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Environmental Modelling & Software 60 (2014) 18e30

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Environmental Modelling & Software

journal homepage: www.elsevier .com/locate/envsoft

An environmental assessment system for environmental technologies

Julie Clavreul a, Hubert Baumeister b, Thomas H. Christensen a, Anders Damgaard a, *

a Technical University of Denmark, Department of Environmental Engineering, Building 113, DK-2800 Kongens Lyngby, Denmarkb Technical University of Denmark, Department of Applied Mathematics and Computer Science, Building 303B, DK-2800 Kongens Lyngby, Denmark

a r t i c l e i n f o

Article history:Received 21 August 2013Received in revised form25 May 2014Accepted 7 June 2014Available online

Keywords:EASETECHLife cycle assessmentWasteLCA modelUncertaintyFlow modelling

* Corresponding author. Tel.: þ45 45251612; fax: þE-mail address: adam@env.dtu.dk (A. Damgaard).

http://dx.doi.org/10.1016/j.envsoft.2014.06.0071364-8152/© 2014 Elsevier Ltd. All rights reserved.

a b s t r a c t

A new model for the environmental assessment of environmental technologies, EASETECH, has beendeveloped. The primary aim of EASETECH is to perform life-cycle assessment (LCA) of complex systemshandling heterogeneous material flows. The objectives of this paper are to describe the EASETECHframework and the calculation structure. The main novelties compared to other LCA software are asfollows. First, the focus is put on material flow modelling, as each flow is characterised as a mix ofmaterial fractions with different properties and flow compositions are computed as a basis for the LCAcalculations. Second, the tool has been designed to allow for the easy set-up of scenarios by using atoolbox, the processes within which can handle heterogeneous material flows in different ways and havedifferent emission calculations. Finally, tools for uncertainty analysis are provided, enabling the user toparameterise systems fully and propagate probability distributions through Monte Carlo analysis.

© 2014 Elsevier Ltd. All rights reserved.

Software availability

Name: EASETECH (Environmental Assessment System forEnvironmental Technologies)

Developer: DTU Environment and DTU Compute (TechnicalUniversity of Denmark)

Contact: corresponding author or www.easetech.dkProgramming framework: .NETLanguage: EnglishAvailability and online documentation: See www.easetech.dkYear first available: 2013

1. Introduction

Over the last 30 years, life cycle assessment (LCA) has developedas a major tool for quantifying the environmental impacts ofproducts and systems. Detailed guidelines such as the ILCD Hand-book (European Commission, 2011) and ISO standards (2006) havebeen developed to guide the users in how to carry out the LCA. Tofacilitate the actual modelling more than 50 models are availabletoday to help practitioners in their LCA projects (EPLCA, 2013), tomeet the demand of more and more complex modelling needs(Hilty et al., 2014). As these models have developed with increasing

45 45932850.

functionality and database sizes, there has been a split betweengeneric models intended for the modelling of all types of productsand systems and models that specialise in certain types of productsor systems.

1.1. Generic versus specialised LCA models

An example of specialised areas for the application of LCA is theassessment of solid waste management systems.Whereas product-based LCA usually follows a single product from cradle-to-grave, awaste-LCA will assess the handling of a very heterogeneous mate-rial consisting of a number of different waste fractions (end-of-lifeproducts in the waste mass) from end-of-life to grave or remanu-facturing. These waste fractions have significant variability in theirphysical and chemical properties (Riber et al., 2007), and theiroptimal handling varies greatly among fractions (e.g. handling offood waste versus a plastic bottle). Splitting the waste into severalflows, e.g. by source separation, and keeping track of masses andsubstances in the subsequent treatment schemes are key issues inmodern waste management.

Generic LCA tools, such as SimaPro (2013) or GaBi (2013), do notallow for the modelling of a reference flow consisting of a mix ofmaterials. To make up for this, “add-on” models have been devel-oped that can take into account these very heterogeneous referenceflows. An example of this is the waste management modelsdeveloped by Doka (2009) for waste treatment technologies suchas landfills and waste incineration facilities. Users can input data

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e30 19

corresponding to their waste input flow in the add-on models, andthe results of these models can then be imported into the genericLCA models.

The LCAmodels dedicated towastemanagement (WM) are builtto copewith these specificities and to handle parallel flows throughcomplex systems (Damgaard, 2010). The main difference in com-parison to generic LCA models is that the LCA and treatment pro-cess modelling are directly integrated into one model. This meansthat the LCA practitioner can more easily evaluate the influence ofvarious parameters of the waste management scheme (composi-tion of reference flow, options for sorting, changes in technologiesetc.) on the resulting environmental impacts, as variations in theseparameters will have direct impact on any linked process. This alsomeans that a user can easily track the impacts back to find thematerial in the heterogeneous reference flows that caused theimpact, since the impacts are directly related to the chemical andphysical properties of each material.

In a review of more than 200 waste-LCA studies, Laurent et al.(2014) showed that in approximately half of the studies practi-tioners preferred using dedicated waste-LCA models rather thangeneric LCA models. This confirms the recognition of a need fordedicated tools which help the practitioner keep track of multi-fraction flows.

