone, 2-propanol, propylene glycol, toluene, 1,1,1-trichloroethane, water, and xylene (equimolar mixture of the ortho, meta, and para isomers). 2. Solvent Selection Spent solvents constitute a major source of pollution in the chemical and pharmaceutical industries because, in many processes, the solvents represent a large portion of the total material input and are dumped after use. Furthermore, they represent a potential harm within the manufacturing section because they can be released into the environment through leakage or explosion, subsequently causing damage there. Various efforts in designing more benign solvents with performance properties similar to those of the solvents in use have been conducted. Gani and Brignole7 generated

molecular structures in using combinatorial partition strategies prior to UNIFAC calculations and feasability studies for extraction purposes. Another methodology developed by Gani and co-workers8 presents the combination of molecular modeling techniques with traditional computer-aided molecular design. Marcoulaki and Kokossis9 developed stochastic optimization algorithms with molecular vectors and physical properties as restrictions, whereas Sinha et al.10 used the structure of computer-aided molecular design for surface cleaning applications. Sherman et al.11 discussed solvent selection and design methods from a more environmental point of view and evaluated computational methods as well as solvent databases. Cabezas et al.12 developed Windows-based commercial software (Paris I and II), which not only designs solvent mixtures but also searches for solvents that are already available on the market. From an economic point of view, this strategy is preferable to pure designing. In contrast to all of the efforts mentioned previously, Joback developed a concept in ref 13 that does not take into account properties of the solvent in use. He selects the best-performing and most environmentally benign solvents for extraction purposes based on properties of the solutes and various constraints. The solubility calculations in his methodology represent a more quantitative approach, using Hansen solubility parameters instead of UNIFAC methods. Developing on these ideas, our method for solvent selection is applicable also to other solvent functions, e.g., solvents as reaction media; it is suitable for software implementation, hence automatation; and it concentrates on environmental aspects in addition to performance. The method is clearly structured in two parts: a performance test for examining the suitability of solvents for the process and a test for estimating the environmental hazards of the solvent. 2.1. Performance Test. 2.1.1. Theory. Two main points are considered to obtain good solvent performance in a process: the process temperature and the choice of necessary solvent properties according to the solvent function. Whereas the first point is a necessary condition for any chemical reaction, the second can vary strongly in the concerned constraints and their grade of importance. A total score SXT reflecting the solvent properties X is therefore calcuand composed of single scores Ci,n lated for each solvent, and all solvents are ranked according to this parameter. 1. Temperature. Because solvents are usually used as liquids, it holds for a solvent that

TSolidification < TProcess

(5)

TBoiling > TProcess

(6)

In the case of solvents supposed to work above or at their boiling points, e.g., for evaporative cooling, the condition in eq 6 changes to

TBoiling e TProcess

(7)

2. Solvent Function. Solvents fulfill various tasks: there are solvents for extraction, refrigeration, or heattransfer purposes; solvents as reaction media; and solvents for many more purposes. For each task, the solvent needs special properties, e.g., a high solubility for a solute or a low boiling point to enhance regeneration after extraction. The three most important func-

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tions for solvents in the chemical manufacturing industry involve their use as reaction, extraction, and heattransfer media. We concentrate on the first function, reaction medium, because it is the most typical one for the chemical process industry. Many reactions require the help of a solvent, either to dissolve solid reactants or to dilute already dissolved reactants. Important constraints are therefore a high solubility for reactants and a low reactivity of the solvent, so that it does not react with other substances such as reactants and catalysts. With the existence of various solubility estimation methods, it is possible to estimate the solubility of one substance in another without conducting laboratory experiments and to consider many more solvents than those typically used. Furthermore, we cannot dispense with solubility estimation methods because it is not possible to automate solubility estimation through laboratory data. Frank et al.14 give an overview over existing estimation methods and their characteristics. Hansen’s radius of interaction method is used here, because the parameters are easily accessible and calculation can be easily done. It is based on regular solution theory. Hansen’s method can be applied even to very complicated molecule structures (see Table 4), whereas the UNIFAC method shows reasonably accurate results only if interaction parameters can be interpolated from data for similar systems involving the same functional group pairings. The structures of more complicated molecules, as are found in organic chemistry, pose particularly strong problems for UNIFAC because of the absence of binary interaction parameters. Three different physical effects are included via factors in Hansen’s solubility calculation: (1) dispersive forces via δd (MPa1/2), (2) polar forces via δp (MPa1/2), and (3) hydrogen-bonding forces via δh (MPa1/2). The closer the solubility factors of one substance to those of another, the higher the solubility. The distance between solubility parameters can be measured using a Euclidean distance metric. For two molecules A and B, the distance R between their solubility parameters is given by

