Comparative Evaluation of Chemical Life Cycle Inventory Generation

Research Article Cite This: ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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Comparative Evaluation of Chemical Life Cycle Inventory Generation Methods and Implications for Life Cycle Assessment Results Abhijeet G. Parvatker† and Matthew J. Eckelman*,‡ †

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Department of Chemical Engineering, Northeastern University, 313 Snell Engineering Center, 360 Huntington Ave, Boston, Massachusetts 02115 United States ‡ Department of Civil and Environmental Engineering, Northeastern University, 400 Snell Engineering Center, 360 Huntington Ave, Boston, Massachusetts 02115, United States S Supporting Information *

ABSTRACT: A life cycle assessment (LCA) practitioner is often faced with the problem of missing chemical life cycle inventory (LCI) data sets, as current databases cover only a small fraction of chemicals used in commerce. Here, we critically review eight different methods used by LCA practitioners to estimate missing chemical LCI data, including process simulation, engineering process calculations, molecular structure-based models, stoichiometric approaches, use of proxies, and omission. Each method is technically described with examples from the literature, description of advantages and disadvantages, and discussion of suitability depending on availability of data and expertise. Methods are then fully demonstrated and compared for accuracy against reported chemical industry data using case studies of styrene and its downstream product, acrylonitrile-butadiene-styrene (ABS). Resulting LCI and life cycle impact assessment (LCIA) values are compared and discussed, with specific attention to methodspecific exclusion of particular flows. Out of the four methods with which full LCIs can be generated, the advanced processbased methods give the most accurate life cycle GHG emission results for styrene and ABS, compared to plant data. Stoichiometric calculations, which are the most commonly used approach, underestimate the actual global warming results by 35−50%. Among the 18 impact categories of the ReCiPe LCIA method, results for the estimated LCI data were within 10% of the actual plant results for only 4−5 categories. Based on the critical review and demonstration results, we provide recommendations for appropriate use of LCI estimation methods in various LCA modeling situations. KEYWORDS: Process calculations, Stoichiometry, Molecular structure-based models, Proxy LCI

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INTRODUCTION Challenges of Limited Chemical Life Cycle Inventory Data. The ubiquitous use of synthetic chemicals in modern consumer products makes them an integral input for product life cycle assessment (LCA), which is a modeling framework used to assess resource use, emissions, and environmental impacts over the life cycle of a product. The building blocks of an LCA are physical accounts of material and energy flows for individual materials or processes, compiled in life cycle inventory (LCI) databases. However, only a fraction of those chemicals synthesized or in commercial use are represented in existing LCI databases.1,2 Currently, LCI databases account for LCIs for approximately 500 commonly used chemicals, which predominantly include bulk chemicals and some intermediates, compared to approximately 85 000 chemicals in commerce.3 As a result, practitioners are frequently required to estimate LCI data for chemicals that are not found in LCI databases. Limitations of time, resources, and expertise required to © XXXX American Chemical Society

generate an accurate LCI data set have been major challenges for LCA practitioners.4,5 Reasons for the relative scarcity of LCIs for chemicals include: unavailability of proprietary primary data from chemical plants; a large variety of unit operations and process configurations used in production of different chemicals, hindering extrapolation; typical barriers in cost and time required to produce a full LCI; and the sheer enormity of the known chemical universe. Primary data measured from industrial operations is typically considered the most valuable form of LCI data,6 even though data from a specific plant may not be representative of national or global production of a chemical. Plant data, such as quantities of raw materials and utility inputs, are often closely protected by companies as confidential business information. Even when such Received: July 27, 2018 Revised: October 9, 2018

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DOI: 10.1021/acssuschemeng.8b03656 ACS Sustainable Chem. Eng. XXXX, XXX, XXX−XXX

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ACS Sustainable Chemistry & Engineering

Figure 1. Hierarchy of methods used in LCI generation of chemicals with respect to the data/time requirements and accuracy.