1.2. Waste LCA models and EASETECH

For waste LCA models, the approach has thus far involveddeveloping dedicated tools targeted at WM experts, which followwaste from its generation to emissions in the environment, providespecific treatment processes adapted to waste management anduse parameters with which WM experts are familiar and for whichthey can provide data. Several waste-LCA models have beendeveloped over the past decade. These models are either veryspecific targeted models for single technologies such as Boeschet al. (2014), or larger more complex models covering full wastemanagement systems. Gentil et al. (2010) presented a review ofnine full models, showing how the different models learnt andevolved from previous models while incorporating new knowledgeand functionality. The work and research carried out with thesemodels provided a valuable holistic understanding of how thesewaste systems worked and how different processes and flowsinteracted. In the meantime, systems have become increasinglymore complex in terms of management (e.g. combined treatmentof different waste streams) and technologies (e.g. new thermalprocesses), therefore there is a need for more flexible models, thatgive the user the ability to design better process models and toexpand all possible flow paths. Additionally, sensitivity and un-certainty analysis with regards to system parameters is crucial, toassess the robustness of results. To cope with these new re-quirements, we decided to develop a new LCA model, based onexperience gained from the development of EASEWASTE (seeFig. 1). The only other model included in the review by Gentil et al.(2010) still being developed and externally released is the DSTmodel (Weitz et al., 1999). This model has just been released in anew version called the Solid Waste Optimization Life-cycleFramework (SWOLF) (Levis et al., 2013).

Excel-basedEASEWASTE

(C++)

2002

Fig. 1. Historical development of LCA models

The first model developed at the Technical University ofDenmark was EASEWASTE, which was initially released in 2004(Kirkeby et al., 2006) and followed by updated versions in 2008 and2012. The model handles a flow of fractions that have differentphysical properties (e.g. moisture content, heating value) andchemical compositions (e.g. carbon, nitrogen, mercury). Theseflows are thus handled as a matrix of waste fractions and materialproperties, and each fraction can be handled independently orgrouped based on general similarity (e.g. PE bottle and plasticwaste) in different processes. For each flow the user can define thecollection system, transport mode and treatment in a definednumber of processes. The purpose of EASEWASTE is to provideinventories of waste management technologies to users which canbe used in LCA modelling. All of these processes are based onpublished research, making use of the findings of different researchprojects, assessing for examplewaste-to-energy (Riber et al., 2008),landfilling (Manfredi and Christensen, 2009; Damgaard et al., 2011)and biological treatments (Boldrin et al., 2011) (see EASEWASTE(2013) for a full list).

EASETECH has been developed to allow modelling of a range ofdifferent environmental technologies from a systems perspective,using a toolbox of processes (as explained in 3.1.2). The model iscurrently used by DTU researchers to model wastewater treatment(Yoshida et al., 2014), sludge treatment (Gable et al., 2013) andrenewable energy technologies (Turconi et al., 2013) which was notpossible in the former model.

The objectives of this paper are to present this new model,called EASETECH, and in particular:

� to explain how the new LCA EASETECH model was developed(Section 2);

� to show the important novelties in this model as a result of thisdevelopment (Section 3.1);

� to describe the software in terms of data input (Section 3.2),calculation structure (Section 3.3) and uncertainty propagation(Section 3.4) and

� to present a case study implemented in EASETECH (Section 4).

2. The development process

This section describes the development process and does notinclude any description of themodelling. The EASETECHmodel wasdeveloped through several steps. The first step consisted in thedevelopment of a conceptual model to ensure the feasibility of themodel. The objective of this phase was to rethink the design of theprevious model, identify the core features of waste treatmenttechnologies and exploit similarities between the differenttechnologies.

This conceptual model was developed as a computational pro-totype in the programming language Ruby (Flanagan andMatsumoto, 2008), using an agile software development method(Abrahamsson et al., 2002). To specify each new feature, a userstory was written, describing what the user provides as inputvalues for the different technologies and which outcomes are ex-pected from the computations. All user stories were entered intothe FitNesseWiki (FitNesse, 2013), an automated testing tool which

Domain model(Ruby)

EASETECH(C#)

2011 2013

at the Technical University of Denmark.

Fig. 2. Example of material composition computed in the flow layer. The figure only shows an excerpt of the chemical elements and fractions available in EASETECH.

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e3020

uses the Framework for Integrated Tests (Fit) (Mugridge andCunningham, 2005) to evaluate all user stories in the context ofthe developed conceptual model. This process ensures that theconceptual model always models the correct computations. A userstory example is presented in the supplementary information.

Overall, 73 different user stories were implemented to test thevarious computations in the conceptual model, from simple ma-terial flow transformations to LCA calculations including compu-tations with uncertainty. The conceptual model resulted in atoolbox of generic processes which could be combined tomodel thedifferent treatment technologies.

Based on the experiences learnt from the conceptual model andsome first concepts of user interfaces, the EASETECH model wasdeveloped from 2011 in the .Net Framework. The developmentfollowed an agile software development method called ‘Scrum’

(Schwaber, 2004), wherein the client specifies small incrementalfeatures to be implemented during a time period of two weeks,called a ‘sprint’. The software was first released in January 2013. Allmajor features are explained in detail in Section 3.

3. Program description

3.1. Specificities and novelties of EASETECH

In this section the two key features that make EASETECH uniquecompared to other LCA software are described, namely the use ofmaterial fractions and a toolbox of modules that can be combinedfreely.

