Statistical Analysis of Empirical Data - American Chemical Society

parameter, thus enabling quantitative uncertainty analysis. (e.g. Monte Carlo Simulation). In a case study we illustrate the application of the invent...
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Environ. Sci. Technol. 2005, 39, 5885-5892

Life-Cycle Inventory of Waste Solvent Distillation: Statistical Analysis of Empirical Data C H R I S T I A N C A P E L L O , * ,† STEFANIE HELLWEG,† BEAT BADERTSCHER,‡ AND KONRAD HUNGERBU ¨ HLER† Swiss Federal Institute of Technology, Safety and Technology Group, ETH Ho¨nggerberg, HCI G143, CH-8093 Zurich, Switzerland, and Valorec Services AG, Postfach 118, CH-4019 Basel, Switzerland

Distillation is one of the most important processes in the chemical industry. An environmental assessment of distillation processes is difficult because of the highly specific features of each distillation process. Life-cycle inventory (LCI) information is therefore scarce. The goal of this paper is to provide reliable data ranges for inventory flows of waste solvent distillation (i.e. amount of recovered distillate, consumption of steam, electricity, nitrogen, cooling water and ancillary product, and the generation of organic waste, wastewater, and outlet air). For this purpose, we collected data from approximately 150 waste solvent distillation processes from chemical industry and analyzed them statistically. The results of the statistical analysis compose generic data ranges for each LCI parameter. Where appropriate, the data of each LCI parameter have been subdivided according to the distillation technology or the waste solvent composition. Additionally, probability distributions have been fitted to the data of each LCI parameter, thus enabling quantitative uncertainty analysis (e.g. Monte Carlo Simulation). In a case study we illustrate the application of the inventory data ranges according to situations of differing data availability.

Introduction The chemical industry produces large amounts of chemical liquid mixtures. In many cases only one or few specific substances in such a mixture are of interest to the industry. Therefore, separation of individual substances or complete fractionation of such mixtures into their components is an important step in many processes, from production to recycling stages. Among the various existing separation technologies, distillation is the most important industrial process (1). The principle mode of operation for the distillation process is to create two or more coexisting zones, which differ in phase state, i.e., vapor and liquid (2). When the vapor is cooled and condensed, the condensate contains the majority of volatile components, while the remaining mixture contains the majority of less volatile material (3). Various kinds of devices in the distillation column, such as packing, plates, * Corresponding author phone: 0041-44-6334401; fax: 0041-446321189; e-mail: [email protected]. † Swiss Federal Institute of Technology, Safety and Technology Group. ‡ Valorec Services AG. 10.1021/es048114o CCC: $30.25 Published on Web 06/21/2005

 2005 American Chemical Society

or trays, are used to bring the liquid and gas phase into intimate contact. The feed material, which is to be separated into fractions, is introduced at one or more points along the column shell. Liquid that reaches the bottom of the column is vaporized in a heated reboiler to provide boil-up, which is sent back up the column. Vapor that reaches the top of the column is cooled and condensed to liquid in the overhead condenser. When a part of this liquid is returned to the column as reflux, the distillation process is called rectification. The remainder of the overhead stream is withdrawn as distillate or overhead product (2). In the present study, we focus on such rectification processes, because rectification is used widely as a separation process in the chemical industry (1). One of the major product groups that are subject to rectification in chemical industry are solvents. Solvents are used in large quantities for a range of products (paints, coatings, adhesives), as raw material for product syntheses, as reaction media, and for equipment cleaning (4). From the 440 000 tonnes fresh solvents that are annually imported to Switzerland (5), 250 000 tonnes are used in the pharmaceutical and speciality chemical industry (6). Due to this considerable amount and the fact that solvents may pose environmental problems as a result of their high volatility and toxic nature (7), a comprehensive environmental assessment of waste solvent treatment is needed. A suitable tool for such an assessment is life-cycle assessment (LCA). Waste solvents are either treated thermally or they are distilled and therefore recycled. While inventory tools for waste solvent incineration technologies have been published (8, 9), general inventory information on distillation processes is scarce. This lack of data is largely due to the highly specific features of each distillation process that may compromise the general applicability of the results to other feed compositions: for instance, final product purity, the column types, and the properties of the mixture differ from distillation to distillation. Published inventory data of distillations with different feedstock, such as used oil (10), are therefore not directly applicable for modeling waste solvent distillations. Some existing frameworks for waste solvent distillation are limited to single case studies (11, 12), the results of which cannot be transferred to other waste solvent mixtures. Geisler et al. (13) estimated default values of utility inputs for solvent regeneration as well as an average solvent recycling rate, based on industry data and expert judgment. A potential further approach is the calculation of inventory data considering the fundamental chemical nature of the waste solvent mixture and the properties of the distillation equipment. Such calculation routines mostly focus on energy demand e.g. refs 14 and 15. However, such “bottom-up” approaches require specific information about the waste solvent composition and the design of a distillation column. Often, this information is not available, especially in the early state of process development. Therefore, methods are needed that allow the estimation of inventory data as a function of only a few input parameters available to decision makers in the chemical industry. The goal of this paper is to provide reliable data ranges for all life-cycle inventory (LCI) flows of waste solvent distillation, i.e., use of steam, electricity, cooling water, and nitrogen to avoid critical oxygen concentration within explosion limits, ancillary products for pH-regulation, equipment cleaning, entrainer, and the output of the recovered component, residues, wastewater, and outlet air. For this purpose, we collected data from approximately 150 waste solvent distillation processes in chemical industry and VOL. 39, NO. 15, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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analyzed them statistically. Probability distributions were fitted to the data of each LCI parameter. The provided data ranges and probability functions are particularly useful to inventory distillation processes in the absence of precise information. In a case study we illustrate how such inventory data ranges may be used in situations of differing data availability.

