Framework for Sustainability Metrics - Industrial & Engineering

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Framework for Sustainability Metrics Anto´ nio A. Martins,* Teresa M. Mata, and Carlos A. V. Costa Faculdade de Engenharia, UniVersidade do Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

Subhas K. Sikdar National Risk Management Research Laboratory, Office of Research and DeVelopment, U.S. EnVironmental Protection Agency, 26 West Martin Luther King DriVe, Cincinnati, Ohio 45268

This work presents the application of a new framework for sustainability metrics to industrial processes, in particular, to chemical processes. The sustainability of an industrial process can be evaluated using a set of three-dimensional (3D) indicators that represent all three dimensions of sustainability: economic, environmental, and societal. The four 3D metrics proposed in this worksnamely, material intensity, energy intensity, potential chemical risk, and potential environmental impactsare applicable to a wide range of process systems. The first two metrics are associated with the process operation. The remaining two metrics, potential chemical risk and potential environmental impact, respectively represent chemical risk to human health in the process environment, and the potential environmental impact of the process on the surrounding environment. To illustrate this framework and the applicability of the proposed set of 3D metrics, two case studies are presented: chlorine production process using three different alternatives (membrane, diaphragm, and mercury cells), and the separation of an acetone/chloroform mixture by two different solvents (benzene and methyln-pentyl-ether). Results of this study show that this framework can be effective in selecting the more-sustainable process by comparing process alternatives. Introduction It is generally acknowledged that sustainability results from a balance among the three aspects of sustainable development: economic, environmental, and societal.1 With the rise of environmental awareness, because of external pressure, both legal and societal, manufacturing operations recently have attempted to improve their environmental impacts through the practice of improved process efficiency and waste minimization. As a result, environmental, economic, and societal benefits are being realized. Several attempts have been made to measure the progress of process technologies and manufacturing operations toward sustainability. For instance, BASF applied eco-efficiency metrics in making decisions on process alternatives that are better from economic and environmental viewpoints,2 i.e., by applying two of the three dimensions of sustainability. The CWRT (Center for Waste Reduction Technologies) of the AIChE (American Institute of Chemical Engineers) proposed a set of sustainability metrics that are quantifiable for industrial processes.3 For the corporate level reporting (i.e., at a level higher than the constituent processes), the Global Reporting Initiative4 proposed sustainability reporting guidelines for companies, regional or global. The Institution of Chemical Engineers (IChemE) proposed an elaborate list of indicators for industrial operations that was grouped into several categories.5 These indicators are applicable to a specific process or to the entire corporation. However, this list is too long and unwieldy for systematic application. Several other proposals for sustainable development indicators are noteworthy. Krajnc and Glavic6 offered a set of sustainability indicators for companies and a strategy for comparing them. These authors suggested aggregating these indicators into a * To whom correspondence should be addressed. Tel.: +351 914784483. Fax: +351 225081674. E-mail address: [email protected].

single score for supporting decision making. Although this aggregate indicator reflects the three dimensions of sustainability, the aggregate score does not allow for identification of the particular aspects of the activities that can be improved. Other suggestions7-9 for aggregate indicators can be also found in the literature. Azapagic and Perdan10 and Azapagic11 analyzed how companies can assess and measure their status and performance in sustainability issues. Their indicators are compatible with the general indicators proposed in the Global Reporting Initiative. They applied their findings to a case-study involving the mining and minerals industry.12 Ragas et al.13 considered the societal and scientific barriers to be overcome to measure sustainability and outlined a procedure to measure the sustainability of production systems. Afgan et al.14 presented a set of sustainability indicators for energy systems evaluations. Veleva and Ellenbecker15 suggested a methodology for expressing core and supplemental indicators of sustainable development for manufacturing systems. Coll16 proposed a strategy for evaluating the sustainability of chemical processes, using a set of indicators that was based on process data obtained using process simulators. Coll16 took into account the aspects of mass and energy consumption, process safety, and health by defining a large number of indicators for chemical processes. These are considered sequentially, starting with mass and energy indicators, followed by economic considerations. Published works on metrics (or indicators) for sustainability show that either (i) the chosen metrics are not truly reflective of all three aspects of sustainability; (ii) they are too many and, consequently, are difficult to apply; or (iii) both. An aggregate indicator does allow easy comparison between processes, but the loss of information in the analysis is not conducive to the adoption of specific measures for improvement. The use of a small set of quantifiable indicators offers the advantage of assessing technological or policy changes needed to make a manufacturing system more sustainable.17 Ideally, the chosen indicators should be independent of each other, in addition to

10.1021/ie060692l CCC: $37.00 © 2007 American Chemical Society Published on Web 09/27/2006

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Figure 2. General structure of the sustainability metrics framework.

Figure 1. Schematic depiction of the three dimensions of sustainability.

being small in number. The independence requirement makes it easy to change the definition of some indicators or the way they are calculated, when suitable, according to the characteristics and type of data available, without affecting the others. With the objective of providing an easy-to-implement method for applying indicators or metrics for the purpose of analyzing industrial systems for sustainability, Sikdar18,19 proposed a typology of indicators, considering the three dimensions of sustainability (Figure 1) in three distinct hierarchical groups: (1) One-dimensional (1D) indicators, which provide information about only one dimension of sustainability: economic, ecological, or societal; (2) Two-dimensional (2D) indicators, which provide information simultaneously about two dimensions of sustainability: socio-ecological, socio-economic, or economic-ecological; and (iii) Three-dimensional (3D) indicators or sustainability indicators, which provide information about all three dimensions of sustainability. As an illustration, we can consider one of AIChE metrics: energy intensity, which is the amount of nonrenewable energy used in making a unit mass of a product. This is arguably the most important metric one can use for a manufacturing process. However, it does not uniquely provide information about just economic, or environmental, or societal aspects. All three dimensions are integrated in nonrenewable energy intensity. Therefore, this metric is a 3D metric. On the other hand, the cost of manufacturing provides information about both societal and economic aspects and, as such, is identified as a socioeconomic metric. Similarly, nonhazardous waste is an economic indicator, because it represents a lost economic opportunity. The purpose of this work is not to exhaustively identify all possible metrics that are relevant for manufacturing processes, but rather to single out those that are most important for decision making. It is clear from the previously described typology that, to asses the contribution of manufacturing systems to sustainable development, one first must calculate the 3D metrics. In some instances, when all the chosen 3D metrics show an improvement of a system as a result of making changes, it may be possible to conclude that the improved process is more sustainable. On the other hand, if the results are ambiguous, it will be necessary to consider 2D metrics, and then 1D metrics, for decision making. This hierarchical procedure does not suggest what metrics one should use in a particular situation. The choice of these metrics is system-dependent. For instance, the number and type of metrics used for a chemical process would be somewhat different than those chosen for a manufacturing site that includes many processes and other services or operations. Nevertheless, it is important that, for a system under study, the 3D metrics are carefully chosen to cover the sustainable development concerns. Examples of 1D metrics are “gross domestic product” and “waste disposal costs” (economic);