3.1.1. Modelling flows of material fractionsA main feature of EASETECH is the use of material fractions

when defining the reference flow of a system. In most LCA pro-grams the reference flow is defined as a single material (e.g. plasticpolymer or a coffee maker), but when modelling environmentaltreatment technologies, reference flows can consist of a very het-erogeneous mix of materials. It is thus critical to keep this infor-mation during the whole modelling. Therefore in EASETECH thereference flow it is not defined as a single material, but as acomposition of a number of different fractions, and the fate of eachsingle fraction is tracked throughout the system. This is a maindifference in comparison to traditional LCA and MFA tools such asSimaPro (2013), GaBi (2013), openLCA (2013) and Umberto (2013).This is very important because different materials have differentchemical compositions, and the optimal treatment for one materialfraction might be suboptimal for another fraction. It is thereforecritical that the starting point of the modelling process is a

composition matrix where each material fraction is specified interms of chemical composition (e.g. carbon or mercury content), aswell as fraction-specific parameters (i.e. water content, heatingvalue, methane potential). An example of a composition matrix isprovided in Fig. 2.

This means that in EASETECH the focus is on tracking substancesin the different fractions of material flows, from their generation totheir release into the environment. The flows thus consist of amatrix of material properties specified for an unlimited number ofmaterial fractions, which is maintained as the input and output ineach module into which it enters. This setup not only allows theuser to track the different substance flows over the full system, butalso to create Sankey diagrams as known from material flowanalysis (Brunner and Rechberger, 2004) and to relate emissioninventories to the different material flows throughout the model.

3.1.2. A toolbox of material processesThe basis for building the different technologies in EASETECH

lies in the use of a toolbox of template material processes. Asexplained in more detail in Section 3.2.9, the processes that handleflows in EASETECH are called “material processes”. Flows need tobe handled in different ways, so 17 templates have been created inEASETECH version 1.0 and are listed in Table 1. The toolbox offers aset of generic process modules to create, modify and split flows. Inaddition to this, more specific material processes have beendeveloped to model anaerobic digestion, landfill gas generation,leachate generation and the application of processed waste onagricultural land, as the initial focus of this model was on wastemanagement. In the example of landfill gas generation, a specificmodule was developed which models first order degradation oforganic matter and whose output details the landfill gas compo-sition over time. The option to be able to add specific materialprocesses whose output compositions can be related to any desiredparameter of the input flow is an important strength.

Currently new specific material processes are added by thedevelopers, and in the future an external editor will be developedto allow the user to define new template processes with specificmaterial transfer functions. Indeed, because the matrix formatforces the inputs and outputs of all material processes to be definedbased on the same pattern, it is relatively easy to add new templateprocesses to the software. This allows for more flexibility, as newresearch needs might require new functionalities in the materialprocesses.

All templates can be used to form new technologies by applyingproject-specific data. They can also be conceptualised as sub-technology-level processes which can be combined and grouped

Table 1Overview of all templates available in EASETECH version 1.0.

Template name Description Material transfer function inflow calculation layer

Input-specific emissions addedto the LCI calculation layera

Material generation Create a material flow (two possible data input methods:using a library of material fractions or direct input)

Create a flow Upstream impacts can be included

Energy generation Create an energy flow (with associated mass andsubstances)

Create a flow Upstream impacts can be included

Basic process Keep the flow unchanged Equal e

Water content Modify the water content of the input flow Modify the flow e

Change of energy content Modify the energy content of the input flow Modify the flow e

Addition of substances Add substances to the input flow Modify the flow e

Mass transfer Split the input flow according to total mass Transfer material fractions to outputs e

Substance transfer Split the input flow according to different properties (twopossible data inputs: fraction specific or default)

Transfer substances to outputs Possible emissions of substancesto environment compartments

Mass transfer over years Split the input flow according to years Transfer year fractions to outputs e

Anaerobic digestion Produce a gas and a digestate out of an anaerobic digester Produce gas and digestate e

Landfill gas generation Degrade organic matter according to exponential first-orderdecay, creating a landfill gas and remaining waste

Produce gas and remaining waste e

Leachate generation Define leachate generation and remaining waste Produce leachate and remaining waste e

Use on land Distribute C, N and P from the input flow and create anavoided flow

End or create an avoided flow Emissions of C, N and P compoundsto air, water and soil compartments

No output Has no output End e

Emissions to theenvironment

Translates input flow into release into an environmentalcompartment

End Emissions of substances toenvironment compartments

a All templates have basic process exchanges (elementary exchanges and external process use) contributing to the material process's LCI.

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e30 21

together to form whole treatment technologies. The grouped pro-cesses can be saved in the library of material processes and used inany scenario. An example of such a combination is given in Fig. 3.While landfilling was modelled in EASEWASTE as one large processcontaining the modelling of gas and leachate generation andhandling, it is regarded in EASETECH as a combination of differentmaterial processes whereby two specific material processesmodelling landfill gas and leachate generations are followed bydifferent generic processes to model, for example, gas collectionand the transfer of gas pollutants into the environment. This newway of defining processes from the detailed level up to the fullprocess makes the software flexible in the sense that small pro-cesses can usually be found in various treatments; for instance, agas engine can be found to treat gas produced in an anaerobicdigester but also in a landfill. Similarly, leaching processes can bemodelled in landfills and in the use of processed waste in unboundlayers in roads, which allows users to easily model new possibleprocesses by combining parts of already existing ones.

The software is provided with a predefined set of technologiesfor all commonly used treatment options for solid waste. Most ofthese technologies consist of grouped material processes to formthe actual treatment process. Table 2 provides an overview of thetechnologies provided in the current database and the material

Fig. 3. Modelling of landfilling in the EASETECH model. Each box is an in

process they use. All of these technologies have been documentedin published scientific research.