Methods Data Acquisition and Sample Description. LCI data from 150 waste solvent distillation processes were collected in collaboration with the Swiss chemical industry. The LCI data contain values for the demand of steam, electricity, cooling water, inert gas (nitrogen), and ancillary products as well as for the amount of recovered solvent, residues, wastewater, and outlet air. All data refer to the distillation of 1 kg of waste solvent. Since data were not always available for all parameters, the total number of data points per LCI parameter varied between 131 and 143. The sample included directly measured data (solvent recovery, ancillary products, residues, and wastewater) as well as data calculated on the basis of annual totals measured for entire buildings with multiple distillation columns (steam, electricity, nitrogen, and cooling water). Data of outlet air were calculated for each distillation process based on expert judgment (16). The waste solvent composition within the sample varied largely with regard to the number and concentrations of components and contamination with particulate matter, inorganic salts, or halogens. In general, one or two main components were recovered from the waste solvents. In total, 22 different solvents were recovered as distillate mostly with a purity of 99.5 wt % or higher. The contamination of waste solvent mixtures was generally below 10 wt %. The boiling points of the recovered solvents were between 50 °C and 200 °C. These characteristics are typical of waste solvent mixtures from pharmaceutical and speciality chemical industry (16). The equipment used for all separations were multipurpose distillation columns on an industrial scale with a throughput of 400-2500 L per hour. Multipurpose columns are useful for solvent regeneration because the waste solvent composition can vary from one production campaign to the next within a short time. The mode of operation of the distillation processes was batch (9 columns) as well as continuous (8 columns). In contrast to batch processes, continuous processes work with a continuous feed stream without interruption. Three of the distillation columns can be operated under vacuum pressure up to 50 mbar. But most of the separation processes were conducted under normal pressure. Statistical Data Analysis. The empirical data were analyzed using descriptive statistics. The data points of each LCI parameter vary within a certain range. This uncertainty originates from two sources. First, there is a general parameter uncertainty due to imprecise measurement, calculation, or expert judgment. Second, variability between objects (17) is caused by different distillation columns, modes of operation (batch and continuous distillations), and different waste solvent compositions. The results of the statistical analysis are empirical average, minimum, and maximum values for each LCI parameter. The empirical average values were calculated using the arithmetic mean of the samples. The minimum and maximum values were determined using the empirical 95% interval. Hence, the empirical 2.5th percentile is defined as minimum value and the empirical 97.5th percentile as maximum value. Thus, more robust maximum and minimum values can be determined than with the statistical range as a measure of spread, because statistical outliers do not influence the result. In addition to the descriptive statistics, correlations between each LCI parameters were analyzed. Since the data are often not normally distributed (e.g. they show long tails), 5886

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Spearman’s rank order coefficients were calculated as a measure of the strength of the relationships (18). In addition to the correlation analysis, we investigated whether the sample should be subdivided for each LCI parameter according to the mode of operation of the distillation columns (batch/continuous distillation) or the composition of the feed mixture (polarity, enthalpy of vaporization, and pH). The statistical test used for quantifying the influence of mode of operation and solvent composition was the two-sided MannWhitney test on a significance level of 95% (19). The results were discussed with experts from the chemical industry. LCI parameters were then classified into three groups. The first group contains parameters with independent variability. Neither the mode of operation nor the chemical properties of a waste solvent mixture influence the scatter of the empirical data. LCI parameters of the second group show significantly different values for batch and continuous distillation processes. The third group comprises LCI parameters that depend on the waste solvent composition. Here polar and nonpolar or acid and alkaline mixtures were distinguished. Probability distributions were fitted to the empirical (sub-)sample data of each LCI parameter. The goal was (1) to provide general estimates for LCI data of distillation processes with comparable equipment and mixtures and (2) to enable quantitative uncertainty analysis using stochastic modeling (e.g. Monte Carlo Simulation (20)). To determine the best fitting probability distribution, three different fit statistics were considered: Chi-squared, KolmogorovSmirnov, and Anderson-Darling (20). The fitting was conducted using the @risk software tool (21). The procedure described above is depicted in Table 1. Minimum and maximum values of the empirical data as well as of the fitted probability distributions were determined using the 95% interval. To estimate average values, point estimators and confidence intervals were calculated from the sample according to eq 1 (22)

xj -

ZR/2‚s

xn

e µ e xj +

ZR/2‚s

xn

(1)

where xj is the sample mean, s is the standard deviation of the sample, n is the number of data points, µ is the true average value of the population, and the ZR/2 are obtained from a table of normal deviates based on the desired level of confidence, R. We used a confidence level of 95%.