R(A,B) ≡ x(δAd - δBd )2 + (δAp - δBp )2 + (δAh - δBh )2 (8) Various methods for determining solubility parameters are given in ref 15; they are based on either molecular physical properties or structural contributions. The latter method is suitable for the automatation of solubility calculations, and the user will not have difficulties in searching for special molar physical properties. With the help of Table 1, the factors δZd , δZp , and δZh for a substance Z can be calculated according to this method as follows

δZd )

δZp )

δZh

)

Z Fd,i ∑ Z i)1

x x 1

-

Fd [(J cm3)1/2/mol]

Fp [(J cm3)1/2/mol]

-Uh [J/mol]

cyclohexyl (-C6H11) phenyl (-C6H5) phenylene (-C6H4-) -CH3 dCH2 -CH2dCH>CHdC< >C< -F -Cl >Cl2 -Br -I -CN -OH -O-COH -CO-COOH -COO-NH2 -NH-NO2 -N< -SdPO4

1620 1430 1270 420 400 270 200 80 70 -70 220 450 0 550 0 430 210 100 470 290 530 390 280 160 500 20 440 740

0 110 110 0 0 0 0 0 0 0 460 550 360 610 665 1100 500 400 800 770 420 490 610 210 1070 800 0 1890

0 0 0 0 0 0 0 0 0 0 0 400 720 2100 4000 2500 20 000 3000 4500 2000 10 000 7000 8400 3100 1500 5000 0 13 000

where i is the index for the structural groups; VZ is the molar volume of substance Z which can be taken from literature; and FZd , FZp , and UZh are the group molar Hansen parameters for disperse, polar, and hydrogenbonding effects, respectively. According to ref 15, a special condition has to be made for δp: If a molecule has two identical polar groups and, in addition, is symmetric, the polar force of the molecule decreases. To account for this decrease, the parameter is multiplied by a factor of 0.5 for one plane of symmetry, 0.25 for two planes of symmetry, or 0 for more than two planes of symmetry. Different sources and slightly deviating values for Fd, Fp, and Uh can be found in the literature. To ensure that the solubility parameters harmonize with each other we take the values given by van Krevelen and Hoftyzer,16 who are the only ones to present values for all three molar attraction constants. If values for some functional groups are missing, we take them from other sources; see ref 15. As shown below, the distances RZ including water as one component require correction factors. Because often more than one substance should be soluble in a solvent, the radius of interaction is calculated separately for each substance Z, and the arithmetic mean is used

R h )

k

Z 2 (Fp,i ) ∑ i)1

VZ

structural group

k

1 V

Table 1. Group Molar Hansen Parameters for Disperse, Polar, and Hydrogen-Bonding Effects Compiled from Refs 15 and 16

1

V

k

UZh ∑ i)1

Z

(9)

1

z

∑ Ri

z i)1

(10)

The number of soluble substances z should be limited to a maximum of three because the more solutes are included, the less importance is given to a single solute. As a result, an insoluble substance has too little weight in the mean value R h. Hansen’s radius of interaction itself presents only relative solubilities, i.e., it indicates which solvent from