3.2. Data input in EASETECH

Two types of data can be identified in EASETECH: “background”data, which the user is not expected to modify very often(considered as being stored in “catalogues”), and data that the userwill edit to model specific scenarios (considered as being stored in“process libraries”). Fig. 4 shows how EASETECH utilises thedifferent data storage types when creating scenarios and how thedifferent catalogues, process libraries and scenarios are connected.

The section gives a short description of all catalogues, processlibraries and scenarios, describing of what they consist, which dataare provided in EASETECH version 1.0 and how the user can modifydata using the graphical user interface presented in Fig. 5. Addi-tional screenshots of the interface are provided in Part II of theSupplementary Information (SI).

3.2.1. Material fractionsThe material fractions catalogue consists of a list of material

fractions for which material properties have been defined. Thesematerial fractions are used in the processes of material and energy

dependent module. Grey boxes are explanations of actual processes.

Table 2Overview of the technologies for waste management provided in the current database. Each technology is built by combining several modules (as illustrated in Fig. 3). Thenumber in the cell is the amount of modules of each type used for one technology. Footnotes refer to article examples.

Technologies Modules

Basic Substancetransfer

Masstransferto outputs

Changeof energycontent

Nooutput

Watercontent

Addition ofsubstances

Emissions to theenvironment

Masstransferover years

Landfill gasgeneration

Leachategeneration

Anaerobicdigestion

Use-on-land

Collectiona 1Transporta 1Material recovery

facility1

Material recycling b 1Thermal treatment c 1 1 1 1Mixed waste

landfill d1 1 5 2 1 1

Mineral wastelandfill d

1 3 1 1

Composting plante 1 1 1 1Anaerobic digestion

plant e1 1 1

Application on farmland f

2

a Larsen et al. (2009).b Merrild et al. (2008).c Riber et al. (2008).d Manfredi and Christensen (2009).e Boldrin et al. (2011).f Boldrin et al. (2010).

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e3022

generation at the start of any scenario. Seventy material fractionsare provided to the user in version 1.0, based on several wastecharacterisation studies (Boldrin and Christensen, 2010; Eisted andChristensen, 2011; Riber et al., 2009), but the user can always addmore material fractions, if required.

3.2.2. Elementary exchangesThis catalogue contains all the elementary exchanges that can be

used in the program. The ecoSpold (v2) format (ecoinvent Centre,2013) was adopted to define these elementary exchanges. Thisnomenclature is used by the ecoinvent database v3 and in anumber of other LCA programs, so this choice will ensure dataexchange compatibility with many databases and software appli-cations. In addition, a converter developed by the openLCA project(openLCA, 2013) enables the conversion of datasets between theecoSpold v1, ecoSpold v2 and ELCD data formats (EC, 2010).

Fig. 4. Interactions between the different data catalogues. An arrow going from one box toanother catalogue or process. In the table in each box are given examples from the databa

EASETECH version 1.0 is provided with the 3700 elementary ex-changes contained in the ecoSpold v2 format, but new elementaryexchanges can also be added.

3.2.3. Impact categoriesThis catalogue contains all the impact categories and associated

characterisation factors that are used in LCIA methods to evaluatethe impacts of a system. Each impact category consists of a list ofelementary exchanges (from the catalogue of elementary ex-changes) and their associated characterisation factors with regardto this particular impact category. The unit of the characterisedresult is also specified therein. EASETECH version 1.0 is providedwith the impact categories of the LCIA methods EDIP97 (Wenzelet al., 1997), EDIP2003 (Hauschild and Potting, 2004), ReCiPe(Goedkoop et al., 2009), CML (CML, 2013), USEtox (Rosenbaumet al., 2008) and IPCC 2007 (Solomon et al., 2007). New impact

another box indicates that an element in one catalogue can be used as an element inse. The catalogues are all explained in detail in this section.

Fig. 5. EASETECH user interface (1: main window; 2: data input and results display; 2a: material transfer; 2b: process exchanges; 2c: documentation; 2d: LCI; 2e: characterisedimpacts; 2f: normalised impacts; 2g: weighted impacts; 2h: flow composition; 3: libraries; 3a: material processes; 3b: projects and scenarios; 3c: external processes; 4: catalogues;4a: material fractions; 4b: elementary exchanges; 4c: impact categories; 4d: LCIA methods; 4e: interfaces; 4f: constants; 4g: material properties). Additional screenshots of theinterface are provided in Part II of the Supplementary Information (SI).

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e30 23

categories can be created through the manual input of characteri-sation factors, or they can be imported as xml files following theecoSpold v1 and v2 data formats.

3.2.4. LCIA methodsThis catalogue contains all LCIA methods which are used for the

impact assessment of scenarios in EASETECH. Each LCIA methodconsists of a selection of impact categories (from the catalogue ofimpact categories). Associated normalisation references andweighing factors can be added for each of these impact categories.The LCIA methods cited in the previous section are provided inEASETECH version 1.0, together with their most recent normal-isation factors published in the literature. The user can create newLCIA methods manually or import them as xml files.