Results Statistical Analysis. In Table 2, the Spearman’s rank order correlation coefficients between the various parameters are shown. No strong correlations were found. This finding indicates that the variability of the data may be influenced by other factors (e.g. mode of operation, see Table 3). Maximum values (empiric 97.5th percentile), minimum values (empiric 2.5th percentile), and average values (sample mean) of the total sample and of the subsamples are shown in Figure 1 and Table 3. Table 3 additionally shows the probability models fitted to the empiric subsamples of every LCI-parameter. With respect to the amount of recovered solvent, the empiric average value is 0.71 kg of recovered solvent per kg of waste solvent mixture (Figure 1). The amount of recovered solvent depends on the concentration of the recovered component in the waste solvent mixture and the solvent recovery rate. The solvent recovery rate is defined here as the amount of solvent recovered per kg input of the specific component. Experts from the chemical industry estimate the average solvent recovery rate for waste solvent distillations to be 90% (16). The amount of recovered solvent varies from

TABLE 1. Procedure of the Statistical Data Analysis

a

The sample data are subdivided according to distillation technology or waste solvent composition, as appropriate.

0.31 to 0.97 kg per kg waste solvent (Figure 1). Distillations with the very high solvent recovery of 0.97 kg per kg waste solvent purify the specific component from an already highly concentrated level (up to 98 wt %) to 99.5 wt % or more. On the lower end, distillations with a low solvent recovery of 0.31 kg per kg waste solvent are conducted in case costly solvents can be recovered (e.g. tetrahydrofuran or triethylamine) or due to logistical reasons. For example, solvents may be distilled if there is no capacity in incineration or wastewater treatment plants or if they cannot be directly treated by these technologies due to environmental regula-

tions. In the latter case, waste solvent is distilled in order to separate environmentally problematic compounds, such as dioxane or halogenated solvents, from the mixture. In addition, the solvent recovery efficiency is significantly dependent on the mode of operation of the distillation column: Continuous distillations have an average solvent recovery of 0.76 kg per kg waste solvent, as opposed to batch distillations, with an average of 0.68 kg per kg waste solvent (Table 3). The reason efficiency of batch distillations is lower should be attributed to the fact that batch distillation processes require some time until the optimal equilibrium VOL. 39, NO. 15, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 2. Spearman’s Rank Order Correlation Coefficients of the LCI Parameters recovered solvent

residues

wastewater

steam

electricity

nitrogen

cooling water

outlet air

-0.08

0.20 -0.12

0.19 -0.41 -0.39

-0.02 -0.16 0.07 0.26

0.10 -0.03 0.08 0.26 0.39

0.07 -0.14 0.41 0.07 0.41 0.39

-0.22 0.05 -0.16 0.21 0.40 0.52 0.34

0.21 -0.04 0.3 0.15 0.47 0.45 0.56 0.14

ancillaries recovered solvent residues wastewater steam electricity nitrogen cooling water

TABLE 3. Subdivision of the LCI Parameters into Subsamples and Results of the Statistical Analysis LCI parameter [unit]

subsamples

na

fitted probability model

empiric min/ max values

min/max values of the fitted model

parametrization

estimation of the average using eq 1

Group 1 electricity [kWh/kg waste solvent]

no

131 min ) 0.005 max ) 0.102

cooling water [m3/kg waste solvent]

no

131 min ) 0.001 max ) 0.079

recovered solvent [kg/kg waste solvent]

batch distillation

log-normal dist µ* ) 0.036 X∼lN(µ*,σ*)b σ* ) 0.029 shift ) -0.003 log-normal dist µ* ) 0.038 X∼lN(µ*,σ*)b σ* ) 0.017 shift ) -0.011

min ) 0.004 max ) 0.110

0.033 ( 0.005

min ) 0.004 max ) 0.070

0.027 ( 0.003

min ) 0.40 max ) 0.96 min ) 0.66 max ) 0.98 min ) 0.10 max ) 0.57

0.68 ( 0.03

min ) 0.14 max ) 0.45 min ) 0.66 max ) 3.12

0.22 ( 0.02

min ) 0.44 max ) 2.69

1.21 ( 0.16

min ) 0.0013 max ) 0.0036

2.4 × 10-3 ( 0.14 × 10-3

Group 2 82 min ) 0.42 max ) 0.94 48 min ) 0.21 max ) 0.98 83 min ) 0.14 max ) 0.56