Figure 3. Inputs and outputs crossing the system boundary.

pollutant emissions (environmental or ecological); and “employment rate” (societal). Examples of 2D metrics are “ecoefficiency indicators”, which represent economic and ecological dimensions; “waste generation” (especially hazardous), which represents societal and economic dimensions for populated areas; and “land use”, which represents societal and ecological dimensions in areas with high biodiversity. There are different methods available for calculating 1D and 2D metrics (for example, the WAR algorithm,20 life cycle assessment,3 life cycle costing, and total cost accounting21). Examples of 3D metrics are “energy intensity” and “material intensity”, which account for the use of resources required for economic growth and could have a significant ecological and societal impact, because their use usually creates wastes that can affect the environment and create health effects. In this work, the typology of metrics proposed by Sikdar18,19 is refined further for application as a sustainability metrics framework in real industrial processes, explicitly considering its hierarchical structure. The choice of 3D metrics proposed in this work is general for this type of processes, because it takes into consideration those aspects directly related to sustainability, in particular, the aspects linked to potential chemical risk (human health impact in the immediate environment of the process) and to potential environmental impact (hazardous to the surrounding environment). The proposed framework is illustrated by two case studies. Note that, in this work, the designations “3D indicators”, “sustainability indicators”, “indicators for sustainable development”, and “sustainability metrics” are interchangeable. Framework for Sustainability Metrics The hierarchical framework for sustainability metrics20 illustrated in this work is schematically represented in Figure 2. The use of this construct starts with the definition of the system under study (step 1) and its surrounding (Figure 3). The system inputs and outputs (energy, water, materials, product, coproducts, wastewater, gas emissions, solid wastes, etc.) that cross the system boundary are determined to quantify all the relevant metrics. The 3D metrics are calculated (step 2) based on the inventory analysis of the process under study. If the number of 3D metrics is sufficient to warrant a decision, an economic analysis must be performed to estimate the cost of any change in the process (step 3). If needed, 2D and 1D indicators are identified and calculated (step 4) based on specific information about the system. Finally, decisions for improving the process are made

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after the results of the metrics calculations and cost estimation have been analyzed (step 5). Characteristics of Sustainability Metrics. The selection of an adequate set of metrics is very important, for comparative analysis of versions of a process. The metrics must satisfy several main aspects,18,19,22-24 such as (i) a coherent set of quantifiable variables that are consistent with the principles of sustainability; (ii) they must be clear, simple, and unambiguous; and (iii) they must be representative of the physical system under study. One could fall on the temptation to consider a large set of metrics6 to cover all the relevant aspects of sustainability. However, the large effort of collecting the necessary data and the associated costs may limit their applicability. From a practical standpoint, a small set of metrics is desirable. Choosing Three-Dimensional (3D) Metrics. The typology proposed by Sikdar18,19 can be used to identify a suitable set of 3D metrics for industrial processes. Although it is not possible to define a “universal” set of 3D indicators applicable to all systems or processes, one can identify a small set valid for a wide range of conditions, making this framework attractive for comparison among different processes or design alternatives. The number of metrics that one can consider increases significantly from 3D to 2D and 1D. Metrics that account for only one aspect of sustainability are more site-specific and may be important for evaluating particular aspects of a system. They can also serve to support decision making and strategies when 3D metrics do not provide enough information. Starting from the extensive lists of indicators or metrics defined in the literature,5 one can classify them according to the typology previously presented. A pre-defined set of 3D metrics may not be enough in some cases. In some instances, the chosen set of 3D metrics may be sufficient to provide comparative benefits of a process, from sustainability viewpoint. Where this is not the case, appropriately chosen 2D and 1D metrics also should be considered, in conjunction with 3D metrics, to make decisions. These decisions, when implemented, should lead to processes that are “more sustainable” than the reference process. Thus, this analysis is essentially comparative, because it is not possible to use these metrics without a frame of reference. The availability and quality of information are normally the limiting factors for quantifying metrics. To assess the sustainability of processes, which is the focus of this work, as distinct from an enterprise with multiple processes, products, and site management, we can start with the extensive lists of metrics or indicators considered by AIChE4 and IChemE.5 The principles behind the metrics framework previously described can be used to sort among the existing indicators, to identify the relevant 3D metrics for a given process or system. For chemical processes, in most cases, four 3D metrics can be identified or defined to assess their comparative merits from a sustainability viewpoint. In most cases, a unit product or a time period of operation are the natural and more effective choices, in particular, the first. Using the ideas on which the hierarchy of metrics is based, first, 3D indicators are calculated and then, if needed, 2D and 1D indicators are identified and calculated based on specific information about the system. We have identified four 3D (or sustainability) metrics for our purpose: (1) Energy intensity: This indicator measures the energy demands of the process. It is calculated per unit mass of products and primarily focuses on the use of nonrenewable energy. (2) Materials intensity: This indicator measures the amount of nonrenewable resources required to obtain a unit mass of products. It includes raw materials, solvents, and other ingredients.

(3) Potential chemical risk: This metric connotes process safety and measures the potential risk to human health associated with the manipulation, storage, and use of hazardous chemical compounds in the process, and it is calculated per unit mass of products. (4) Potential enVironmental impact: This indicator measures the potential impact to the environment that is due to emissions and the discharge of hazardous chemicals to the environment, and it is calculated per unit mass of products. Note that, for a single-product process, the natural basis is a unit mass of that product, whereas for a multiple-product process, one can use moles instead of mass as the basis, or perform parallel calculations for each product. The first two indicators were picked directly from the IChemE and AIChE5,6 indicators list, because they are relevant in most chemical processes. The “energy intensity” and “materials intensity” can be calculated directly by dividing the total consumption of nonrenewable energy and materials, respectively, by the total mass of product. For the remaining two indicators, “potential chemical risk” and “potential environmental impact”, the calculation method involves a sequence of steps, which will be explained shortly. The choice of these four 3D metrics was made by considering the available metrics that are relevant to industrial processes and noting that these four represent, in some measure, the three sustainability aspects (environmental, economic, and societal). Energy use (especially nonrenewable) and material use are negative for the environment, because of the waste generated in their production and use, and they are positive for the economy, because of value creation; they are both positive and negative for the society, because this generation has a higher standard of living and future generations will be deprived because of their depletion, respectively. Thus, energy intensity and material intensity are 3D metrics. The remaining two metrics represent measures of potential chemical risk to human health within the system boundary and potential impact on the surrounding environment. As an example of the potential chemical risk, the use of a toxic chemical in a process may pose a risk to workers within the process boundary. The result could be a poor working environment (environmental), financial loss due to shutdown (economic), and illness (societal). In fact, it is estimated that, in the European Union (EU), the cost of accidents and illnesses represent ∼1.5%-3% of the gross domestic product (GDP) in each country.25 Contrary to this impact within the process environment, emissions from the facility to the external environmental can have similar impact on the larger surroundings, involving many more people outside the working environment. This latter impact is called potential environmental impact. The potential chemical risk and potential environmental impact are negative for the environment, negative for economic interest, and negative for society, because of the fact that these two factors are also 3D metrics, and the intent of a design should be to minimize them. The potential chemical risk and potential environmental impact explicitly consider the nature of the chemicals involved. The calculation of both these metrics is based on the assumption that the more dangerous a chemical, or the greater the quantity used, the larger the values of potential chemical risk and potential environmental risk associated with it. This observation comes from the well-known fact that risk is a function of the perceived level of hazard and potential exposure, which are normally assumed to be proportional to quantity and the level of use. Therefore, for each metric, the procedure includes

