An environmental assessment system for environmental technologies

Highlights

• Enables LCA modelling of complex systems handling heterogeneous material flows.

• Focus is on material flow modelling and substance balancing.

• Flow compositions are computed as a basis for the LCA calculations.

• Easy set-up of scenarios by use of a toolbox of template processes.

• Tools for uncertainty analysis are provided.

Abstract

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

Introduction

Over the last 30 years, life cycle assessment (LCA) has developed as a major tool for quantifying the environmental impacts of products and systems. Detailed guidelines such as the ILCD Handbook (European Commission, 2011) and ISO standards (2006) have been developed to guide the users in how to carry out the LCA. To facilitate the actual modelling more than 50 models are available today to help practitioners in their LCA projects (EPLCA, 2013), to meet the demand of more and more complex modelling needs (Hilty et al., 2014). As these models have developed with increasing functionality and database sizes, there has been a split between generic models intended for the modelling of all types of products and systems and models that specialise in certain types of products or systems.

An example of specialised areas for the application of LCA is the assessment of solid waste management systems. Whereas product-based LCA usually follows a single product from cradle-to-grave, a waste-LCA will assess the handling of a very heterogeneous material consisting of a number of different waste fractions (end-of-life products in the waste mass) from end-of-life to grave or remanufacturing. These waste fractions have significant variability in their physical and chemical properties (Riber et al., 2007), and their optimal handling varies greatly among fractions (e.g. handling of food waste versus a plastic bottle). Splitting the waste into several flows, e.g. by source separation, and keeping track of masses and substances in the subsequent treatment schemes are key issues in modern waste management.

Generic LCA tools, such as SimaPro (2013) or GaBi (2013), do not allow for the modelling of a reference flow consisting of a mix of materials. To make up for this, “add-on” models have been developed that can take into account these very heterogeneous reference flows. An example of this is the waste management models developed by Doka (2009) for waste treatment technologies such as landfills and waste incineration facilities. Users can input data corresponding to their waste input flow in the add-on models, and the results of these models can then be imported into the generic LCA models.

The LCA models dedicated to waste management (WM) are built to cope with these specificities and to handle parallel flows through complex systems (Damgaard, 2010). The main difference in comparison to generic LCA models is that the LCA and treatment process modelling are directly integrated into one model. This means that the LCA practitioner can more easily evaluate the influence of various parameters of the waste management scheme (composition of reference flow, options for sorting, changes in technologies etc.) on the resulting environmental impacts, as variations in these parameters will have direct impact on any linked process. This also means that a user can easily track the impacts back to find the material in the heterogeneous reference flows that caused the impact, since the impacts are directly related to the chemical and physical 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 practitioners preferred using dedicated waste-LCA models rather than generic LCA models. This confirms the recognition of a need for dedicated tools which help the practitioner keep track of multi-fraction flows.

For waste LCA models, the approach has thus far involved developing dedicated tools targeted at WM experts, which follow waste from its generation to emissions in the environment, provide specific treatment processes adapted to waste management and use parameters with which WM experts are familiar and for which they can provide data. Several waste-LCA models have been developed over the past decade. These models are either very specific targeted models for single technologies such as Boesch et al. (2014), or larger more complex models covering full waste management systems. Gentil et al. (2010) presented a review of nine full models, showing how the different models learnt and evolved from previous models while incorporating new knowledge and functionality. The work and research carried out with these models provided a valuable holistic understanding of how these waste systems worked and how different processes and flows interacted. In the meantime, systems have become increasingly more complex in terms of management (e.g. combined treatment of different waste streams) and technologies (e.g. new thermal processes), therefore there is a need for more flexible models, that give the user the ability to design better process models and to expand all possible flow paths. Additionally, sensitivity and uncertainty analysis with regards to system parameters is crucial, to assess the robustness of results. To cope with these new requirements, we decided to develop a new LCA model, based on experience gained from the development of EASEWASTE (see Fig. 1). The only other model included in the review by Gentil et al. (2010) still being developed and externally released is the DST model (Weitz et al., 1999). This model has just been released in a new version called the Solid Waste Optimization Life-cycle Framework (SWOLF) (Levis et al., 2013).

The first model developed at the Technical University of Denmark was EASEWASTE, which was initially released in 2004 (Kirkeby et al., 2006) and followed by updated versions in 2008 and 2012. The model handles a flow of fractions that have different physical properties (e.g. moisture content, heating value) and chemical compositions (e.g. carbon, nitrogen, mercury). These flows are thus handled as a matrix of waste fractions and material properties, and each fraction can be handled independently or grouped based on general similarity (e.g. PE bottle and plastic waste) in different processes. For each flow the user can define the collection system, transport mode and treatment in a defined number of processes. The purpose of EASEWASTE is to provide inventories of waste management technologies to users which can be used in LCA modelling. All of these processes are based on published research, making use of the findings of different research projects, assessing for example waste-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 of different environmental technologies from a systems perspective, using a toolbox of processes (as explained in 3.1.2). The model is currently used by DTU researchers to model wastewater treatment (Yoshida et al., 2014), sludge treatment (Gable et al., 2013) and renewable energy technologies (Turconi et al., 2013) which was not possible 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 this development (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).