normal dist X∼N(µ,σ)c uniform dist X∼U(R,β)d triangular dist X∼T(R,µ,β)e

continuous distillation batch distillation

51 min ) 0.14 max ) 0.42 71 min ) 0.81 max ) 2.81

exponential dist X∼exp(λ)f log-logistic dist X∼lL(γ,β,R)g

continuous distillation

51 min ) 0.42 max ) 2.57

log-normal dist X∼lN(µ*,σ*)b

batch distillation

71 min ) 0.0015 log-logistic dist max ) 0.0039 X∼lL(γ,β,R)g

continuous distillation batch distillation

51 min ) 0.0000 max ) 0.0015 72 min ) 0.032 max ) 0.178

exponential dist X∼exp(λ)f log-normal dist X∼lN(µ*,σ*)b

continuous distillation

51 min ) 0.024 max ) 0.116

log-normal dist X∼lN(µ*,σ*)b

acid mixtures

62 min ) 0.0003 exponential dist λ ) 0.043 min ) 0.0018 shift ) -4 × 10-4 max ) 0.1286 max ) 0.1466 X∼exp(λ)f 0 no fitting

continuous distillation batch distillation

residues [kg/kg waste solvent]

steam [kg/kg waste solvent]

nitrogen [Nm3/kg waste solvent]

outlet air [Nm3/kg waste solvent]

µ ) 0.68 σ ) 0.14 R ) 0.65 β ) 0.99 R ) 0.04 µ ) 0.22 β ) 0.65 λ ) 0.084 shift ) 0.14 γ ) 0.15 β ) 1.23 R ) 4.15 µ* ) 1.20 σ*) 0.59 shift ) -0.002 γ ) -0.001 β ) 0.004 R ) 11.72 λ ) 2.4 × 10-4 shift -4.7 × 10-6 µ* ) 0.15 σ* ) 0.03 shift ) -0.06 µ* ) 0.05 σ* ) 0.03 shift ) 0.01

0.76 ( 0.06 0.30 ( 0.03

1.53 ( 0.14

min ) 1 × 10-6 0.24 × 10-3 ( max ) 0.87 × 10-3 0.12 × 10-3 min ) 0.042 0.095 ( 0.008 max ) 0.166 min ) 0.024 max ) 0.151

0.062 ( 0.008

Group 3 ancillary products: pH-regulation, equipment cleaning [kg/kg waste solvent] ancillary products: entrainer [kg/kg waste solvent] wastewater [kg/kg waste solvent]

a

alkaline mixtures

polar, azeotropic 10 min ) 0.21 mixtures max ) 0.36 nonpolar mixtures 0 polar 103 min ) 0.00 max ) 0.70 nonpolar 40 min ) 0.00 max ) 0.03

1 x-µ exp 2 σ σx2Π

( (

2

))

b

f(x) )

1

µ e x e β triangular distribution. f f(x) ) λ‚exp(-λx) exponential distribution.

is leveled off for each newly filled vessel. In the start-up phase, the distillate output has to be disposed of as residue until the 5888

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R ) 0.19 β ) 0.38

min ) 0.19 max ) 0.38

0.27 ( 0.05

λ ) 0.126 shift ) -0.001 λ ) 0.0016 shift ) -4 × 10-5

min ) 0.002 max ) 0.464 min ) 0 max ) 0.006

0.126 ( 0.045

( (

0.002 ( 0.002

))

1 1 ln(x) - µ* 2 exp log-normal distribution. c f(x) ) x 2 σ* σ*x2Π 2( 2(β - x) x R) 1 normal distribution. d f(x) ) , R e x e µ f(x) ) , , R e x e β uniform distribution. e f(x) ) β-R (µ - R)(β - R) (β - µ)(β - R)

The sample size of the subsamples is shown in column “n”.

1

uniform dist X∼U(R,β)d no fitting exponential dist X∼exp(λ)f exponential dist X∼exp(λ)f

0.043 ( 0.012

ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 39, NO. 15, 2005

‚

g

f(x) )

x-γ R‚t(R-1) , with t ) log-logistic distribution. β β(1 + t R)2

desired purity level is reached. Additionally, further residues remain in the vessel and piping. The most suitable probability