3.2.5. InterfacesThis catalogue is utilised to link the physical flow of a substance

with receiving environmental subcompartments. Thus, when auser sets the transfer coefficient of a substance to a receivingcompartment in a material process, the model knows how toconvert the mass of substance in the material input to anelementary exchange. For example, a flow of fossil carbon (e.g. inplastics) which is combusted and released through a stack into theair as carbon dioxide will be linked to the elementary exchange“Carbon dioxide, fossil; non-urban air or from high stacks” andmultiplied by a conversion factor of 44.01/12.00 to include theweight of oxygen in the final compound.

3.2.6. Constants and parametersThe catalogue of constants contains all constant values that can

be used in all data input fields. Examples of constants provided inEASETECH version x are the molar mass of carbon (“M_C”), thelower heating value of methane (“LHV_CH4”) and the molar vol-ume of an ideal gas at a standard temperature and pressure(“volume_gas”).

Parameters, which are similar to constants but specific to onescenario, are used to perform uncertainty analysis and areexplained in more detail in Section 3.4. The list of parametersattached to a scenario can be accessed by right-clicking in thebackground of this scenario.

3.2.7. Material propertiesAll the material properties that are included in the calculations

are found in the material catalogue. The user can also select thematerial properties to be included in the different display modes(named “default”, “gas”, “liquid” and “soil”) when asking for ma-terial composition.

3.2.8. External processesWhile all catalogues are accessed from the menu bar (“4” in

Fig. 5), process libraries are located in the left pane (“3” in Fig. 5).The library of external processes (“3b” in Fig. 5) includes all “sec-ond-level processes” that can be used in material processes (whichare considered as “first-level processes”). The basic idea behindEASETECH is to follow a flow through several material processes.

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e3024

The modelling of these material processes includes the use ofauxiliary materials and energy, the production of which producesemissions called “upstream impacts”. The processes fulfilling theseservices are called “external processes” in EASETECH. Each externalprocess contains all elementary exchanges related to the produc-tion of one functional unit of material or energy (e.g. the productionof 1 kg ammonia, or 1 kWh of Electricity, at the consumer, based oncoal power). Besides defining elementary exchanges related to theproduction of the material, it is also possible to add the use ofanother auxiliary material or form of energy in the production ofthe external processes themselves. However, this adds the risk ofcreating a loop function, wherebymaterial A uses material B, whoseproduction again uses material A (for example: Coal extraction /

Energy Production / Coal extraction). To avoid the model stayingin this loop, the model sets a number of iterations through which itshould run before cutting off the loop.

Users can define their own processes as well as import LCIs ofprocesses in the ecoSpold (v1 and v2) format. Additionally anumber of external processes are already included in EASETECHversion 1.0, these processes are based on publicly available reports.Metadata and sources are provided together with the data fortransparency and consistency check. Data input is carried out in thetabs “Process exchanges” and “Documentation”, presented in Fig. 5as “2b” and “2c”, respectively.

3.2.9. Material processes e templates and predefined materialprocesses

The library of material processes (located in “3a” in Fig. 5)contains two types of modules: templates and predefined materialprocesses. Templates are single empty material processes whichcan be used as a basis for creating new processes, as described inSection 3.1.2. Predefined processes are processes already made byusers to model processes for handling and treating a material. Bothkinds of processes can be used directly in a scenario, where a usercan then modify them further, if needed, for the specific intendeduse.

Predefined processes can be either single material processes orgroupings of material processes, as presented in Section 3.1.2.When saved in the process library, grouped processes are seen as asingle material process and can be used in scenarios. The user hasthe option to expand this grouped process at a later time, in orderto see all sub-processes in the procedure.

Material processes can be edited once they are dragged into themain window (“1” in Fig. 5) and can be stored in the library ofmaterial processes (“3a” in Fig. 5). Data for material processes areinput into the tabs “Material transfer”, “Process exchanges” and“Documentation” (“2a”, “2b” and “2c” in Fig. 5). Material transferdata explain how the process's outputs are computed based on theinputs. As explained in Section 3.1.2, material transfers are specificfor each template module. Substances can also be transferred tosubcompartments (e.g. to air or surface water), following which theresulting emissions are referred to as “input-specific”, as they arerelated directly to the input composition. Process exchanges datacontain all elementary exchanges and external processes used inthe material process. Direct emissions included in the “Processexchanges” tab are called “process-specific”. The documentationtab contains detailed information on how the data were gatheredand handled before being entered into EASETECH, and also assignsscores for the quality of the data. This ensures that other users ofthe dataset understand the underlying assumptions and intendeduse.

3.2.10. ScenariosThe straightforward creation of scenarios is undertaken by

dragging and connecting material processes from the library of

material processes over into the scenario window (“1” on Fig. 5). Ascenario always starts with a material or energy generation processwhich covers the functional unit for the scenario (i.e. amount ofmaterial/energy and composition of this amount in fractions). Fromthis point the user keeps connecting outputs to inputs, until alloutput boxes are connected and no more flows need to be handled.The final process can either be in the form of a final storagematerialprocess (e.g. mixed waste landfill) or in the form of a conversionprocess where the material crosses the system boundary (e.g.material recycling of scrap steel into new steel that can be used fornew products outside the system).

3.3. Calculations and modelling structure in EASETECH

The structure of computations in EASETECH is presented inFig. 6. The program uses data contained in catalogues and in thescenario to compute results in five different layers: material flowcompositions, life cycle inventory (LCI), characterised, normalisedand weighted impacts. Layer computations are consecutive, i.e.results of the first layer are used in the calculation of the secondlayer, and so on. The results from these five layers are presented inthe tabs shown as “2d”, “2e”, “2f”, “2g” and “2h” in Fig. 5.