Ind. Eng. Chem. Res., Vol. 46, No. 10, 2007 2965 Table 1. Determination of Frequency Classa

Figure 4. Assessment of potential chemical risk and potential environmental impact.

a

Figure 5. Evaluation of potential chemical risk.

quantification of both the hazard, which is dependent on physical and chemical properties, and usage class, which is dependent on the quantity and frequency of use. This way, all the material and operational characteristics of the process are considered explicitly. In the next two sections, both methods are presented in detail. Evaluation of Chemical and Environmental Risks Potential chemical risk and potential environmental impact are determined as described in Vincent et al.,26 considering information about the composition of the process streams or the chemicals entering and leaving the system under study. The calculation method (Figure 4) comprises three elements: (i) inventory analysis; (ii) potential chemical risk and potential environmental impact assessment; and (iii) prioritizing and decision making. First, an inventory is prepared including all the chemicals used in the system under study. A detailed inventory analysis is essential to guarantee good data quality. For the inventory analysis, one needs to include the codes and names of the chemicals used in the production process, their quantities, frequency of use, and information about their hazard characteristics. Second, the potential chemical risk and the potential environmental impact are quantified. Although both procedures are similar, their description will be done separately, highlighting the main differences between them. Third, the values determined for the chemical and environmental risks are placed in a hierarchy to set priorities and facilitate decision making. Potential Chemical Risk Evaluation. The potential chemical risk evaluation method is schematically represented in Figure 5. The chemicals used in the process system under study are classified depending on their relative quantity, frequency of use, danger characteristics, and potential exposure. Thus, for each chemical, the designated “frequency class”, “quantity class”, “hazard class”, and “potential exposure class” are determined. The quantity class for a compound i is calculated for a time interval and is expressed as the ratio of the quantity of each chemical, qi, to the quantity of the most used chemical in the process, qmax. This way, five levels of quantity class are defined: 25 level 1 for q /q i max e 1%; level 2 for 1% < qi/qmax e 5%; level 3 for 5% < qi/qmax e 12%; level 4 for 12% < qi/qmax e 33%; and level 5 for qi/qmax > 33%. The quantity class is a relative value that is less than or equal to one, and it is useful for classifying different chemicals in a given process, regardless of their quantities. For the “frequency class”, five levels are defined, in terms of the duration of use of each chemical (see Table 1, reproduced

Reproduced from Vincent et al.26

from Vincent et al.26): level 0, when the use of the chemical compound has been interrupted for more than a year; level 1, for occasional use; level 2, for intermittent use; level 3, for frequent use; and level 4, for permanent use.26 Although many chemical processes are operated in continuous mode (corresponding to level 4 of frequency class), the growing importance of the batch processes requires consideration of the remaining levels of frequency class. The “hazard class” of each chemical is determined considering the risk phrases (R phrases) obtained from the Material Safety Data Sheet (MSDS) or from the security pictograms available in the packaging label. When the MSDS and packaging labels are not available, one can use the International Chemical Safety Cards (ICSC) that are available via the Internet (URL: www.ilo.org) to obtain the R phrases or the threshold limit value (TLV). This way, one guarantees that, in any situation, it is possible to identify the “hazard class” of the chemicals used. When a compound has more than one hazard class, according to the R phrases associated with it, the largest value is selected, to evaluate its potential chemical risk. In Table 2, which has been reproduced from Vincent et al.,26 five levels (from 1 to 5) of “hazard class” are defined, depending on the R phrases, the TLV, or the pictogram available in the packaging labels. For example, for chlorine, the R phrases indicated in the ICSC are R23 (which means toxic by inhalation), R36/37/38 (which means irritating to the eyes, respiratory system, and skin), and R50 (which means very toxic to aquatic organisms). According to Table 2, R23 corresponds to a hazard class of 4 and R36/ 37/38 corresponds to a hazard class of 2; R50 is not considered in Table 2, because it represents a risk to the environment and not a chemical risk to human health. Thus, for chlorine, the hazard class is 4, which is the higher value obtained from Table 2. For certain chemicals, depending on their hazard class, the potential chemical risk could be very high, even when the level of frequency class is occasional. For the “potential exposure class”, five levels (from 1 to 5) are defined, depending on the quantity class and frequency class that have been determined previously. Thus, for each chemical, the level of the potential exposure class is determined using Table 3 (which has been adapted from Vincent et al.26). For example, from Table 3, considering a chemical compound with a quantity class of 4 (first column) and a frequency class of 2 (first row), one obtains a potential exposure class of 4 (the value where the column and row intersect). Now, the potential chemical risk of each chemical compound is determined combining the potential exposure class with the hazard class previously determined, using Table 4, which has been adapted from Vincent et al.26 For example, from Table 4, considering a chemical compound with a hazard class of 4 (first column) and potential exposure

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Table 2. Determination of Hazard Classa

a

Reproduced from Vincent et al.26

Table 3. Determination of Potential Exposure Classa Potential Exposure Class quantity class

frequency class 0

frequency class 1

frequency class 2

frequency class 3

frequency class 4

5 4 3 2 1

0 0 0 0 0

4 3 3 2 1

5 4 3 2 1

5 4 3 2 1

5 5 4 2 1

a

Adapted from Vincent et al.26

class of 4 (first row), one obtains a potential chemical risk of 30 000. The determination of the potential chemical risk to human health allows for prioritization and measures to minimize it. Evaluation of Potential Environmental Impact. The method for the evaluation of potential environmental impact26 is similar to that for potential chemical risk, as represented in Figure 6. The chemicals are classified according to their relative quantity, hazard characteristics, physical state, and total potential environmental impact. However, the potential environmental impact of interest is dependent on the transfer to a receiver medium, (such as air, water, or soil) which is governed by physicochemical characteristics and transfer coefficients. The logic of the method is that we get an estimate of total environmental impact by just considering the quantity class and hazard class, per Vincent et al.26 However, a realistic impact evaluation is dependent on a transfer to appropriate environmental media. The latter is obtained by determining a transfer coefficient from the knowledge of the physical state and the receiving medium, as shown in Figure 6. The determination of quantity class here is similar to that for potential chemical risk.26 As described previously, five levels