Ind. Eng. Chem. Res., Vol. 41, No. 18, 2002 4537 Table 2. Categories of Solubility: Comparison for Nonaqueous Solvents radius of interaction, R solubility status

lower bound

upper bound

soluble slightly soluble insoluble

0 16.45 17.75

16.45 17.75 infinity

Table 3. Categories of Solubility: Comparison for Water as Solvent radius of interaction, Raq

Figure 1. Comparison of Hansen’s R with Scheflan’s solubility categories.17 The µ values are the medians of the categories.

a list of solvents dissolves a substance best. In a solvent database of three solvents, there is always a bestsoluting solvent for a substance, but a substance might still be insoluble in all three solvents. To obtain absolute solubilities, we create categories of solubility and relate them to R with the help of literature. In ref 17, Scheflan and Jacobs list many organic substances plus their solubilities in several solvents. They give six categories of solubility: infinitely soluble, very soluble, soluble, slightly soluble, very slightly soluble, and insoluble. We reduce this list to the categories soluble, slightly soluble, and insoluble, because Hansen’s model is not an absolutely accurate model, but it still proves fine in many cases.14 For estimation of the upper and lower bounds of each category, the radius of interaction is calculated for several solvent-solute combinations given in ref 17 and compared to the categories of Scheflan and Jacobs, as can be seen in Figure 1. With the assumption of a Gaussian distribution, we calculate the median µ1,2 of the median radii µ1 and µ2 of adjacent categories. Subsequently, we obtain the bounds in weighting the obtained medians according to the variances of the adjacent categories. As can be seen in Table 2, the range for slightly soluble substances is very small. However, it is useful because it softens the bounds between the cases of solubility and nonsolubility. While conducting the comparison between Hansen’s radius and the categories of Scheflan and Jacobs, we found that water has solubility values that deviate markedly from those of other solvents, and consequently, it cannot be included in the concept presented above. In addition, Barton15 reports a considerable variation in the Hansen parameters for water. A possible reason for this is the strong hydrogen-bonding and polar effects of water, which lead to significantly higher values for R. As a result, we obtain a very large radius Raq for nearly every water-solute combination compared to other values of R, even if water dissolves the substance better than solvents with lower values of R. Analyzing the variances and medians in Figure 1, we see that, although the values for water do not fit with the values for ordinary solvents, a correlation between Hansen’s radius and solubility categories is obtained: the smaller the radius Raq, the more soluble the substance in water. As a result, it is indeed possible to implement the solvent water in our concept.

solubility status

lower bound

upper bound

soluble slightly soluble insoluble

0 35.68 38.94

35.68 38.94 infinity

We calculate special bounds for the solubility in water according to the bound calculations of usual solvents and transfer them by correction factors to equivalent R h values; see Table 3. For estimation of the second performance constraint, reactivity, we consider a parameter of the National Fire Protection Agency, the reactivity value NR (range ) 0-4). NR gives a clear distinction of grades of reactivity of the solvent: 0 ) stable, even under fire conditions; 1 ) mildly reactive upon heating with pressure; 2 ) significantly reactive; 3 ) detonation with confinement; and 4 ) detonation without confinement. It is easily available for solvents, e.g., in material safety data sheets.6 We compare and rank the solvents according to various performance constraints (index i ) 1, .., N) as in ref 13. A single score CXi for every solvent X is normalized, denoted by subscript n, with Cmax as the i highest single score among all considered solvents X Ci,n

)

CXi Cmax i

(11)

The single normalized scores are summed after each has been given a weight wi corresponding to its importance N

SXT )

X Ci,n wi ∑ i)1

(12)

There is no unambiguous basis for the weights; they must be selected subjectively for each process by estimating the sensitivity of the process to the various constraints. For the case considered here, the total score SXT is calculated via the two considered normalized constraints solubility, R h Xn , and reactivity, NXR,n, as follows

h Xn wsolub + NXR,nwreact SXT ) R

(13)

where wsolub is the weight for solubility and wreact is the weight for reactivity. For a process in which the two aspects are equally important, the weights would be set to wsolub ) 1 and wreact ) 1. In each case, one would vary the weights slightly to gauge the sensitivity. In the case of identical outcomes over a wide range, the choice is clear; in the case of different results, one would evaluate the various solvents according to other criteria as well. Solvents that dissolve all substances and are among the best-performing 10 pass the performance test.