The first layer e the flow layer e keeps track of all the mass andsubstance flows, as described in Section 3.1.1. Each material processtemplate has a specific material transfer function that defines howoutput(s') composition(s) are calculated based on the data input bythe user (always defined in tab “Material transfer, “2a” in Fig. 5).The different material transfer functions are explained briefly inTable 1 and in more detail in the SI. The results of this first calcu-lation layer constitute flow compositions, presented in tab “2h” inFig. 5.

The second layer involves calculating the LCI of all materialprocesses by using the results from the flow layer and the LCI datafrom the material process. The computation of an LCI involvesdetermining, for each elementary exchange i, the quantity Qi ofemissions or resource consumption. Therefore, an LCI is a collectionof pairs {(i; Qi)}. Like in other LCA tools, elementary exchangesoriginate either through the use of a material or energy productionprocess (external process) or from an emission from the processitself. In the first case, for each external process k in the tab “Processexchanges” (“2b” in Fig. 5), the list of elementary exchanges LCIk isthe product of the LCI of the process k, the amount of k used in thematerial process and the amount of the associated substance in theinput flow.

Concerning emissions, EASETECH distinguishes between twotypes: process- and input-specific emissions (cf. Section 3.2.9),which are specified by the user in two distinct parts of the materialprocess and have slightly different calculation modes:

� For process-specific emissions, quantity Qi is quantified for eachelementary exchange i specified in the “Process exchange” tab(“2b” in Fig. 5) as the product of the emission of i in the processby the amount of the selected substance in the input flow.

� Input-specific emissions are elementary exchanges resultingfrom transferring substances of the flow composition to envi-ronment subcompartments. These kinds of emission are speci-fied in the “Material transfer” tab (“2a” in Fig. 5) and LCIcalculations are specific to each material process template. De-tails for each material process are given in the SI (Section III,part 3).

Therefore the LCI of a material process is defined as the sum ofall external process contributions, process-specific emissions andinput-specific emissions:

Fig. 6. Data and calculations in the different results' layers. Arrows show the flows of information needed for calculations. The different layers represent the parallel calculations. *Input-specific emissions are specific to each of these material process templates: substance transfer, emissions to the environment and use-on-land.

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e30 25

LCIMaterial process ¼X

k¼external process

LCIk þ LCIprocess�specific

þ LCIinput�specific (1)

An example is used to explain the calculations of process- andinput-specific emissions. Fig. 7 shows an example of how anincineration plant was modelled by using the “Substance transfereper fraction” template. The “process exchange” tab shows theprocess-specific emissions happening on-site, in addition to the useof external processes (Fig. 7b). This results for example in anemission of carbon monoxide of 3.3 10�5 kg/kg TWW. In the “ma-terial transfer” tab, transfer coefficients are specified for differentsubstances, which induce input-specific emissions. For example,Fig. 7a shows the distribution of mercury among the differentcombustion outputs: this result in an emission of 0.007475 kgmercury to the “air e unspecified” subcompartment per kg mer-cury input to the process.

The third layer contains the calculations of the characterisedimpacts of all processes, based on the LCI results and the use of thecharacterisation factors contained in the applied LCIA method(Equation (2)). Characterised impacts are presented in two tables inthe tab “Charact. Imp.” (“2e” in Fig. 5), both of which show thecharacterised impact of the full process (or scenario) in thedifferent impact categories (columns).While the first table presentsthe contributions of the different elementary exchanges to thecharacterised impact, the second table shows the contribution ofthe various sub-processes. The fourth layer calculates the normal-ised impacts using Equation (3). Finally, the weighted impacts arecalculated in the fifth layer using Equation (4). Normalised andweighted impacts are presented respectively in tabs “Norm. Imp.”and “Weight. Imp.” (respectively “2f” and “2g” in Fig. 5), in the twosame forms of tables as in the tab “Charact. Imp.”.

IPðjÞ ¼X

i

Qi*CFðjÞi (2)

NIPðjÞ ¼ IPðjÞNFðjÞ (3)

WIPðjÞ ¼ NIPðjÞ*WFðjÞ (4)

where: IP(j) is the impact potential for impact category j,

Qi is the quantity of elementary exchange i inventoried,CF(j)i is the characterisation factor of elementary exchange ifor impact category j,NIP(j) is the normalised impact potential for impact categoryj,NF(j) is the normalisation factor for impact category j,WIP(j) is the weighted impact potential for impact category jandWF(j) is the weighting factor for impact category j.

3.4. Tools for uncertainty analysis

It is important to evaluate the uncertainty of the obtained re-sults. Tools implemented in EASETECH, to make uncertainty anal-ysis easier, are based on the recommended method for uncertaintyanalysis provided by Clavreul et al. (2012).

EASETECH allows the use of parameters or formulae usingparameters in all input fields. For each parameter the user canspecify one value, a list of values or a probability distribution.When computing with lists of values, the result will also beshown as a list of values. For example, when a parameter called“elec_rec” has the list of values [0.2, 0.3, 0.4, 0.7], the resulting LCIfor carbon dioxide is shown as a list of values, e.g. [4, 6, 8, 14].Thisallows the user to test for different assumptions (sensitivityanalysis).