and by the well-recognized difficulty in attributing facility-level data to individual products when multiple chemicals are being produced.8 Another example of automated chemical LCI generation is the use of molecular structure-based models developed using artificial neural networks (ANNs) to predict selected environmental impacts for several missing chemicals. However, the reliability of such models is dependent on the size and range of the training data chosen, and they are “black-box” models that cannot be investigated for mechanistic relationships.9,10 The other general approach to estimate missing inventory data is for authors to generate LCIs one chemical at a time. Several methods have been used by LCA practitioners and other researchers, often in combination, most commonly: basic stoichiometry, process mass intensity, and engineering process calculations.1,11 The generation of chemical LCIs in the literature has been largely focused on stoichiometric methods since the availability of the data required for this method, namely balanced chemical reactions with molecular mass of the chemicals involved, are easily available in the open domain.1,2,11 Extrapolation and substitution or using proxy LCI from existing databases are other two popular approaches found in the literature and do not require much data,9,12 though they do require expert domain knowledge in order to properly identify proxies that share similarities in process routes and synthesis conditions, not just in nomenclature. All of these suggested approaches aim at reducing the amount of time required to do a LCA while managing to build LCIs for chemicals. These approaches are a substitute to full-fledged process calculations or process simulations, which require the greatest resources in terms of time, expertise, and access to modeling tools, although these are considered the most accurate when done with reliable operational data. Simplified methods such as stoichiometric equations and basic process-based approaches can ignore many meaningful drivers of material and energy use and emissions during chemical production, including actual production scales, process conditions, heat

data are not protected, LCA practitioners may not have the resources to obtain this information from industry. Consequently, the use of estimated values based on aggregated reporting and/or engineering models rather than primary data is widespread but also faces numerous challenges.6 Creating a complete LCI for a target chemical requires estimation of associated material and energy flows, including reactants, solvents, catalysts, processing aids, cleaning, equipment, energy required for the operation of the plant, and any emissions from the process. Other important LCI inputs include the transportation of raw materials and the infrastructure of the plant itself. Production of a target chemical can have multiple pathways or processes, and the inventory of raw materials and energy use for these different pathways could differ significantly. Ideally, LCA practitioners developing estimates for chemical LCI data have (1) chemistry and chemical engineering domain knowledge, (2) access to process data on the synthesis in question and modeling tools, and (3) ample time to perform the necessary calculations. Often this is not the case, and over the years, various estimation methods have been proposed and utilized to fill LCI data gaps for chemicals. Approaches to Address the Scarcity of Chemical LCIs. It has been widely recognized that with increasing demand for environmental footprint data for products, there is a need to reduce the time and effort required to complete an LCA.7,8 In the case of chemicals, there has been an increasing focus on making reliable LCIs available to LCA practitioners without having them go through the effort of seeking data from the industry or doing all the detailed process calculations and simulations by themselves. Two general approaches have been proposed. One is to develop automated tools that can generate LCI data for a large number of chemicals based on data mining, statistical, and/or machine learning techniques. For example, chemical industry data reported to regulatory authorities can be mined, manipulated, and anonymized to generate LCIs, as demonstrated by Cashman et al.8 This strategy is limited by the data quality and coverage of records available through reporting B


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standard operating procedures, and, perhaps most commonly, human errors in manual data recording. Plant primary data can also be obtained from government agencies to which the plants report. In the US, chemical manufacturers can be identified through databases such as Chemical Data Access Tool (CDAT), which is maintained by the United States Environmental Protection Agency (EPA)13 and lists production capacity for each facility. Simultaneous inventory information for the major producers of a chemical available directly from the manufacturers would address issues such as temporal and geographical uncertainties in the data, which make the LCI representative of national conditions. US EPA also publishes declared greenhouse gas (GHG) emissions data for large-scale facilities across the country.14 For facilities that produce multiple chemical product streams, such aggregate facility data must be allocated to each output, commonly done on the basis of mass or value. Precautions must be taken when integrating data from diverse sources as these could be aggregated at different levels or from different time periods. Cashman et al. have developed a streamlined methodology for generating chemical LCI data using information from multiple databases, primarily from US EPA.8 Independent of government reporting, some chemical companies publish Environmental Product Declarations (EPDs), which communicate the life cycle impacts of the product according to product category rules (PCRs) and are independently certified.15 EPDs come with a period of validity and may specify the geography of the production plants considered. LCAs or EPDs published by companies and industry associations typically provide aggregated results for those environmental impacts specified in product category rules (PCRs); however, they seldom divulge detailed process steps or give complete LCI data sets. Chemical companies may also use life cycle assessment to identify “hot-spots” in their operations, but reported results are typically aggregated at the company level and are difficult to disaggregate to a facility or especially product level. Plant-level data published by chemical industry associations such as the International Council of Chemical Associations (ICCA), the American Chemistry Council (ACC), or Plastics Europe are other potential sources of primary data for chemical LCI data sets.16 Method 1: Process Simulation Tools. With sufficient data for the reaction and subsequent separation steps, a chemical process can be modeled using process simulation tools. Because process simulation is commonly used at the design stage to represent industrial-scale production, it is valuable for estimating life cycle inventory data for chemicals which are not currently produced at commercial scales, or for which the primary plant data are not available. Process simulation tools have been used widely to generate chemical LCIs, including for refinery products, bulk organic and inorganic chemicals, and biobased chemicals.17−19 Integration of life cycle assessment and process systems engineering, which includes modeling, simulation, and optimization of chemical processes using computer-based tools, has been explored by various authors, as reviewed by Jacquemin et al.20 Chemical engineers use process simulation at the design stage of pilot and large-scale plants to test the design data in a virtual environment. Several open-source and commercial chemical process simulators21 such as DWSIM, Aspen Plus, HYSYS, PROSIM, or CHEMCAD can be used to estimate heat duty for reaction and unit operations and electricity use for pumps,