FIGURE 1. Generic inventory results based on the full data sample. model for the solvent recovery of batch distillations is a normal distribution; for continuous distillations, a uniform distribution (Table 3). On the average, 0.27 kg of residues are generated per kg of waste solvent (Figure 1). Residues are either treated in hazardous waste incinerators or can be used as fuel substitute in cement kilns (8, 9). As in the case of recovered solvent, the mode of operation of the distillation significantly influences the amount of residues. For batch distillation, an average amount of residues of 0.30 kg accrues per kg of waste solvent; for continuous distillations, 0.22 kg. The most suitable probability model for batch distillation is a triangular distribution; for continuous distillation, an exponential distribution (Table 3). Aqueous waste from the distillation of aqueous solvents (mostly methanol mixtures) or cleaning residues can be treated as wastewater in a wastewater treatment plant, if the characteristics of the residue are in accordance with environmental regulations. The average TOC was 4 g/kg wastewater. Wastewater had to be disposed of in only 32 of 143 distillations. Therefore, the empiric minimum value is 0 kg of wastewater per kg of waste solvent. The empirical average was calculated to be 0.09 kg per kg of waste solvent, and the maximum was 0.68 kg (Figure 1). The polarity of the mixture has a significant influence on the amount of wastewater: only polar solvents form homogeneous mixtures with water. The average amount of wastewater for polar waste solvent mixtures is 0.126 kg per kg of waste solvent and 0.002 kg for nonpolar solvent mixtures. Both cases are described best by an exponential probability model. In some cases, waste solvent distillations require additional chemicals as ancillaries. If the mixture has a low pH, there is a risk of equipment corrosion (2). To avoid such corrosion, sodium hydroxide is added to the waste solvent mixture. In addition to the pH regulation, a small amount of chemicals may be needed to clean the equipment (e.g. methanol, acetone, or acetic acid). Cleaning is only necessary if the consecutive batch of waste solvent in the column is different from the previous one and if the remaining chemicals affect the subsequent distillation negatively. In our sample, it was not possible to distinguish between ancillary products used for pH regulation and equipment cleaning. Furthermore, if the boiling points of the components are too close and/or if components form an azeotrope,

entrainer needs to be added in order to separate the azeotropic mixture (2, 23). We only investigated polar solvents that formed an azeotropic mixture with water. In these cases, nonpolar solvents, namely toluene and cyclohexane, were used as an entrainer. For many distillations no ancillary products were needed. Azeotropic mixtures require an average of 0.27 kg of entrainer per kg of waste solvent, up to a maximum of 0.38 kg. Acid waste solvent mixtures with pH regulation and/or equipment cleaning require an average of 0.043 kg of sodium hydroxide or cleaning chemicals per kg of waste solvent (Table 3). The most suitable probability model for acid waste solvent mixtures is an exponential distribution, and for azeotropic mixtures a uniform distribution. Steam is used for heating the waste solvent. Low-pressure steam (5-15 bar, 150-210 °C with an average energy content of 2.8 MJ/kg) was used in all processes. Different heating equipment exists. Generally, indirect heating with a heating jacket is applied. Only in special cases is the steam directly injected into the waste solvent mixture. This latter procedure has the advantage of requiring less steam due to direct heat transfer. The downside, however, is that the injected steam becomes wastewater and needs to be treated at a wastewater treatment plant. Additionally, the equipment is exposed to high thermal stress. Only distillations with indirect heating were evaluated for the statistical analysis. On average, 1.4 kg of steam per kg waste solvent is needed (Figure 1). This is about five times more energy than the enthalpy of vaporization for the solvents (0.2-1.1 MJ/kg). One reason for that is that energy demand depends on the interaction and the difference of the boiling points between the main component and the secondary components. For instance, if the boiling points are close, the distillation will be conducted with a high reflux ratio. The consequence is that a large proportion of the output flow will be reboiled, which increases the steam demand. The reflux ratio is more important for the steam consumption, and, therefore, no statistically significant influence of the enthalpy of vaporization could be shown with the collected data. Technology, on the other hand, has a significant influence on the steam consumption. Batch distillations need an average of 1.5 kg of steam and continuous distillations 1.2 kg per kg of waste solvent (Table 3). One reason for the higher steam consumption of batch distillations is that all waste solvent components in the vessel have to be VOL. 39, NO. 15, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 2. Results of the case study. Case 1 (left bar, no information available), case 2 (middle bar, little information available), and case 3 (right bar, precise information available) show the applicability of the generic inventory data, including the 95% uncertainty intervals. evaporated and pass through the distillation column, whereas in case of continuous distillations the heavy fraction can be removed from the bottom. Additionally, the equipment has to be heated for every batch of waste solvent. Electricity is used for pumping, propulsion of the stirrer, injection of nitrogen, for the pull-off of outlet air, and the vacuum pump. Neither the technology nor the waste solvent composition has a significant influence on the electricity consumption (Table 3). The average electricity consumption is 0.033 kWh per kg of waste solvent. The sample can be described best using a log-normal distribution. One potential risk of distillations is the forming of vapor within the explosion limits. To avoid explosive vapor, inert gas (nitrogen) is injected into the columns and vessels before filling. All batch distillations and 32 out of 51 continuous distillations required nitrogen. The average use of nitrogen for batch distillation is 1 order of magnitude higher than for continuous distillations. The reason is that for batch distillations, nitrogen has to be injected for every batch of waste solvent. The use of nitrogen for batch distillations can be described with a log-logistic distribution, and for continuous distillations with an exponential distribution (Table 3). For all investigated distillations, cooling water was used as a cooling medium in the condenser. Groundwater with a constant temperature of about 13 °C was used. The use of cooling water is neither significantly influenced by technology nor by waste solvent composition. The average use of cooling water is 0.027 m3 per kg of waste solvent (Figure 1). The use of cooling water can be described by a log-normal distribution (Table 3). For the quantification of outlet air no measured data were available. Experts estimate the amount of outlet air to be 20-30 m3 per hour of operating time (16). Using this figure, the amount of outlet air was calculated for all distillation processes when information about operating time was available. The emission of outlet air was significantly higher for batch distillation (0.095 m3 per kg of waste solvent) than for continuous distillation (0.062 m3 per kg of waste solvent) (Table 3). Information about the composition of the outlet air is equally scarce. Experts estimate the carbon content at 10-20 g of carbon per m3 of outlet air (16). In most of the data-providing companies, the outlet air is collected and incinerated. Assuming full oxidation of the carbon, 0.0010.012 kg of CO2 per kg of waste solvent are emitted. Case Study. The applicability of the generic data ranges was shown with a case study on the distillation of a mixture 5890