(from 1 to 5) are defined, expressed as the ratio of the quantity of each chemical, qi, to the quantity of the most-used chemical in the process, qmax. For hazard class, five levels (from 1 to 5) are defined, according to the R phrases of each chemical, as shown in Table 5, which has been adapted from Vincent et al.26 Considering again the example of chlorine, the risk phrase R23 (toxic by inhalation) corresponds to a hazard class of 4, the risk phrase R36/37/38 (irritating to the eyes, respiratory system, and skin) is not represented in Table 5 and the risk phrase R50 (very toxic to aquatic organisms) corresponds to a hazard class of 5. In regard to the evaluation of the potential chemical risk, the highest value of the hazard class is used in potential environmental impact evaluation. This result also shows that, for the same chemical, the hazard class may be different when calculating the two metrics. Only chemicals whose quantity is greater than a certain threshold, for a representative period of time, are considered in the potential environmental impact calculations. This quantity threshold, as determined in the work of Vincent et al.,26 is defined in Table 6, as a function of the hazard class and the category of the chemical (product, substance, preparation, or residue). The identification of chemical category may be dependent on an applicable legislation or on the characteristics of the process or system under study. For example, for chlorine with a hazard class of 5, the potential environmental impact is only evaluated if the total quantity used or produced in the process is larger than 5 kg. The total potential environmental impact for each chemical then is determined according to Table 7 , which has been adapted from Vincent et al.,26 knowing that the quantity class

Ind. Eng. Chem. Res., Vol. 46, No. 10, 2007 2967 Table 4. Determination of Potential Chemical Riska Potential Chemical Risk

a

hazard class

potential exposure class 1





5 4 3 2 1

100 30 10 3 1

1000 300 10 30 10

1000 3000 1000 300 100

100000 30000 10000 3000 1000

1000000 300000 100000 30000 10000

Adapted from Vincent et al.26 Table 6. Definition of Quantity Thresholds in Relation to the Hazard Class and the Category of the Chemical Compounda

Figure 6. Evaluation of potential environmental impact. Table 5. Determination of Hazard Class, Using Risk Phrases and Combinationsa risk phrases and combinations Hazard Class 1

hazard class

category

threshold (kg)

5, 4, 3 2, 1 5 2, 1

product, substance, or preparation product, substance, or preparation residue residue

5 100 100 500

a

Table 7. Determination of Total Potential Environmental Impacta Total Potential Environmental Impact hazard class

quantity class 1

quantity class 2

quantity class 3

quantity class 4

quantity class 5

5 4 3 2 1

2000 100 10 2 1

5000 1000 30 5 1

10000 2000 100 10 2

30000 5000 1000 30 5

10000 10000 2000 100 10

none the residue type is not mentioned on the residue classification list nonhazardous industrial waste Hazard Class 2 R66, R67 the type of residue is mentioned (with an asterisk) in the residues classification R29 and accidental probability of a contact with water R31 and accidental probability of a contact with acid Hazard Class 3 R20, R21, R22, R33, R36, R37, R38 R40/20, R40/21, R40/22, R40/20/21/22, R48/20, R48/21, R48/22 R48/20/21, R48/20/22, R48/21/22, R48/20/21/22 R52, R53, R52/53, R65 R29 and R32 and accidental probability of a contact with water R31 and accidental probability of a contact with acid Hazard Class 4 R23, R24, R25, R34, R35, R40, R41, R42, R43, R48 R48/23, R48/24, R48/25, R48/23/24, R48/23/25, R48/24/25 R48/23/24/25, R51, R51/53, R54, R55, R56, R57, R58, R59 R62, R63, R64 R29 and permanent probability of a contact with water R31 and permanent probability of a contact with acid R32 and occasional probability of a contact with acid Hazard Class 5 R26, R27, R28 R39/23, R39/24, R39/25, R39/23/24, R39/23/25, R39/24/25, R39/23/24/25, R39/26, R39/27, R39/28 R39/26/27, R39/26/28, R39/27/28, R39/26/27/28 R45, R46, R49 R50, R50/53 R60, R61 the type of residue is mentioned (with an asterisk) in the residues classification R32 and permanent probability of a contact with acid a

Data taken from Vincent et al.26

and the hazard class have been previously determined. For example, from Table 7, for a chemical compound with a hazard class of 5 (first row) and a quantity class of 4 (first column), the total potential environmental impact is 30 000. The total potential environmental impact thus obtained is an aggregated value valid for a particular chemical, because it does not yet consider the impact per receiving medium. Therefore, after the total potential environmental impact has been deter-


a


Table 8. Transfer Coefficients, Realtive to the Chemical’s Physical State and the Receiving Mediuma Transfer Coefficient physical state

receiving medium is air

receiving medium is water

receiving medium is soil

gas liquid solid solid as a powder

0.95 0.5 0.001 0.1

0.05 0.35 0.005 0.85

0.001 0.002 0.005 0.005

a


mined, one can determine the potential environmental impact on air, water, or soil, by knowing the transfer coefficients of each chemical in each receiving medium. Similar to what we did for potential chemical risk, by comparing the relative values of total potential environment impact, it is possible to assign priorities when considering actions to reduce or eliminate the impact that is due to the use of certain chemicals. As shown in Table 8, which has been adapted from Vincent et al.,26 considering the physical state (gas, liquid, solid, or powdered solid) and the receiving medium (air, water, or soil), one can obtain the transfer coefficients of each chemical.26 For example, for a liquid, under process operation, the transfer coefficients for air, water, and soil are 0.5, 0.35, and 0.002, respectively. The transfer coefficients of Table 8 are average values that could be refined if more information is available about the transfer and dispersion of the chemicals in the various media, in particular, concerning the distribution of the mass of chemicals in the different receiving media. Finally, multiplying the transfer coefficients by the previous total value of the potential environmental impact, one can determine the potential environmental impact per receiving medium, which is the quantity of interest for this metric. For

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Figure 7. Chlorine production process alternatives with (a) mercury cells, (b) diaphragm cells, and (c) membrane cells.

example, for a total potential environmental impact of 30 000 and transfer coefficients of 0.95, 0.05, and 0.001 in the air, water, and soil, respectively, the potential environmental impact is 28500 on air, 1500 on water, and 300 on soil. Case Studies To illustrate the application of the methodology proposed in this work, two case studies have been presented: chlorine production process via three different alternatives (using membrane, diaphragm, and mercury cells) and acetone/chloroform mixture separation process via two different solvents (benzene and methyl-n-pentyl-ether). For the chlorine production process, data were obtained from a life cycle inventory database.27 For the separation of acetone/chloroform mixture, a chemical process simulator16 was used to generate the required data. Case I: Chlorine Production. (1) Process Description. Chlorine is used as a cleaning agent in metal degreasing and dry cleaning, as a bleaching agent in the papermaking industry, and also as a disinfectant for domestic use.29 According to the World Health Organization, chlorine helps to control infectious diseases and thus increases human life expectancy.30-32 Chlorine has an annual production of ∼40 million tons per year, and its use has been increasing, placing it among the top 10 chemicals, according to volume, globally.33