To compute the uncertainty of the obtained LCA results, theimplemented tools involve representation of parameter

Fig. 7. Data input in the incineration process (based on the template “Substance transfer e default”). a: Material transfer tab (shown only for mercury), b: Process exchanges.

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e3026

uncertainties as probability distributions and propagation byMonte Carlo simulation (Clavreul et al., 2012). The user can define,for each parameter, a probability distribution of uniform, triangular,normal or lognormal shapes. For a Monte Carlo simulation, thecalculation involves sampling all distributions to obtain a list ofvalues for each parameter (the length of which equals the numberof runs) and then running the model with this list of values. Toacquire a first rough impression of the results of the Monte Carlosimulation, results are first runwith a list of 100 sampled values foreach distribution. The result of this first run is thus imprecise butquick to calculate, which allows the user to gain immediate feed-back on the effect of using the distributions. Instead of showing thelist of sampled values in the result, which can be very long, theresult is presented as a distribution with the average and standarddeviation of the list of values, shown as “D(average, standard de-viation)” in the results fields. In addition, the user may want toobtain more precise results, e.g. for certain impact categories' im-pacts, and run the simulationwith a larger list size, e.g. 10,000 runs.This can be done by clicking on the result and typing in the samplesize one would like to use, i.e. “10,000”, which will then run theMonte Carlo simulation 10,000 times and will copy the list of

resulting values into the copy buffer, which can then be pasted intoExcel for further analysis.

While it is important to assess uncertainty in the impacts fromsingle scenarios, it is even more valuable to assess the uncertaintyof the difference between two scenarios' impacts. This analysis,known as “discernibility analysis” (as introduced by Heijungs andKleijn, 2001), is a crucial step in uncertainty analysis, as severalparameters may be used in both scenarios. For example the car-bon footprint of an electricity production mix may have uncer-tainty (e.g. between 0.8 and 1.1 kg CO2-eq/kWh) which willinduce uncertainty in the results of two electricity-consumingscenarios (getting e.g. respective impacts of [1.6e2.2] and[2e2.75] kg CO2-eq). Based on this, the modeller might concludethat there is uncertainty in the designation of a scenario as thebest, but when calculating the difference between the two sce-narios the results will show in this example that the secondscenario always emits more, meaning that there is no uncertaintyin the final decision. Discernibility analysis can be performedeasily by using the same list of values for each parameter in thetwo scenarios, extracting all results into Excel and thencomputing in Excel the difference between the scenarios for each

Fig. 8. Graphical overview of the two scenarios modelled in EASETECH. The blue boxes are the material processes. Blue arrows are composition flows between processes. Orangearrows depict a transfer from the composition layer to an environmental compartment (i.e. air, soil, water). Red crosses represent the degradation or removal of a compound (e.g.evaporation of water). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e30 27

run. An example of discernibility analysis is presented in thefollowing section.

4. Case study

A case study was implemented to show how process datasetscan be added in the EASETECH model and which kinds of resultscan be extracted therefrom. The case study investigated the envi-ronmental impacts of two organic waste treatments: incinerationand anaerobic digestion (AD). The functional unit is the collectionand treatment of one tonne of organic kitchen waste from house-holds in Denmark in 2011. The case study is presented in moredetail in Clavreul et al. (2012), where modelling was performed inEASEWASTE.

The waste composition data originate from a samplingcampaign undertaken by Petersen and Domela (2003). The hy-potheses were that organic waste was source-sorted with a 60%efficiency while 5% of other combustible and non-combustiblefractions were missorted and thus ending in the organic kitchen

Fig. 9. Sankey diagram of mercur

waste. The resulting waste composition used as an input for thisstudy is presented in Fig. 2 and contains 12 waste fractions.

Fig. 8 shows an overview of the implementation of the twosystems in the EASETECHmodel. Eight different templates are usedto model the various treatment processes through which the wastetravels. For example, transportation processes are modelled usingthe basic template (where input equals output), while the com-bined heat and power (CHP) gas engine is modelled using the“Emissions to the environment” template (e.g. specifying fugitiveemissions of methane and the conversion of methane to energy).All the processes used in this modelling procedure are presented inthe SI (Part IV, Section 1).

Once the system is set up, the analysis can start. A first possi-bility is to visualise the composition of the different flows, e.g. toanalyse whether or not the digestate produced after anaerobicdigestion fulfils the requirements for application on agriculturalland. Fig. 9 shows an example of a Sankey diagram for mercury, inwhich it can be seen clearly that the mercury in the incinerator isvolatile and has been captured in the air pollution control system,

y flows in the two scenarios.

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e3028

from which it ends up in fly ash that is sent to final storage. In theAD scenario, as mercury is volatile, 40% of it follows the biogas inthe AD and is emitted into the air from the gas engine (based onEarle et al., 2000). This Sankey diagram thus highlights to the userthe importance of controlling the amount of mercury in the ADtechnology.

Normalised impacts, which were calculated using the LCIAmethods recommended by the ILCD (European Commission, 2011;Hauschild et al., 2012, and the SI, Part IV, Section 2), are presentedin Fig. 10. They are presented in person-equivalents (PE) so thateach scenario's impacts are compared to the impacts from oneperson in one year. The results show that the incineration scenarioperforms better in the impact categories of global warming (20%greater benefits), acidification (it is beneficial while the AD scenariois not), terrestrial eutrophication (40% less emissions) and on thethree toxic impacts categories. The AD scenario performs better inphotochemical ozone formation (40% less emissions), freshwatereutrophication (seven times more benefits) and particulate matter

Fig. 10. Normalised impacts of the incineration (Inc) and anaerobic digestion (AD) scenariosAC: acidification, EU: terrestrial eutrophication, fEU: freshwater eutrophication, PM: particuRD: resource depletion).