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Table 4. Hansen Solubility Parameters δd (MPa1/2)

δp (MPa1/2)

δh (MPa1/2)

solute

13.90 17.18 10.39

11.71 19.98 6.33

8.55 5.29 5.17

CH3NCS CH3NO2 O2NCHdC(SK)(NHCH3)

2.1.2. Application to the New NMSM Process. For both NMSM processes, the performance test was conducted. In the following, we demonstrate the application of the concept to only one process, the new manufacturing process. Possible choices for the solutes according to eqs 3 and 4 are CH3NCS, CH3NO2, KOH, (CH3O)2SO2, and O2NCHdC(SK)(NHCH3). Of these, a maximum of three substances have to be selected as solutes. KOH as an inorganic salt is not acceptable because it dissolves in the form of ions; hence, it underlies a different solution concept. We chose CH3NCS, CH3NO2, and the intermediate O2NCHdC(SK)(NHCH3), and after the environmental test, we checked the last solute, dimethyl sulfate, for solubility in the preferred solvents. The process temperature varies between 273 and 283 K. This range is so narrow that in a first approach a mean can be used (278 K). Molecular weight, density, and functional groups, according to Table 1, of all three substances are required for solubility calculations. The obtained Hansen solubility parameters for dispersion, polar, and hydrogenbonding forces are reported in Table 4. Solubility calculation and subsequent estimations of the solubility status according to Tables 2 and 3 give the results presented in Table 5. There are six solvents that dissolve all three substances: acetone, n-butyl acetate, 1,2-dichloroethane, dimethyl sulfoxide, methylethyl ketone, and 1,1,1-trichloroethane. Calculating the mean radius for every solvent and normalizing it according to eq 11 leads to the solubility score R h Xn . In X the NMSM processes, high solubility (R h n ) is much more important than low reactivity (NXR,n) of the solvent X. Hence, we chose wsolub ) 4 and wreact ) 1 as weights for eq 13. This yields the total performance scores SXT shown in Table 6, ranging from 1.39 for the optimum performance to 4.0 for the worst performance. It should be noted that TSolidification(benzene) is 279 K. Furthermore, the condition in eq 5 is fulfilled for only part of the T region (273-283 K); it is not fulfilled for the defined process temperature TProcess ) 278 K. For more exact results, the test therefore can be repeated with the exact T ranges. The total scores of solvents failing in the temperature condition, e.g., benzene, are set to 99 to exclude them from further calculations. The lowest score obtained is 1.39 for acetone, followed by the scores for methylethyl ketone (1.59) and dimethyl sulfoxide (1.92). Variations in the weight wsolub within the range 0.2-20 showed no change in the first three positions; hence, sensitivity is very low, and the result is unambiguous. 2.2. Environmental Test. 2.2.1. Theory. Solvents passing the performance test are checked further for their environmental behavior. Anastas and Williamson18 give a general list of hazards. The hazards can be classified as long-term hazards, such as the depletion of stratospheric ozone, and short-term hazards, such as an explosion in a plant. A further distinction can be made for the range of pollution. Substances can cause