The raw material for the production of chlorine is sodium chloride (NaCl). In addition to chlorine, caustic soda (NaOH) and hydrogen are also produced as coproducts. Three process alternatives may be considered for chlorine production34 with membrane, diaphragm, and mercury cells (Figure 7). The mercury cells process was the first one to be developed. It produces very pure NaOH solutions. Despite health concerns that are due to mercury emissions from this process, some mercury plants are still in operation (see Figure 7a). The diaphragm cells process is based on the use of asbestos inside the cell to separate the anode and the cathode solutions. This process requires less pure brine than the mercury process, but it is less efficient due to the occurrence of side reactions. The resulting NaOH solution is less pure and requires further purification. Although not as important as the other two process alternatives, the diaphragm cell process accounts for 15% of the total European production (see Figure 7b). The membrane cells process was developed in the 1970s. It uses a cation-exchange membrane to selectively control the flow of ions during electrolysis. It represents ∼28% of the total world production with an increasing trend. This process can produce high-purity chlorine and NaOH solutions. The requirements for brine purity are very high, in comparison with the other two process alternatives (see Figure 7c).

Ind. Eng. Chem. Res., Vol. 46, No. 10, 2007 2969 Table 9. Mass Flow, qi/qmax Ratio, and Quantity Class for the Process Alternatives Mercury Cells component water, H2O rock salt, NaCl chlorine, Cl2 caustic soda, NaOH hydrogen, H2 CFC-10 CFC-21 mercury dichloromethane asbestos chloride, Clbromide, Brhypochlorite, HClO-

mass flow (g/kg Cl2)

Diaphragm Cells

Membrane Cells

qi/qmax

quantity class


qi/qmax

quantity class

0.31 1.00 0.61 0.68 0.02

4 5 5 5 2

507 1647 1000 1123 29.7 0.0060 0.0170

0.31 1.00 0.61 0.68 0.02 0.00 0.00

4 5 5 5 2 1 1

0.0035 0.0370

0.00 0.00

1 1

0.0053

0.00

1

0.0050

0.00

1

0.0002 0.3800 0.0076 0.008

0.00 0.00 0.00 0.00

1 1 1 1

507 1647 1000 1123 28.1


qi/qmax

quantity class

0.31 1.00 0.61 0.68 0.02

4 5 5 5 2

0.0175

0.00

1

0.0006

0.00

1

507 1647 1000 1123 29.2

Table 10. R Phrase(s) and Hazard Class for the Process Alternatives Hazard Class component

R phrase(s)

water, H2O rock salt, NaCl chlorine, Cl2 caustic soda, NaOH hydrogen, H2 CFC-10 CFC-21 mercury dichloromethane asbestos chloride, Clbromide, Brhypochlorite, HClO-

none none R23-36/37/38/38-50 R35 R12 R: 23/24/25-40-48/23-52/53-59 none (TLV ) 10 ppm) R: 23-33-50/53 R: 40 R: 45-48/23 none R: 26-35-50 R: 31-36/38

(2) Evaluation of the Chlorine Process Alternatives. The input and output data for the purpose of this evaluation were obtained from a life cycle inventory database,27 for a functional unit of one kilogram of chlorine produced. The system boundary for each process alternative is represented in Figure 7. The main inputs include cooling water, rock salt, and electrical energy. The main outputs include Cl2, NaOH, and H2. Emissions to air, water, and soil for each alternative are as follows: Mercury process: The main emissions to air include Cl2, H2, mercury, and dichloromethane. The main emissions to water include chloride ions, hydrochloride ions, and mercury. No significant emissions to soil are reported. Diaphragm process: The main emissions to air include Cl2, H2, and CFC-10 and CFC-21 chlorofluorocarbons. The main emissions to water include chloride ions, hydrochloride ions, bromide ions, and asbestos. The main emission to soil is asbestos. Membrane process: The main emissions to air are Cl2, H2, and CFC-21. The main emissions to water consist of chloride ions. No significant emissions to soil are reported. For each process alternative, the four 3D metrics previously identified were calculated. The energy and material intensity metrics were estimated directly from the input and output data available for the three process alternatives. The energy intensity, which is the consumption of energy per kilogram of chlorine produced, was determined as 11.70, 10.80, and 10.98 MJ/kg for the mercury, diaphragm, and membrane cell processes, respectively. The material intensity, which is the sum of the rock salt and water consumed in the process, is 2.15 kg per kg of chlorine produced, which was the same for all processes, according to the data presented in Table 9. For the calculation of the potential chemical risk and potential environmental impact, only the inputs and outputs that cross

mercury cells

diaphragm cells

membrane cells

0 0 4 4 1

0 0 4 4 1 4 3

0 0 4 4 1 3

4 3 1 2

4 1 1 2

1

the system boundary were considered,26 because no information on the mass flows of the process streams inside any of the process alternatives was available. The results are shown in the next two subsections. (3) Evaluation of Potential Chemical Risk. For the determination of this 3D metric, the quantity class was determined based on the ratio of the quantity of each chemical, qi, to the quantity of the most-used chemical in the process, qmax. Table 9 shows the mass flow for the inputs and outputs26 for each process alternative, qi/qmax, and the quantity class. Then, the R phrases for each chemical, obtained from the ICSC, were used to determine the hazard class, as presented in Table 10. The frequency class for all the process alternatives is 4 (or permanent), because the processes are continuous. The potential exposure class was determined using the quantity class and the frequency class, according to Table 3 (note that this a doubleentry table, which means that two values must be known in order to determine the third). Finally, the potential chemical risk was determined by combining the hazard class and the potential exposure class (Table 11). Summing the individual values of the potential chemical risk (Table 11), it is possible, at this stage, to compare among the chlorine production processes. Also, priorities of action to minimize the potential chemical risk can be identified based on those specific values. Thus, we find that the emissions for the membrane process are much lower. (4) Evaluation of the Potential Environmental Impact. For the evaluation of the potential environmental impact, the quantity class and frequency class were determined similarly to the potential chemical risk. Thus, the quantity class was determined

2970


Table 11. Potential Exposure Class and Potential Chemical Risk for the Process Alternatives Potential Exposure Class

Potential Chemical Risk

component

mercury

diaphragm

membrane

mercury

diaphragm

membrane


5 5 5 5 2

5 5 5 5 2 1 1

5 5 5 5 2

0 0 100000 100000 3

0 0 100000 100000 3 1000 1000

0 0 100000 100000 3

1

1 1

1000 100 1 1 1 1

1 1

1

10

Table 12. Hazard Class and Potential Environmental Impact for the Process Alternatives component

hazard class


0 0 5 4 1 4 5 5 4 5 1 1 5

Potential Environmental Impact mercury diaphragm membrane 0 0 100000 100000 1

0 0 100000 100000 1 100 2000

0 0 100000 100000 1 2000

2000 100 1 2000

1

2000 1 1 2000

1

Table 13. 3D Indicators for the Process Alternatives Process Alternatives 3D indicator

mercury cells

diaphragm cells

membrane cells

energy intensity (MJ/kg Cl2) material intensity (kg/kg Cl2) potential chemical risk potential environmental impact