losses, reaction yields, and energy for reactions, separation, and other auxiliary operations. So, how accurate are these simplified approaches? Objective and Outline of Current Work. In this critical review, we evaluate and contrast the most common methods used to generate missing LCI data for chemicals, namely (1) process simulation, (2) detailed process calculations, (3) basic process calculations, (4) molecular structure-based models, (5) stoichiometry, and (6) using proxy data. We also explore the consequences of omitting a chemical LCI from a LCA in the absence of a reliable estimate, and we compare all these methods with an LCI based on primary data. First, a general summary of each method for chemical LCI calculation is provided with their relative advantages, limitations and applicability. Next, each method is implemented in a case study for styrene and one of its major downstream products, acrylonitrile-butadiene-styrene (ABS). The resulting LCI data are compared with existing data sets for styrene and ABS from LCI databases to compare the accuracy and reliability of the different methods. We also compare life cycle impact assessment (LCIA) results carried out for each of these different LCIs for styrene and ABS, to investigate how influential the different and/or missing LCI data for chemicals are in the final environmental impact results. The study concludes with recommendations for use of these different methods in various situations and their implication on the impact assessment results. This critical review can serve as a guideline for LCA practitioners in selecting a suitable method for generating missing chemical LCI data sets based on data, time, and resource availability. The comparison would also assist users in interpreting LCA results and the degree of variability that would be expected based on the LCI estimation method employed. General description of chemical LCI estimation methods. In this section, we describe and critique seven different methods used by LCA practitioners to deal with the absence of chemical LCI data. This is followed by detailed calculations of each method for styrene and ABS, where the methodology of using these techniques are presented in detail. Figure 1 shows the estimation methods, presented in a commonly perceived rank order with most accurate at the top of the inverted pyramid.2,9 This is Method 0, or direct use of plant data or using LCIs from existing databases that are based on anonymized plant data. At the opposite end, using a “similar” chemical as a proxy or omitting a chemical from an LCA entirely do not require LCI generation and are supposed to be the least accurate methods; these are shown at the bottom of the pyramid. The two arrows on the left and the right of the pyramid qualitatively depict the data/time requirement and accuracy of the LCIs generated using these methods, respectively. Current molecular structure-based modeling gives LCIA results for the given chemical but bypasses the LCI generation step and so are listed toward the bottom of Figure 1. Method 0: Manufacturing Plant Primary Data. Ideally, LCI data for a target chemical are gathered from the latest plant production data from an appropriate geographical region, but this is rarely possible. Access to such data is difficult not just because such information is often proprietary but also because getting approvals and clearances can be time-consuming. When plant data are available, it is still necessary to compare it with the design requirements of the process and ensure that there are no significant discrepancies from measurement or reporting errors. Inaccuracies in plant data could arise from faulty measurement instruments, unidentified leakages, significant deviations from C