damage in a wide or global range, e.g., global-warming substances, whereas others influence only the surroundings of a process, e.g., a leakage leading to poisoning of groundwaters. To cover all such cases and to obtain the least harmful impact of the solvents on the environment, the following aspects are included in the solvent selection concept: (1) global impact (ODP, GWP), (2) toxicity for man and nature (BOD5, NH) and (3) plant safety (F&E index). Joback13 and Cabezas et al.12 have conducted environmental assessments of solvents by a normalizing, weighting, and ranking process that is equivalent to eqs 11 and 12. In Joback’s concept, the environmental constraints are treated as solvent constraints and, as such, are terms in the equation for calculating solvent scores. By these approaches, environmental hazards can be compared with each other. Applying this approach to our concept might result in a toxic solvent getting a better score, if it has an extremely low BOD and F&E index, than a less toxic solvent with a slightly hazardous BOD and F&E index. This has strong disadvantages, because the score gives only relative results and does not indicate whether single hazards are critical. Additionally, there are several environmental hazards that only apply to very few substances. Ozone-depleting substances, for example, can be totally avoided. Their phase-out has already been conducted in developed countries but still lies in the future for developing countries. Similar agreements are probable in the future for global-warming substances. Hence, we consider each single environmental constraint on its own and set thresholds. The thresholds might depend on regional situations. ODP and GWP. In the Montreal Protocol and in refs 19 and 20, ozone-depleting substances and globalwarming substances are listed according to their ozonedepleting potential (ODP) and global-warming potential (GWP). Because only very few substances are listed there, their usage can be avoided, i.e., the conditions are set to ODP ) 0 and GWP ) 0. BOD5. The biological oxygen demand is a reliable gauge of organic pollution in water. According to ref 21, we set a threshold of BOD5 e 2. NH. There are different methods for estimating the toxicity of a material for man and animals. Not every method is general enough to cover the different means of poisoning, which could be oral, cutaneous, or respiratory and short-term or long-term. The often-cited LD50 covers only the short-term oral exposure; on the other side, thresholds such as the MAK (maximum concentration at the working place) cover only the long-term respiratory poisoning. Therefore, we decided to use the NFPA value NH (range ) 0-4), which covers the main aspects previously mentioned. The threshold for the F&E index already decreases the probability of an accident with the tested solvent; hence, we define a relatively high NH of 3, indicating serious temporary injury, as the upper threshold for acceptable solvents. Two issues deserve closer attention. For plant toxicity, no consistent and dominating criterion is available to date. Carcinogenicity is the other special case. There are carcinogenic substances in the database that can, in certain cases, be preferable solvents. Depending on local regulations, it might yet be necessary to replace carcinogenic solvents that passed the environmental test by noncarcinogenic solvents. Persistent organic pollutants (POPs) might not be a major problem, because the

Ind. Eng. Chem. Res., Vol. 41, No. 18, 2002 4539 Table 5. Solubility of the Reactants for NMSM in Solvents solvent

CH3NCS

CH3NO2

O2NCHdC(SK)(NHCH3)

acetone benzene n-butyl acetate 1-butanol 1,2-dichloroethane dimethyl sulfoxide ethanol 2-ethylhexanol hexane methanol methylethyl ketone 2-propanol propylene glycol toluene 1,1,1-trichloroethane water o-, m-, and p-xylene

soluble soluble soluble soluble soluble soluble soluble soluble soluble soluble soluble soluble soluble soluble soluble soluble soluble

soluble insoluble soluble insoluble soluble soluble insoluble insoluble insoluble insoluble soluble insoluble insoluble insoluble soluble slightly soluble insoluble

soluble soluble soluble soluble soluble soluble soluble soluble soluble insoluble soluble soluble insoluble soluble soluble slightly soluble soluble

Table 6. Performance Scores for Solvents Applied to the new Ranatidine Process

Table 7. Classification for Environmentally Acceptable Solvents

position

solvent

score

NH

F&E index

remaining negative impact

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

acetone methylethyl ketone dimethyl sulfoxide n-butyl acetate 2-ethylhexanol 1-butanol 2-propanol o-, m-, and p-xylene toluene 1,2-dichloroethane hexane ethanol 1,1,1-trichloroethane water methanol propylene glycol benzene

1.39 1.59 1.92 2.23 2.63 2.84 2.89 2.96 2.97 3.13 3.22 3.23 3.44 3.64 3.70 4.00 99