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of toluene, methanol, carboxylic acid, and water (referred to as the toluene waste solvent mixture). The functional unit was defined as the distillation of 1 kg of waste solvent. Three cases of differing availability of information were distinguished. Case 1: We assumed that no information about the distillation process of the toluene waste solvent mixture would be available, except that toluene is recovered. This could be the case, for instance, in the state of planning of new production processes that generate toluene as waste solvent. In this case, the empirical results (Figure 1) were used to estimate minimum, maximum, and average values for every LCI-parameter. Case 2: Little information about the distillation is available. It is known that distillation is conducted batchwise and that ancillary products are used for equipment cleaning. This case represents a situation in which the distillation is already carried out by a company, but no specific measurements for that process were made. To estimate the values of the LCI parameters, the empirical maximum, minimum, and average values of the subsamples were used (Table 3). Case 3: Precise information is available for every LCI parameter. The toluene waste solvent mixture consisted of 98 wt % of toluene that is distilled to a final toluene content of 99.5 wt % (11, 24). The distillation was conducted as a batch process in a column equipped with trays under normal pressure. A small amount of methanol (0.036 kg/kg waste solvent) is used for equipment cleaning after the distillation. The amount of outlet air was calculated using an estimated value of 25 m3 outlet air per hour of operating time (16). These values may be used to validate the estimated values (cases 1 and 2). The LCI results of the single parameters for the three cases are depicted in Figure 2. The results of the case study show that the specific values of the toluene waste solvent (case 3) are always within the generic value ranges (cases 1 and 2). In case of recovered solvent, ancillary products, wastewater, steam, nitrogen, and outlet air, the value ranges become more precise the more information about the distillation is available. This is because the dependence of LCI parameters on the mode of operation and solvent properties could be taken into account in case 2 (see Table 3), while this was not possible with respect to case 1. In regard to electricity and cooling water, no improvement from case 1 to case 2 is achieved. This is because no subsamples could be defined for these LCI-parameters (group 1, Table 3). The value range

TABLE 4. Comparison of Generic Inventory Data of Our Study and Values Published by Geisler et al. (13) empiric result of our study parameter

unit

average

minimum

maximum

Geisler et al.

recovered solvent steam electricity nitrogen cooling water

kg/kg waste solvent kg/kg waste solvent kWh/kg waste solvent Nm3/kg waste solvent Nm3/kg waste solvent

0.71 1.4 0.03 1.5 × 10-3 2.7 × 10-2

0.31 0.4 0.01 0 0.1 × 10-2

0.97 3.0 0.10 3.8 × 10-3 7.9 × 10-2

0.95 1.5 0.05 0.01 8.0 × 10-2

of the residues is wider in case 2. In contrast to case 2, all points of data were considered to determine the 95% interval in case 1. Therefore, fewer extreme values were considered as outliers in case 2.

Discussion In the present study, generic inventory data for waste solvent distillation have been obtained from the analysis of industry data from 150 waste solvent distillation processes. This is an exceptionally comprehensive measurement of data in the context of chemical processes. The wide empirical basis has allowed for the application of statistical methods to determine empirical minimum, maximum, and average values as well as the fitting of probability distributions for all inventory parameters. These data may be used to estimate inventory parameter values and to conduct quantitative uncertainty analysis, such as Monte Carlo simulations, in LCA. Generic inventory data ranges, such as those proposed here, or so-called multi-input allocation methods (8, 9, 13, 25), are extremely valuable when primary data or data from inventory databases are not available. For instance, LCA practitioners are often faced with the problem of data being difficult to obtain from chemical companies due to confidentiality reasons. In such situations, generic inventory data ranges can be used as an approximation. Furthermore, our data are useful for the chemical industry itself. The generic data ranges illustrated in Figure 1 can be used as an approximation if no information about the distillation is available. This is the case in the state of planning or in the case the waste treatment is outsourced to external companies. More precise generic data ranges according to Table 3 can be used in early product stages, when some information is available, or in stage of operating in order to approximate unmeasured data. Especially mass and energy flows of minor economical importance (e.g. nitrogen or cooling water) are rarely measured. Generic inventory data are useful to bridge data gaps. Thus, they contribute to enhance the practicability of environmental assessments in industry and support decisionmaking processes. In most cases, the solvent treatment processes to decide upon in industry are distillation and incineration. There is a chance that the environmental assessment of a distillation based on our generic data ranges may not lead to a significantly different result compared to the corresponding results for incineration, because the environmental impact of the incineration may lie within the uncertainty range of the values for distillation. In such cases, additional effort may be needed to gather more precise information, e.g. by testing the distillation on a pilot installation. The applicability of generic inventory data in situations where the availability of information varies was illustrated in the case study on the toluene waste solvent mixture. The results show that the generic inventory data are suitable for estimating missing information. For most LCI parameters, estimation improved the more information about the distillation process was available. The data ranges proposed reflect the heterogeneity of the data since each distillation process has to be considered as