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Table 1. Equations Used in the Calculation of Energy for Unit Operations and Equipment in the Process Calculation Methodsa unit process/ operation

basic process calculations23,24

advanced process calculations27,29 ÉÑ| ÄÅ l Ño ÅÅ ka o RM RM ES ÅÅA ΔTt ÑÑÑo =o [ m C Δ T + Δ H + m Δ H ] + ( ) m ÑÑ} Å RM p r ES v o o ÅÅ s o ÑÑÖo Å Ç ~ n

reactor

Q react = mRM CpΔT + mΔHr

Q react

distillation

condenser energy balance Q cond = H v − HD − HL

Q dist =

mFCpΔT + mDΔH v(1.3.R min + 1) ηheat

α(1 − XLD) zyz 1 jij XLD zz jj − α − 1 jk XLF 1 − XLF z{

overall energy balance Q reb = Q cond + HB + HD − HF R min = dryer

Q dry = mFCpF(Tb − T )

(mFCpF(Tb − T ) + mESΔH vES)

Q dry =

ηheat

stirrer energy electricity

2

0.33 kWh/kg product

Estir =

NPρmix N3d5t ηstir

pumping energy mg Δh Epump = ηpump emissions

liquids

boiling point at 1 atm23

20−60 °C−2% fugitive losses

60−120 °C−1% fugitive losses

gases−0.5% fugitive losses23

storage emissions30 working losses: during transfers sat V ijj 273.15 yzzijj Pi yzz zz(MW)KNKP Lw = zzzjjj jjj 22.4 k T(K ) {k 760 z{ KN set to 1, KP = 1 for organic liquids Breathing losses i 273.15 zyji Pisat zy ij TR yz z zzjjj L B = 16.3Vvjjjjj zzj 760 zzz(MW)jj T zz T ( K ) k { {k k { process vent emissions

Ei =

sat FxiyP i i

Si(MW) i RT where Si is between 0 and 1 based on degree of saturation. (0.1 for small amounts of gas, 0.5 for intermediate, and 0.9 for a large amount of vented gas) f ugitive emissions average emission factor approach30,31

ET =

∑ i

Δ(number of components)i X Δ(emission factor, i)

a

Terms defined in Notation.

compressors, and other electric drives. Material and energy flows from each unit process can be aggregated together to construct an LCI for the overall production scheme but can also be used in disaggregated form to test different configurations and perform sensitivity analysis around reaction conditions, equilibrium yields, types of separation operations, auxiliary units, and heat recovery opportunities. Uniquely among the methods reviewed here, process simulation accounts for changes in temperatureand pressure-dependent chemical properties such as heat capacity that directly affect energy requirements. Such dependencies are difficult to incorporate in empirical process design calculations, the next method in the hierarchy of Figure 1. Heat integration opportunities in the process can be assessed using pinch analysis tools such as i-Heat and Aspen Energy Analyzer. In general, pinch is defined as that point in the design where the solution is most constrained.22 For heat integration, pinch is the temperature that divides the hot and cold process streams at a given minimum temperature difference, which helps in determining the maximum energy recovery in the process and hence the minimum utility requirements.22 Limitations of using process simulation to generate chemical LCI data include the need for training and chemical engineering

expertise, access to software, time requirements, and knowledge of detailed plant design parameters and reaction conditions. The quality of LCI data obtained from the simulation tool also depends on the level of detail achieved in the process modeling. Only expert users will be able to generate LCI data that most closely represent realistic plant operations. Methods 2 and 3: Process Design Calculations. Chemical process design calculations have been used to generate LCIs when the available data are not sufficient to use simulation tools but are sufficient to parametrize design equations with mass and energy balances to provide material and energy requirements and process emissions. Here we classify these methods as basic or advanced depending on the level of detail considered in the calculations. Examples of equations used in the two methods are shown in Table 1. Basic process calculations are built from mass and energy balance equations. Unlike in process simulation, typically static values are used that do not vary with time, temperature, or pressure. First, a detailed process description lists all reagents, solvents, catalysts, or inert substances required. Assumptions for process yield, solvent recovery, and recycling are then needed to estimate the quantities of each substance required for the D