0-1 0-1 2-3 2-3

x e 60 60 < x e 96 x e 60 60 < x e 96

low medium medium high

Stockholm Convention lists only 12 chemicals, 9 of which are used as pesticides and none as solvents for chemical processes. F&E Index. Dow’s fire and explosion index is a direct method for providing a relative ranking of the risks of fire and explosion in a chemical process plant.22 It assigns penalties and credits based on plant features, which are combined to derive an index, a relative ranking of the plant risk, ranging from 0 for little danger to more than 158 for severe danger. The following three categories show the data relevant for its calculation: (1) material data, including boiling point, flash point, NFPA indices (NF, NH, NR); (2) general process hazards, including kind of equipment, e.g., reactor or tank, presence of moving parts in the facility, etc.; and (3) special process hazards, including material data combined with heat of combustion and process volume. We developed a model of a storage tank to estimate the F&E index deriving from a solvent: a 120 000-kg storage tank at atmospheric pressure and a maximum temperature of 323 K located outside the process area in a warehouse. No special safety measures were taken. The procedure for calculating the index is described in various references,22-24 and the setup calculation is done with the help of the database. Each process can be categorized via the index according to its degree of hazard (see ref 22). We define solvents with F&E index e96 (light and moderate hazards) as acceptable and set a threshold of 96.

Table 8. Results of the Environmental Test solvent

F&E ODP/GWP index BOD5/Nh acceptable?

acetone benzene n-butyl acetate 1-butanol 1,2-dichloroethane dimethyl sulfoxide ethanol 2-ethylhexanol hexane methanol methylethyl ketone 2-propanol propylene glycol toluene 1,1,1-trichloroethane water o-, m-, and p-xylene

0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.00/0 0.11/140 0.00/0 0.00/0

thresholds

0.00/0

128 128 128 128 128 5 124 17 91 128 128 128 23 128 26 1 128

0.69/1 0.00/2 1.02/1 1.50/1 0.00/2 0.00/1 0.93/0 0.00/2 2.21/1 0.76/1 2.24/1 1.29/1 0.96/0 1.23/2 0.00/3 0.00/0 1.25/2

96

2.00/3

no no no no no yes no yes no no no no yes no no yes no

Acceptability and Classification. If the values of all previously discussed environmental constraints are below the respective thresholds, a tested solvent is judged as environmentally acceptable. Still, these environmentally acceptable solvents might have some, less negative impacts on the environment. To estimate the remaining hazard deriving from the acceptable solvents, a classification is given according to Table 7. 2.2.2. Application to the New NMSM Process. According to the concepts described above, we estimate the global hazard through ODP and GWP, the fire and explosion hazards through the F&E index, and the hazard to man and nature through BOD5 and Nh for every solvent. The necessary data are given in the database, and after calculation of F&E index and acceptability, we obtain the results listed in Table 8. The solvents passing the environmental test are dimethyl sulfoxide (DMSO), 2-ethylhexanol, propylene glycol, and water. DMSO is in position 3 in the performance test, and 2-ethylhexanol is in position 5 in the performance test, i.e., both pass the environmental and performance tests. They are, by definition, environmen-

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tally acceptable and give good performances in the examined manufacturing process for NMSM. DMSO has a light remaining impact according to the definition given in Table 7, whereas 2-ethylhexanol is slightly more harmful with a medium remaining impact. Water and propylene glycol are in positions 14 and 16 of the performance ranking. They should not be considered as solvents as long as no special performance enhancer is added. Before we finally decide on the best solvent, we have to consider the solubility of the reactant (CH3O)2SO2 in the two recommended solvents because this reactant has not yet been tested. Calculating the radius of interaction for each (CH3O)2SO2-(recommended solvent) combination and applying the classification into solubility categories according to Table 2, we find that both solvents are suitable. Because DMSO obtains a better score in the performance test and has less remaining impact on the environment according to Table 7, it should be the first choice of a solvent for this process. 3. Waste Minimization 3.1. Theory. Waste minimization is a multifaceted field, ranging from designing or evaluating efficient chemical reaction paths (e.g., refs 25 and 26) via optimization and assessment in development stages of design,27 to flowsheet synthesis or optimization of individual pieces of equipment (e.g., Halim et al.28 and Lakshmanan et al.29). In addition to the diversity of optimization approaches, the concepts show a broad variety of knowledge input into the process. The reactor network synthesis concept of Lakshmanan et al.29 and the approach of Halim et al.28 via ENVOP, a systematic evaluation of waste minimization potential of each piece of equipment, necessitate in-depth knowledge of single process components. A different concept, more global and less detail-enriched, hence easier to apply, has been proposed by Steinbach.32 Based on research in the field of minimizing waste by replacing raw-material-consuming reactions by more efficient reaction paths,26,30,31 Steinbach additionally developed a systematic approach to identify weak points of a process in analyzing the composition of input and output flows. We expand his concept in a few aspects and modify it in a way that enhances its application and implementation as software. Steinbach defines32