(10% greater benefits). The impacts on ozone depletion wereconsidered as negligible (impacts lower than 10�3 PE), and the twoscenarios compared equally with regards to resource depletion(less than 5% difference).

Uncertainty propagation was performed for global warmingimpacts through Monte Carlo analysis, in which the two systemswere parameterised by using 24 parameters. Screenshots showinghow this is done in EASETECH are provided in the SI (Part IV, Sec-tion 3). Uncertainty distributions, which were defined for each ofthe parameters, are presented in Table S2 of the SI. Fig. 11a presentsthe frequency distributions of both scenarios' impacts. The nor-malised impacts of the incineration scenario have a standard de-viation of 13 mPE around their mean of �42 mPE, while the ADscenario impacts have a standard deviation of 11 mPE around theirmean of �32 mPE. The discernibility analysis is presented inFig. 11b, in which the frequency distribution of the difference be-tween the incineration scenario and AD scenario impacts is plotted.This analysis shows that in 87% of the cases the incineration

in PE (GW: global warming, OD: ozone depletion, POF: photochemical ozone formation,late matter, HT: human toxicity (c: carcinogenic, nc: non-carcinogenic), ET: Ecotoxicity,

0%

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Fig. 11. Results of the Monte Carlo analysis (1000 runs) for the global warming impacts of both scenarios (a) and of the difference between scenarios (b). Results are presented asfrequency distributions (bars, left axis) and cumulative frequency distributions (line, right axis). In figure b, a negative result means that the incineration scenario obtained morebenefits than the AD scenario.

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e30 29

scenario obtained a better score than the AD scenario, underliningthe fact that this is a better solution regarding impacts on globalwarming.

5. Discussion and conclusion

Themain objective of this researchwas to develop a newholisticframework that would allow for the modelling of highly hetero-geneousmaterial flowswith large variations in physical parametersand chemical properties. The new software allows the user tomodel flows of materials and the impacts of treatment technologiesuntil release into the environment. Flexibility has been achieved (1)by creating a framework whereby processes can be connectedeasily to form scenarios, without the limitations of system size andcomplexity, and (2) by providing a toolbox of processes that theuser can use to model very different types of treatments andmanagement options.

The opportunity tomodel at the sub-process level is also offeredin other LCA models, but what makes EASETECH unique is its focuson allowing the simultaneous modelling of a large amount of het-erogeneous materials (made of various fractions). This new modelconcentrates on substance tracking and the assessment of flows atthe substance level, which is particularly important when assessingthe performance of environmental technologies.

At the same time, special effort has been applied to usability.Indeed, increasing flexibility may lead to an increase in complexityof use, but we believe the model is quite easy to start with and touse in general, for different reasons: (1) a fixed structure has beenmaintained throughout the processes in the model, where tabs areclearly defined for data input and calculation outputs, (2) the samematrix structure is maintained for all flows, (3) the handling oflarge scenarios is facilitated by an easy-to-use graphical interfacewhich allows the user to drag and drop processes into scenariosand (4) a set of processes based on published research is provided,which enables the user to model scenarios and visualise resultsquickly and efficiently.

The model requires a considerable amount of data to modeltreatment systems, which implies that it is important to be able toassess the robustness of the results. The model allows for thisassessment through the use of parameters in lieu of fixed inputvalues at the data input place. Therefore, the robustness of results

can be explored by carrying out sensitivity and uncertainty as-sessments in the model through parameter variation and MonteCarlo simulation.

In EASETECH we have chosen to store data in the ecoSpold v2data format, which will entail easy data exchange with othermodels. This means that a user can easily import required data intothe model when they are not readily available in the accompanyingdatabase. In the future, the goal is to make the model's resultsdirectly recordable in an aggregated ecoSpold v2 format so that theuser can use it in other software models.

In this first version of themodel, the focus has been on processesallowing the user to model waste management systems. Never-theless, the model has been developed with the intent and flexi-bility to be used in other environmental areas, and we are currentlyworking on expanding it to allow for the modelling of energy sys-tems also consisting of heterogeneous flows. In the near future, itwill also be explored how the model can contribute to modelling inother environmental treatment fields (waste water, soil remedia-tion, water supply, bioenergy production). Finally, future de-velopments of EASETECH will also focus on improving the facilitiesconcerning uncertainty analysis, offering parts of the analysis, e.g.showing the resulting distribution as a histogram, directly withouthaving to use Excel.

Acknowledgements

This work was supported by the Danish Strategic ResearchCouncil and the Danish Environmental Protection Agency. We aregrateful to Elena Maftei, Niels Olesen, Dainis Nikitins, BahramZarrin, Steen Silberg, Ole Henrik Skov andMads Dueholm fromDTUCompute, who helped in turning our ideas into the final productcode. We are also indebted to the Solid Waste Research group atDTU Environment for their valuable discussions and insightsthroughout the project, and 3 anonymous reviewers for theirhelpful comments. Finally a thank to James Levis from North Car-olina State University for commenting on the final manuscript.

Appendix A. Supplementary data

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.envsoft.2014.06.007.

J. Clavreul et al. / Environmental Modelling & Software 60 (2014) 18e3030

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