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ACS Sustainable Chemistry & Engineering synthesis.23 Overall energy requirements can be determined using empirical design equations, found in, for example, Coulson and Richardson Chemical Engineering Series24 or Perry’s Chemical Engineers’ Handbook.25 Previous work using basic process calculations has been reported by Jiménez-González et al.23 and Kim et al.26 to calculate the process energy requirements of common organic and inorganic chemicals. Fugitive emissions are also estimated based on rules of thumb which recommend 2% losses for liquids with boiling point up to 60 °C and 1% losses for boiling point from 60 to 120 °C.23 Advanced process calculations build on basic process calculations but include consideration of production scales, equipment efficiencies, heat transfer processes, reflux, and reactor geometries. Detailed bottom-up equations for energy calculations have been proposed by Bieler et al.27 and further ̈ developed by Szijjarto et al.28 and Piccinno et al.29 These models calculate the energy requirement of each piece of equipment used in the production process, considering heat losses based on specific operation and the equipment used, as opposed to gross estimation in the case of basic process calculations. Advanced process calculations account for heat losses from each piece of equipment and other details such as heat recovery and reuse. In advanced process calculations it is desirable to use actual plant design data with scaled up output unlike basic process calculations which do not require equipment sizing. Advanced process calculations also consider heat integration possibilities in the process which is not always inherent in process description. With enough data this can be done using pinch analysis as described in the process simulation section. Rules of thumb as recommended by Jiménez-González et al.23 can also be used to estimate the heat recovery in the process. In case of emissions from the process, unlike in basic process calculations detailed calculations can be done in this method using the equations proposed by Smith et al.30 Emissions from three different areas within the process: storage emissions, process vent emissions, and fugitive emissions can be estimated using the physical and thermodynamic properties of the chemicals.30 The choice of advanced process calculations depends on the time available to the practitioner to collect the data and do the calculations which require expertise in the field of process equipment design. Utilizing process design calculation methods still requires detailed information on process conditions. Where details are unavailable, informed estimates and assumptions for different processes and unit operations must be made, which requires basic knowledge in chemical engineering. Particular care must be taken when estimating demand for heating and cooling. In case of basic process calculations, ignoring opportunities for heat integration in the plant could result in the overestimation of the energy use for the process. On the other hand, the method used to predict the possible heat integration in the advanced process calculations is still not as accurate as the energy analysis that can be done in full-scale process simulation. Method 4: Stoichiometry. In the absence of any process data, stoichiometric calculations derived from published chemical reactions are commonly used to develop an approximate life cycle inventory of the desired chemical. This is perhaps the most common technique used for chemical LCI data generation.32 Stoichiometry gives the molar ratios of reactants, the target chemical, and any other chemical products, which are considered as emissions in the LCI if they are byproducts or used to perform allocation or system expansion in a multioutput system if they are coproducts. Stoichiometry does not provide

any information about subsequent separation steps or the energy requirements of the process. However, a crude estimation of energy requirements can be made using basic thermodynamic data such as the heat of reaction, derived from the heat of formation of the chemical species involved in the reaction, to evaluate cooling (for exothermic reactions) or heating (for endothermic reactions) needed to maintain reaction temperature. When known, synthesis yield can also be incorporated into stoichiometric calculations. Depending on the data available about the reaction and its stoichiometry, the stoichiometry method can be classified into three levels, as described in Table 2. Table 2. Summary of Stoichiometric Method for LCI Generation method stoichiometry level 1 stoichiometry level 2 stoichiometry level 3

data required

calculation requirements

balanced reaction

molecular weights, mass balance

level 1 + heat of reaction, specific heat level 2 + yield or level 1 + yield

level 1 + heat of formation, sensible heat (Q = mCpΔT), energy balance same as level 1 or 2 while accounting for yield

Limitations of the stoichiometry method include a limited scope, excluding resource-intensive separation processes and any consideration of reaction conditions. Furthermore, there are representativeness concerns, as reactants are rarely used in their stoichiometric ratios in industry, with at least one of the reactants used in excess to ensure higher and faster conversion.33 The major advantages of this method are its nominal data and time requirements, and hence, this method is appropriate when a LCA practitioner is time-constrained or needs to make quick back-of-the-envelope calculations for screening-level comparisons between two processes or chemical products. Method 5: Molecular Structure-Based Models. Process simulation, process design calculations, and stoichiometry techniques all require information on the synthesis route and synthesis conditions of the target chemical. Alternatively, molecular structure-based models use only physical−chemical properties of the target chemical to make model predictions of the environmental impacts of synthesis, using advanced statistical techniques. Such models are used extensively in computational toxicology to predict the toxicity of chemicals, through so-called quantitative structure−activity relationship (QSAR) models.34 For the purpose of estimation in LCA, analogous models have been developed for environmental rather than toxicological end points. The Finechem tool is a molecular structure-based model that uses artificial neural networks (ANNs) to predict the relations between the molecular descriptors, such as molar weight, functional groups, and chiral centers of a chemical with various measures of environmental impact of its production, such as global warming potential (GWP), cumulative energy demand (CED), and Eco-indicator 99.35,36 More recently, Song et al. used a greater number of molecular descriptors and a larger training data set with deep (many-layer) ANN models to estimate environmental impacts such as global warming, acidification potential, human health, ecosystem quality and Eco-Indicator 99.10 Using structure-based models requires basic knowledge of chemistry and knowledge of molecular descriptors and physicochemical characteristics. Molecular descriptors can be gathered E