10.80 2.15 202114 116102

10.98 2.15 202116 116103

11.70 2.15 200104 112002

based on the ratio of the quantity of each chemical, qi, to the quantity of the most-used chemical in the process, qmax, as shown in Table 9. Because the chlorine production facilities are very large, with productions in the range of thousands of tons, one can assume that the mass of each chemical is well above the threshold values (presented in Table 6) for a representative period of time. The hazard class was determined according to Table 5, based on the R phrases of each chemical present in the process. The potential environmental impact of each chemical compound then was determined by combining the quantity class and the hazard class, using Table 12. Similar to the evaluation of the potential chemical risk, the total potential environmental impact of each chemical is summarized, to compare the different process alternatives, as shown in Table 13. By multiplying the transfer coefficients (Table 8) by the previous total value of the potential environmental impact, one can determine the potential environmental impact per receiving medium (air, water, or soil). However, because the potential environmental impacts of all processes are mainly associated with chlorine and NaOH, the definition of priorities according to the previously described method is not particularly relevant here. (5) Sustainability Considerations for the Chlorine Process Alternatives. Table 13 summarizes the values of the 3D (or

2000 1 1 2000

1

sustainability) indicators for the three chlorine production process alternatives calculated previously. The energy intensity is higher for the membrane cells process than the other two alternatives. The material intensity is the same for all three alternatives. The potential chemical risk and the potential environmental impact are higher for the diaphragm cells and mercury cells processes, because of the use of asbestos and mercury, respectively, both of which have a high human toxicity and environmental impact. In this analysis, we only considered the 3D metrics, because no information was available on cost and on any of the 2D or 1D metrics. Given such information, we could consider the merit of their inclusion and their importance in supporting conclusions on sustainability. Within the context of our choice of metrics, because the measures of the metrics are not very different for the process alternatives, the trend of substitution of both the mercury and asbestos cells by membrane cells can be understood from health and environmental concerns. Our study was limited to the emissions data only from the system boundary. It is possible that releases from inside the process streams, especially for mercury, could make the results sharply in favor of the membrane cells. However, the high costs of membrane equipment, the need of substantial changes in the process, and the absence of a strong advantage in terms of operational efficiency (as shown in the material and energy intensity metrics) constitute an obstacle to the change to membrane cells. Case II: Separation of an Acetone/Chloroform Mixture. (1) Process Description. Acetone and chloroform are used as solvents and intermediates for other chemicals, such as methyl methacrylate and bisphenol A (using acetone), and for the production of hydrochlorofluorocarbons (HCFC) (using chloroform). Acetone is on the top-50 list of chemicals, in terms of quantity produced per year.32 Both compounds can occur as mixtures in processes, such as in the production of chloroform. Chloroform can be produced using sodium, calcium hypochlorite, and acetone, where, in the final part of the process, the separation of the acetone/chloroform mixture is needed. The process flowsheet for the separation process is presented in Figure 8.16 The separation process is identical for either benzene (reference process) or methyl-npentyl-ether (proposed alternative), which are used in aiding in the separation. The system boundary is represented in Figure 8, including two distillation columns and a solvent recycle.16 Acetone and chloroform are obtained as products at the top of the first and second columns, respectively. A purge is included in the process to avoid the buildup of impurities that can compromise the separation of an acetone/chloroform mixture. In both separation options, the process streams and the respective compositions data were determined using a chemical


Figure 8. Schematic depiction of the separation process of an acetone/chloroform mixture. Table 14. Flow Rates and Compositions of the Process Streams Mass Flow (kg/h)

component solvent (at 300 K, 1 atm) acetone (at 300 K, 1 atm) chloroform (at 300 K, 1 atm) solvent solvent solvent acetone acetone acetone chloroform chloroform chloroform solvent acetone chloroform

reference alternative process process stream (benzene) (methyl-n-pentyl-ether) solvent feed feed 2 4 7 2 4 7 2 4 7 6 6 6

2.89 × 103 5.81 × 103 1.19 × 104 5.70 × 101 3.77 × 101 5.58 × 103 5.76 × 103 4.48 × 101 1.6 × 10-3 4.70 × 100 1.18 × 104 2.90 × 102 5.30 × 104 1.57 × 10-2 2.76 × 103

8.17 × 101 5.81 × 103 1.19 × 104 1.01 × 100 5.17 × 10-8 8.07 × 101 5.63 × 103 1.78 × 102 2.35 × 10-7 3.64 × 102 1.16 × 104 1.18 × 100 4.03 × 104 1.17 × 10-3 5.89 × 102

process simulator.16 Fugitive emissions, as may occur in the process, were ignored, because of a lack of data.35 Methyl-npentyl-ether was identified as a superior alternative, using a computer aided molecular design program.16 Process simulations for both solvents were performed for an equimolar acetone/ chloroform mixture and a total molar flow rate of 200 kmol/h. The simulation results are presented in Table 14. The value of the energy intensity metric obtained from the process simulation is 98 758 MJ/h for the reference process (using benzene) and 40 919 MJ/h for the proposed process (using methyl-n-pentyl-ether). The alternative process requires less energy than the reference process. This is because the alternative process flow rates are smaller than those of the reference process, which reduces the energy consumption and the costs associated with the process equipment operation. Also, the space needed for storage of the solvents is smaller. The material intensity indicator was taken to be the quantity of solvent required to perform the separation. For the reference and alternative processes, its value is equal to 2890 kg/h and 81.7 kg/h, respectively. The energy and material intensity were calculated directly using the simulation data. The calculation of potential chemical risk and potential environmental impact was not direct, but the calculation was easy to perform. Their results are described in detail in the next subsections. (2) Evaluation of Potential Chemical Risk. In accordance with the methodology described in the flowsheet of Figure 5, the potential chemical risk was determined for both processes. According to the logic previously presented, first one must determine the hazard class, quantity class, and frequency class. Then, from the quantity class and frequency class, we determine