unique. Therefore, data ranges should always be considered, in addition to average values. The fitting of probability models to each subsample allows for the application of quantitative uncertainty modeling (e.g. Monte Carlo simulations). We thus correspond to the general trend of increasingly taking into account uncertainty and variability in LCA (e.g. refs 26-28). The choice of the system boundaries influences the data ranges as well. In this work, the system boundaries were comprised exclusively by the distillation process. In addition to the distillation process, storage tanks and piping are required. Electricity, nitrogen consumption, and the amount of wastewater (cleaning) may be higher than indicated in this study, due to the usage of pumps and avoidance of explosive vapor in tanks. Table 4 shows a comparison between the generic inventory data proposed by Geisler et al. (13) and this study. All values, except for cooling water and nitrogen, are within the range of the empirical minimum and maximum values we found. But the value for the use of cooling water corresponds to the rounded maximum value we calculated. The published value for the use of nitrogen is 1 order of magnitude higher than our result. The most probable reason for this are differences in the system boundaries as discussed above. The application of the generic values presented in this work is limited to distillation processes with similar feed properties and distillation equipment. The waste solvent mixtures we investigate in this study are comprised of solvents with boiling points between 50 °C and 200 °C. Mixtures containing compounds with boiling points below 50 °C require special equipment for charging columns and vessels to avoid volatilisation. The distillation of substances with boiling points above 200 °C often has to be conducted under vacuum conditions of less than 100 mbar. Again, such distillations require specific equipment. In particular the electricity consumption is higher compared to our distillations due to the need of powerful vacuum pumps. Moreover, some organic mixtures from industries, such as the paint or petrochemical industry, may contain a fraction of particle matter of more than 10 wt %. Such highly contaminated mixtures cannot be treated as efficiently as less polluted solvent mixtures. The results can be applied on monocolumns as a first approximation. However, these distillation columns are designed and optimized for one specific mixture and therefore display a better energy efficiency and higher recovery rates. Finally, distillation columns that allow the simultaneous distraction of multiple products at different heights of the column, as is often used in the petrochemical industry, may have different inventory parameter values from our distillations with one distillate per process step. The data of the LCI parameters are of varying quality. The data for recovered solvent, ancillary products, residues, and wastewater are based on good data, since these data were directly measured. Energy use (steam, electricity) and the data for nitrogen and cooling water were calculated based on measurements of entire buildings with multiple distillation columns. More accurately estimated data ranges for these parameters might be achieved by using directly measured data. In case of steam and electricity calculation, algorithms could be alternatively used to estimate energy consumption VOL. 39, NO. 15, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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(14). Concerning outlet air, calculations based on a precise mass-balance would be one possible method for achieving more exact data. Such improvements will be the subject of future research in this area.

Acknowledgments We would like to thank Valorec Services AG and Lonza Group Ltd. for providing data. We also gratefully acknowledged the Swiss Federal Office of Energy (Project No. 100065), Ciba Speciality Chemicals AG, Ems-Dottikon AG, Lonza Group Ltd., Novartis Pharma AG, Hoffmannn-La Roche AG, and Siegfried Ltd.’s funding of this project.