productivity BA ≡

mP,act mIn

(14)

This simple mass balance relates the output of product mP,act to the input of raw material mIn consisting of reactants and additional material such as solvents. The output of the process consists of two streams: the desired product and the residue stream, i.e., byproducts, unrecycled additional agents, etc. Inspecting eq 14, we can conclude that the productivity BA is a measure of efficiency of raw material: the more product per input, the more efficient the process. An increase in productivity requires knowledge of the main influencing parameters. These parameters and their influences can be expressed in terms of a productivity function, which consists of two parts

productivity BA ) BATHBASP

(15)

The first part is based on the chemical reaction equation itself, whereas the second part is the degree of optimization of the existing process. The theoretical balance yield BATH is the theoretical amount of product mP,th divided by the theoretical amount of reactants mR,th p

BATH ≡

mP,th mR,th

nP MWP ∑ j)1 j

)

j

(16)

r

nR MWR ∑ i)1 i

i

where n is the number of moles, MW is the molecular weight, p is the number of products, and r is the number of reactants. BASP is the specific balance yield and is a measure of the degree of optimization of the process. It consists of three factors: the relative yield factor RA, the excess factor MATR, and the additional agents factor EAP. RA is the relative or stoichiometric yield calculated using parameters of the principal raw material

RA ≡

mR,th mR,act,stoich

(17)

where mR,act,stoich is the actual mass of stoichiometrically added reactants. The excess factor MATR is the factor accounting for the excess of reactants

MATR ≡

mR,act,stoich mR,act

(18)

i.e., this factor is the actual stoichiometric amount of reactants divided by the amount of reactants actually used, mR,act. The additional agents factor EAP accounts for the amount of additional material used in the process

EAP ≡

mR,act mIn

(19)

In contrast to Steinbach, we define the system borders at the entrance and exit of the reaction section, i.e., we exclude upstream and downstream processes. 3.2. Application to the NMSM Processes. We tested the processes for NMSM. Table 9 shows the input of reactants and additional agents into the original (I) and improved industrial (II) process and the output of product. The improved industrial process (II), based on the same chemical reaction, was developed because the original process (I) was obviously consuming a huge amount of input: 1300 kg of input to obtain 57 kg of NMSM, i.e., BA ) 4.4%. With the new process, the productivity rose to BA ) 63/1245 ) 5.1%. Laboratoryscale experiments of the newly developed process (III) for manufacturing NMSM, containing the new chemical path, obtained the results presented in Table 10. With an input of 0.449 kg, 0.1 kg of NMSM was obtained (BA ) 22.3%). When applying the balance yield calculation to these three different processes I, II, and III to check for each grade of optimization, we obtained the results shown in Table 11. The first factor BATH, representing the degree of utilization of reactant atoms in the product

Ind. Eng. Chem. Res., Vol. 41, No. 18, 2002 4541 Table 9. Input and Output of the Industrial Ranatidine Processes original process I

improved process II

material

input (kg)

NMSM (kg)

input (kg)

NMSM (kg)

CH3NO2 CS2 KOH (CH3O)2SO2 CH3NH2 additional agents

80 150 180 234 20 636

57

80 150 180 187 16 632

63

1300

57

1245

63

total

Table 10. Input and Output of the New Ranatidine Process III material

input (kg)