the potential exposure class. Finally, by combining the hazard class and the potential exposure class, we obtain the potential chemical risk. The hazard class for each chemical, as presented in Table 15, was determined according to the R phrases of the chemicals obtained from the ICSC and using Table 2. Table 16 shows the quantity class, which is calculated based on the ratio of the quantity of each chemical, qi, to the quantity of the most-used chemical in the process, qmax. The data used are obtained using process simulation.16 Assuming that the process operates in continuous mode, the frequency class for both process alternatives is 4 or permanent. Using Table 3, by combining the quantity class and the frequency class for each chemical component, one determines the potential exposure class (Table 17). Finally, the potential chemical risk for each chemical, which is presented in Table 18, is determined by combining the hazard class and the potential exposure class, according to Table 4. The priorities shown in Table 18 were identified according to Vincent et al.,26 who defined three levels of priority as a function of the potential chemical risk of a chemical: weak if the value is 10 000. These values serve as guidelines, and they may change according to the process characteristics and the objectives of the study. Note that the priorities given are particular to one process, and they cannot be used to assess if a process is more sustainable than another. As in the chlorine production case study, to compare the processes, one must sum the potential chemical risk values of each chemical. For the reference and alternative processes, the total values of the potential chemical risk are 3101 and 1200, respectively. Thus, the results show that the potential chemical risk for the alternative process (using methyl-n-pentyl-ether) is smaller than that of the reference process (using benzene), which allows one to claim that the alternative process is safer, in terms of potential chemical risk. (3) Evaluation of the Potential Environmental Impact. The determination of the potential environmental impact follows the same procedure used in the previous case study. First, one must determine the hazard class and quantity class to evaluate the “total potential environmental impact”. Then, knowing the physical state and the receiving medium (air, water, and soil) of each chemical, one determines the transfer coefficient in each receiving medium. Finally, by multiplying the transfer coefficient by the total potential environmental impact, one obtains the desired potential environmental impact on air, water, or soil. According to the methodology described previously, the quantity class is calculated based on the ratio of the quantity of

2972


Table 15. R Phrase(s) and Hazard Class for the Process Alternatives Reference Process (Benzene)

Alternative Process (Methyl-n-pentyl-ether)

component

R phrase(s)

hazard class

acetone solvent chloroform

R: 11 R: 45-46-11-36/38-48/23/24/25-65 R: 22-38-40-48/20/220

1 4 3

R phrase(s)

hazard class

R: 11 none R: 22-38-40-48/20/220

1 1 3

Table 16. Ratio qi/qmax and Respective Quantity Class Reference Process (Benzene)


component

mass flow (kg/h)

qi/qmax

quantity class

mass flow (kg/h)

qi/qmax

quantity class


5810.0 55922.8 14696.3

0.10 1.00 0.26

3 5 4

5810.0 40366 12529

0.14 1.00 0.31

2 5 4

Table 17. Potential Exposure Class

Table 20. Potential Environmental Impact for the Receiving Medium

Potential Exposure Class component

reference process (benzene)

alternative process (methyl-n-pentyl-ether)


1 5 4

2 5 5

Table 18. Potential Chemical Risk of the Compounds Used Reference Process (Benzene) component

potential chemical risk


1 100 3000


priority

potential chemical risk

priority

weak medium strong

100 100 1000

weak weak medium


component

water

air

soil

water

air

soil


0.70 10500 1750

1 1500 2500

0.0014 21 3.5

0.35 3.5 1750

0.5 5 2500

0.0007 0.07 3.5

Table 21. 3D Indicators for Both Process Alternatives Process Alternatives

Table 19. Hazard Class and Potential Environmental Impact for the Process Alternatives Reference Process (Benzene)

Potential Environmental Impact Reference Process (Benzene)


component

hazard class

potential environmental impact

hazard class

potential environmental impact


1 4 4

2 30000 5000

1 1 4

1 10 5000

each chemical, qi, to the quantity of the most-used chemical in the process, qmax. The hazard class is determined using Table 5, considering the R phrases of each chemical available in the ICSC. Table 19 presents the hazard class for both processes. The total potential environmental impact then is determined according to Table 12, by combining the quantity class and hazard class. These values are shown in Table 7 for each chemical. To compare the processes, one can sum the potential environmental impact of each chemical in the process. The values are 35 002 and 5011 for the reference and alternative processes, respectively. These results show that the potential environmental impact for methyl-n-pentyl-ether is smaller than that of benzene. Taking into account the physical state of the chemicals, it was also possible to evaluate the potential environmental impact in each receiving medium (water, air, or soil). Multiplying the transfer coefficients presented in Table 8 and the potential environmental impact for each chemical, the potential environmental impacts per receiving medium can be easily determined, and they are presented in Table 20. (4) Comparison between Solvents. At this time, it is possible to summarize the results of the 3D analysis. This is shown in

3D indicators energy intensity material intensity potential chemical risk potential environmental impact

reference process alternative process (benzene) (methyl-n-pentyl-ether) 98758 MJ/h 2890 mol/h 3101 35002

40919 MJ/h 81.7 mol/h 1200 5011

Table 21. This table clearly shows that the proposed alternative process, using methyl-n-pentyl-ether, is superior, in terms of all four sustainability metrics. Finally, if the cost of the alternative solvent does not make it uncompetitive, this is a valid recommendation for a more sustainable separation of chloroform and acetone. This analysis does not take into account the capital costs that are associated with the solvent substitution. For this reason, it is recommended to support this study with a cost analysis. The conclusion of a preference for the proposed process in this study resulted from simply calculating four 3D metrics. The same conclusion was determined for this process by Coll,16 who used a more complex methodology. Conclusions In this work, a simple methodology is presented to evaluate the sustainability of an industrial process. A set of four threedimensional (3D) sustainability metrics is proposed, including material intensity, energy intensity, potential chemical risk, and potential environmental impact. To validate this methodology, two case studies were presented. In both cases, the necessary data were obtained from literature or using commercial process simulators. The methodology was based on a hierarchical classification of metrics, in terms of 3D, two-dimensional (2D), and one-dimensional (1D) metrics, and on the suggestion that we evaluate the 3D metrics first and examine 2D and 1D metrics as may be needed for special considerations or if the 3D metrics did not allow clear decision making. In the case studies, the first one, which concerns the making of chlorine, is an illustration where the process options did not provide as clear a direction as one would like and it would require one to examine 2D and 1D metrics, in that order. In the second case, however,