Literature Cited (1) Stichelmair, J. In Ullmann’s Encyclopedia of Industrial Chemistry, 7th electronic ed.; Wiley-VCH, Ed.; Wiley-VCH: Weinheim, 2002. (2) Perry, R.; Green, W. Perry’s Chemical Engineers Handbook, 7th ed.; Mc-Graw-Hill: 1998. (3) Cheremisinoff, N. Handbook of Chemical Processing Equipment; Butterworth-Heinemann: Boston, 2000. (4) Industrial Solvents Handbook; 5th ed.; Flick, E.-W., Ed.; William Andrew Publishing/Noyes Data Corporation: Westwood, NJ, 1998. (5) Eidgeno¨ssische Oberzolldirektion: Schweizer Aussenhandel 2002: Statistik nach Waren und La¨ndern (Statistics of Swiss External Trade 2002). 2003. (6) SGCI (Swiss Society of Chemical Industries): The Swiss Chemical and Pharmaceutical Industry. 2004. http://www.sgci.ch (7) Wypych, G. Handbook of Solvents; ChemTec Publishing: Toronto, 2001. (8) Seyler, C.; Hellweg, S.; Monteil, M.; Hungerbu ¨ hler, K. Life Cycle Inventory for Use of Waste Solvent as Fuel Substitute in the Cement Industry: A Multi-Input Allocation Model. Int. J. LCA 2004, 10 (2), 120-130. (9) Seyler, C.; Hofstetter, T. B.; Hungerbu ¨ hler, K. Life Cycle Inventory for Thermal Treatment of Waste Solvent from Chemical Industry: A Multi-Input Allocation Model. J. Clean. Prod. 2004, accepted. (10) Boughton, B.; Horvath, A. Environmental Assessment of Used Oil Management Methods. Environ. Sci. Technol. 2004, 38 (2), 353-358. (11) Hofstetter, T. B.; Capello, C.; Hungerbu ¨ hler, K. Environmental Preferable Treatment Options for Industrial Waste Solvent Management - A Case Study of a Toluene Containing Waste Solvent. TransIChemE 2003, 81 (B), 189-202. (12) IFEU - Institute for Energy and Environmental Research Heidelberg: Bewertung der Umweltvertra¨glichkeit von Entsorgungsoptionen - Methodenentwicklung und Durchfu ¨ hrung einer vereinfachten Bewertung und deren beispielhafte Ueberpru ¨ fung an vier Abfallarten. Ministerium fu ¨ r Umwelt und Verkehr, Reihe Abfall 63 2002. (13) Geisler, G.; Hofstetter, T. B.; Hungerbu ¨ hler, K. Production of Fine and Speciality Chemicals: Procedure for the Estimation of LCIs. Int. J. LCA 2004, 9 (2), 101-113.

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9

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(14) Bieler, P.: Analysis and Modelling of the Energy Consumption of Chemical Batch Plants, Dissertation ETH no. 15532. Environmental and Safety Technology Group, ETH Zurich 2004. (15) Jime´nez-Gonzalez, C.; Overcash, M.; Curzons, A. Waste Treatment Modules - a Partial Life Cycle Inventory. J. Chem. Technol. Biotechnol. 2001, 76 707-716. (16) Expert Panel of the project Waste Solvent Management in Chemical Industry consisting of Ciba Speciality Chemicals AG, Ems-Dottikon AG, Lonza Group Ltd., Novartis Pharma AG, Hoffmann-La Roche AG, Siegfried Ltd., and Valorec Services AG 2003-2004. http://www.sust-chem.ethz.ch/research/lifecycle/solvents.html (17) Huijbregts, M. A. J. Application of Uncertainty and Variability in LCA - Part I: A General Framework for the Analysis of Uncertainty and Variability in Life Cycle Assessment. Int. J. LCA 1998, 3 (5), 273-280. (18) Morgan, M. G.; Henrion, M. Uncertainty. A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis; Cambridge University Press: Cambridge, 1990. (19) Motulsky, H. Intuitive Biostatistics; Oxford University Press: New York, Oxford, 1995. (20) Vose, D. Risk Analysis: A Quantitative Guide, 2nd ed.; John Wiley & Sons: Chichester, 2000. (21) Palisade Corporation @Risk Software - Risk Analysis Add-in for Microsoft Excel, 4.5 ed.; Newfield, NY, 2001. (22) Graham Richard, C. Data Analysis for the Chemical Sciences: A Guide to Statistical Techniques; VCH Publishers: New York, 1993. (23) Smallwood, I. Solvent Recovery Handbook; Arnold: London, 1993. (24) Hofstetter, T. B. Abfalllo¨sungsmittelbewirtschaftung in der chemischen Industrie - Ein o¨kologischer Vergleich von Rektifikation und Verbrennung (Swiss Federal Office of Energy, Project no. 38368/78144). ETH Zurich 2002. http://www. energieforschung.ch (25) Hellweg, S.; Hofstetter, T. B.; Hungerbu ¨ hler, K. Modeling Waste Incineration for Life Cycle Inventory Analysis in Switzerland. Environ. Model. Assess. 2001, 6 (4), 219-235. (26) Geisler, G.; Hellweg, S.; Hungerbu ¨ hler, K. Uncertainty Analysis in Life Cycle Assessment (LCA): Case Study on Plant-Protection Products and Implications for Decision Making. Int. J. LCA 2005, 10 (3), 184-192. http://dx.doi.org/10.1065/lca2004.09.178. (27) Geisler, G.; Hellweg, S.; Liechti, S.; Hungerbu ¨ hler, K. Variability Assessment of Groundwater Exposure to Pesticides and its Consideration in Life- Cycle Assessment (LCA). Environ. Sci. Technol. 2004, 38 (16), 4457-4468. (28) Huijbregts, M. A. J.; Gilijamse, W.; Ragas, A. M. J.; Reijnders, L. Evaluating uncertainty in environmental Life-Cycle Assessment. Environ. Sci. Technol. 2003, 37, 2600-2608.

Received for review November 30, 2004. Revised manuscript received May 12, 2005. Accepted May 18, 2005. ES048114O