CH3NCS CH3NO2 KOH (CH3O)2SO2 additional agents

0.078 0.090 0.085 0.170 0.026

total

0.449

product

output (kg)

NMSM

0.1

0.1

Table 11. Productivity and Productivity Factors of the Ranatidine Processes original process I

improved process II

new process III

36.5 61.7 38.0 51.1 4.4

36.5 84.5 33.5 49.2 5.1

46.8 54.8 92.1 94.2 22.3

BATH (%) RA (%) MATR (%) EAP (%) BA (%)

molecules, shows a higher value for the third process III compared to the industrial processes I and II. This stems from a change in chemistry, as can be seen from eqs 3 and 4 for III and eqs 1 and 2 for the two industrial processes I and II. Additionally, there are huge improvements in the excess factor MATR and the additional agents factor EAP from below 50% to over 90% optimization. The new chemistry improves not only the theoretical utilization of input chemicals, i.e., BATH, but also the ratio of stoichiometric to actually used reactants, MATR. That is, this process is more efficient in two ways. The main reduction in the use of additional agents influencing EAP could be reached because the new process III requires much less solvent. These increases obviously lead to a higher productivity, and as a result, the degree of waste minimization is more than 4 times higher. From the environmental point of view, the newly developed process III should therefore be preferred by the manufacturing industry. As already mentioned in the Introduction, the ecological impact assessment performed does not include economic aspects. These and similar issues must be evaluated subsequently by other methods. 4. Software Both concepts are implemented in the free software PoProP, which is platform-independent and can be downloaded from the Web at http://poprop.sourceforge.net. It can be redistributed and/or modified under the terms of the GNU General Public License as published by the Free Software Foundation. 5. Conclusions In this paper, two important aspects of pollution prevention have been considered. Concepts and software

for waste minimization and solvent substition are developed in which chemical process industries and related research institutes can examine already developed processes or processes in work for their degree of optimization with respect to pollution prevention. The application of both methods to the production of NMSM gives reliable results and highlights the most suitable solvent for each process and the most productive, hence least waste-producing, process. Acknowledgment The corresponding author gratefully acknowledges the financial support of the Ernest-Solvay Stiftung. M.L. thanks the DFG for a fellowship (Li669/2-1). Nomenclature BA ) balance yield BASP ) specific balance yield BATH ) theoretical balance yield BOD5 ) biological oxygen demand in 5 days C ) single raw score EAP ) additional agents factor Fd ) dispersion molar attraction factor [(J‚cm3)1/2/mol] Fp ) polar molar attraction factor [(J‚cm3)1/2/mol] GWP ) global-warming potential MATR ) reactant excess factor MW ) molecular weight (kg/mol) N ) number of constraints NF ) NFPA fire parameter NH ) NFPA health parameter NR ) NFPA reactivity parameter ODP ) ozone-depleting potential R ) radius of interaction (MPa1/2) RA ) relative yield ST ) total performance score T ) temperature (K) Uh ) hydrogen-bonding cohesive energy (J/mol) V ) molar volume (cm3/mol) k ) number of functional groups m ) mass (kg) mIn ) input of raw material (kg) mP,act ) actual output of product (kg) mR,act ) actual input of reactants (kg) mR,act,stoich ) actual mass of stoichiometrically added reactants (kg) n ) number of moles (mol) nR,act,stoich ) actual amount of stoichiometrically added reactants (mol) p ) number of products r ) number of reactants wi ) weight for constraint i z ) number of soluble substances Greek Letters δd ) dispersion solubility factor (MPa1/2) δp ) polar solubility factor (MPa1/2) δh ) hydrogen-bonding solubility factor (MPa1/2) µ ) median radius of interaction (MPa1/2) Subscripts and Superscripts P ) product R ) reactant X ) solvent Z ) substance act ) actual aq ) water i, j ) counters max ) maximum

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n ) normalized react ) reactivity

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Received for review January 11, 2002 Revised manuscript received May 17, 2002 Accepted May 25, 2002 IE020037N