F

omit

proxy

extended environmental input−output (EEIO) analysis for missing flows in LCI USEEIO v1.160

can be used when the chemical input is very low compared to other inputs in a LCA

could be effective in saving time if done with detailed analysis and expert judgment

simple calculations for rapid LCI generation, very little data requirements

chemical reactions from online databases and literature

molecular structurebased models stoichiometry

design material and energy requirements of the process can be calculated based on basic chemical engineering principles, can be applied to any kind of chemical as long as data is available only the molecular structure of the chemical is required to make a prediction

design equations, physical and thermodynamic data for chemicals from Perry’s Chemical Engineers’ Handbook, NIST database, chemical process encyclopedia FineChem web model

process design calculations

primary data/validated estimation and approximations

LCI databases simulate nonexistent processes, Sensitivity analysis tools, account for heat recovery using energy plug-in (Aspen Plus)

primary data primary data

company sustainability publications industry associations

open-source: DWSIM, advanced simulation library commercial: Aspen Plus, Aspen HYSYS, CHEMCAD, UniSim, Pro II

primary data

environmental product declarations (EPD)

advantages primary data with minimum geographical and temporal uncertainties

estimation tools

manufacturers (CDAT)

process simulation

plant data

method

data limited to reactions, pre- and postreaction operations are ignored; industry does not typically use reactants in stoichiometric ratio slight change of chemical structure could mean a completely different process route, could result in gross errors in a LCA if the chemical is key component of the product system if a significant input or one with high impacts, the results could be grossly underestimated

limited applicability, accuracy depends on the training data of the model

detailed process description may not be available, especially for new process; inaccurate assumptions used during calculations

not all databases are transparent, generalized estimations could lead to errors significant data and time requirements

old data from different geographic region, not detailed LCI aggregated average data may not represent individual region

old data from different geographic region

possible errors in data collection

limitations

Table 3. Summary of Methods to Deal with Missing LCI for Chemicals and Their Advantages, Limitations, and Examples examples

impact of the injection molding process43

comparative LCA of cleaning products61 LCA for laundry detergents62

environmental performance of catalysts for conversion of CO259

soft PVC and tobacco flavor LCA9

naphtha catalytic cracking using Pro II,17 catechols from lignin depolymerization,18 dairy industry−milk concentration19 gate-to-gate LCI information−ammonia production,23 gate-to-gate process energy for 86 chemicals26

acetic acid manufacturing8 US EPAflight−GHG emissions from large facilities51 sodium chlorate, calcium carbonate, EPS52−54 chelating agents−AkzoNobel55 building and construction, lighting market, automotive, consumer products56 Ecoinvent,57 GaBi58

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source data and temporal uncertainties would be carried over to the LCI data generated using this method.41,42 Nevertheless, this approach can be used to obtain rapid estimates in absence of detailed process level data. Method 7: Omit. Leaving out a chemical LCI entirely from an LCA is perceived as the least preferred alternative, and yet, it is frequently done in practice. Common situations are omitting chemical reagents in synthesis schema because they lack LCI data and/or are complex in structure or are mixtures, omitting chemical process aids because of unknown quantities, or omitting chemical catalysts.39,43 Omitting a chemical LCI could be considered if goal of the LCA is to qualitatively compare two systems which have the same chemical in equal quantities.39 The results should be presented as percentage difference between the two product systems which would not change with the addition of the LCI of the chemical. The other scenario when omitting of a chemical LCI could be justified is when the impacts of the chemical is below the cutoff criteria defined in the goal and scope of the LCA. Cut-off criteria allows a practitioner to leave out the insignificant life cycle stages, processes or elementary flows from the system boundary in LCA modeling.44 The ISO standards recommend using a percentage of total mass, total energy and total environmental impacts as the criteria for cutoff.45 Cutoff at

Comparative Evaluation of Chemical Life Cycle Inventory Generation

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