the proposed solution from the 3D analysis provided a clear preference for sustainability, subject only to cost estimation. The proposed framework allows one to easily and quickly evaluate the sustainability of industrial processes. The proposed 3D indicators are quite universal to permit its application to a wide variety of industrial processes. In particular, the inclusion of potential chemical risk and potential environmental impact as indicators addresses the need to account for the risk to human health and the impact on the environment, which are related to economic and social costs. Future work must be directed to identify a collection of these metrics from which to choose, for a particular process under consideration. System definition determines the number and type of metrics that one requires. The scope of this work was limited to evaluating the relative sustainability merits of a process among alternatives. The four 3D metrics that we selected served this purpose. However, for manufacturing enterprises that involve many processes at a site, or at multiple sites, other 3D metrics may be needed. We did not attempt to identify all possible 3D, 2D, and 1D metrics that one needs to complete a study. The illustration showed that enough decision making may be possible by considering only the 3D metrics first, and if needed, extend the consideration to 2D and, eventually, 1D metrics. Acknowledgment Authors would like to acknowledge the support of Professor Rafiq Gani of Technical University of Denmark, for providing a copy of the M.Sc. Thesis of Nuria Coll. Literature Cited (1) Our Common Future, Bruntdland Commission (formerly the World Commission on Environment and Development, WCED), Rio de Janeiro, Brazil, 1987; Oxford University Press: Oxford, U.K., 1987. (2) Saling, P.; Kicherer, A.; Kramer, B. D.; Wittlinger, R.; Zombik, W.; Schmidt, I.; Schott, W.; Schmidt, S. Eco-Efficiency Analysis by BASF: The Methodology. Int. J. Life Cycle Assess. 2002, 7 (4), 203. (3) GRI - Global Reporting Initiative, Sustainability Reporting Guidelines 2002 on Economic, Environmental and Social Performance, Boston, MA (available via the Internet at http://www.globalreporting.org). (4) Center For Waste Reduction, Technologies Focus Area: Sustainability Metrics, 2004 (available via the Internet at http://www.aiche.org/ cwrt/pdf/BaselineMetrics.pdf, 2004). (5) IChemE.; Sustainable Development Progress Metrics Recommended for use in the Process Industries, 2004 (available via the Internet at http:// www.icheme.org/sustainability/metrics.pdf). (6) Krajnc, D.; Glavic, P. How to compare companies on relevant dimensions of sustainability. Ecol. Econ. 2005, 55, 551. (7) Narodoslwasky, M.; Krotscheck, C. Integrated Ecological Optimization of Process with the Sustainable Process Index. Waste Manage. 1995, 20, 599. (8) Narodoslwasky, M.; Krotscheck, C. The Sustainable Process Index (SPI): Evaluating Processes According to Environmental Compatibility. J. Hazard. Mater. 1995, 41, 383. (9) Krotscheck, C.; Narodoslwasky, M. The Sustainable Process Index. A New Dimension in Ecological Evaluation. Ecol. Eng. 1996, 6, 241. (10) Azapagic, A.; Perdan, S. Indicators of Sustainable Development for Industry: A General Framework. Process Saf. EnViron. Prot. 2000, 78 (B4), 243. (11) Azapagic, A. Systems Approach to Corporate SustainabilitysA General Management Framework. Process Saf. EnViron. Prot. 2003, 81 (B1), 2003.

(12) Azapagic, A. Developing a Framework for Sustainable Development Indicators for the Mining and Minerals Industry. J. Clean. Prod. 2000, 12, 639. (13) Ragas, A.; Knapen, M.; Heuvel, P.; Eijkenboom, R.: Buise, C.; Laar, B. Towards a Sustainability Indicator for Production Systems. J. Clean. Prod. 1995, 3, 123. (14) Afgan, N.; Carvalho, M.; Hovanov, N. Energy System Assessment with Sustainability Indicators. Energy Policy 2000 28, 602. (15) Veleva, V.; Ellenbecker, M. Indicators of Sustainable Production Framework and Methodology. J. Clean. Prod. 2001, 9, 519. (16) Coll, N. Development of an Indicator-Based Retrofit Design Method, M.Sc. Thesis, Department of Chemical Engineering, Technical University of Denmark, 2003. (17) Scharwz, J.; Beloff, B.; Beaver, E. Use Sustainability Metrics to Guide Decision-Making. Chem. Eng. Prog. 2002, (July), 58-63. (18) Sikdar, S. K. A Journey Towards Sustainable Development. A Role for Chemical Engineers. EnViron. Progress 2003, 22, 227. (19) Sikdar, S. K. Sustainable Development and Sustainability Metrics. AIChE J. 2003, 49, 1928. (20) Cabezas, H.; Bare, J. C.; Mallick, S. K. Pollution Prevention with Chemical Process Simulators: The Generalized Waste Reduction (WAR) AlgorithmsFull Version. Comput. Chem. Eng. 1999, 23, 623. (21) Beloff, B.; Schwan, J.; Beaver, E. Integrating Decision Support Tools for a More Sustainable Industry. Presented at the SPE Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production 2001, Kuala Lumpur, Malaysia, 2001, Paper No. SPE 73970. (22) Batterham, R. J. SustainabilitysThe Next Chapter. Chem. Eng. Sci. 2006, 61 (13), 4188-4193. (23) Spangenberger, J. H.; Pfahl, S.; Deller, K. Towards Indicators for Institutional Sustainability. Ecol. Indic. 2002, 2, 61. (24) OECD EnViromental Indicators: Towards Sustainable DeVelopment 2001; OECD Code 97200109P1; Organisation for Economic Co-Operation and Development: Paris, France, 2001. (25) Cordis Focus No. 258. TIPS: Safety for Sustainable European Industry Growth (available via the Internet at http://www.cordis.lu/focus/ en/home.html, September 2005). (26) Vincent, R.; Bonthoux, F.; Mallet, G.; Iparraguirre, J. F.; Rio, S. Me´thodologie D’EÄ valuation Simplifie´e du Risque Chimique: Un Outil d’Aide a` la De´cision. INRS Hyg. Secur. TraVail 2005, 195 (Second Quarter), 7 (INRS Ref. No. ND 2233). (27) Life Cycle InVentories for Packagings; SAEFL Environmental Series No 250; Bundesamt fu¨r Umwekt, Wald und Landwirtschaft (BUWAL), 1998. (28) Bommaraju, T. V.; Luke, B.; O’Brien, T. F.; Blackburn, M. C. Chlorine. In Kirk-Othmer Encyclopedia of Chemical Technology; Wiley: New York, 2004. (29) The European Chlor-Alkali Industry. Steps Towards Sustainable DeVelopment; Euro Chlor: Brussels, Belgium, 2004. (30) Key Facts about Chlorine; Euro Chlor: Brussels, Belgium, 2002. (31) Protecting Human Health. The Facts about Water Disinfection; Euro Chlor: Brussels, Belgium, 2000. (32) Chenier, P. J. SurVey of Industrial Chemistry, 3rd Edition; Kluwer Academic/Plenum Publishers: Dordrecht, The Netherlands, 2002. (33) Schmittinger, P.; Florkiewicz, T.; Curlin, L. C.; Luke, B.; Scannel, R.; Navin, T.; Zelfel, E.; Bartsch, R. Chlorine. In Ullmann’s Encyclopedia of Industrial Chemistry; Wiley: Weinheim, Germany, 2002. (34) Mata, T.; Smith, R.; Young, D.; Costa, C. Life Cycle Assessment of Gasoline Blending Options. EnViron. Sci. Technol. 2003, 37, 3724. (35) Smith, R.; Mata, T.; Young, D.; Cabezas, H.; Costa, C. Designing Environmental Friendly Chemical Processes with Fugitive and Open Emissions. J. Clean. Prod. 2004, 12, 125.

ReceiVed for reView May 30, 2006 ReVised manuscript receiVed August 2, 2006 Accepted August 3, 2006 IE060692L

Framework for Sustainability Metrics - Industrial & Engineering

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