Parameterised inventories for life cycle assessment
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Background and Objective
Life cycle assessment (LCA) is a highly data intensive undertaking, where collecting the life cycle inventory (LCI) data is the most labour intensive part. The aim of this paper is to show a method for representing the LCI in a simplified manner which not only allows an estimative, quantitative LCA, but also the application of advanced analysis methods to LCA.
Procedure and Method
The method is based on two main components: Firstly the parameterisation of the life cycle inventory for complete product ranges, e.g. relative material composition (the parameterisation factor is product mass or product volume), relative manufacturing inputs and wastes (the parameterisation factor is production output, in number of items, mass or volume), inputs, wastes and emissions during use (the parameterisation factors are efficiency, emissions per unit energy converted, etc.). Secondly, the parameterisation factors are related to design parameters, e.g. the efficiency of three-phase induction motors increase as the torque output increases and decreases with the number of poles, while the mass of the same induction motor increases with torque. Determining these relationships is initially labour-intensive, but only has to be done once for a product type and it is just a matter of fitting appropriate models after the collection of the relevant data. Also, required data is not impossible to come by, and respective industries publish many of the relevant data for marketing and design purposes. Due to the wide variety of products-whereby here the term ‘product’ is used in the widest sense and can be a component, an assembly, a consumer product or service-the relationships are represented as ranges with upper and lower limits. One of the limits represents ‘the best practically possible’ and is a good indication of what the technologies’ capabilities are at the time. Top-down approaches allow the approximate determination of manufacturing inventories for product ranges. Bringing the two components together, the LCA analyst can use known design parameters and so quickly determine an estimate of the life cycle inventory, after which it is then a relatively simple step to carry out the full, approximate LCA. Furthermore, this method can be extended to include life cycle costing as an extension, to simultaneously assess economic aspects of the design.
The method is further illustrated using a 3-phase asynchronous motor as an example and it is shown how the changing needs during the design process are utilised.
Discussion and Conclusion
The paper introduces the concept of parameterised inventories for the use in LCA, describes the general procedure of determining the relationships of the parameterised inventories to design parameters and outlines future developments enabled by this method of inventory representation. The novel parts of the method are a simplified, but quantitative LCA method. For the first time this enables parametric studies and sensitivity analysis, not only for varying material compositions, but varying the underlying design parameters in complex interactions, and finding optimised sets of solutions for those design parameters to achieve one or more optimised criteri(on)a.
The full potential of the method as an analysis tool, especially in the early design phases, will be reached in an integrated engineering environment, where relevant LCA and cost data are automatically and systematically exchanged along the supply chain.
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- Parameterised inventories for life cycle assessment
The International Journal of Life Cycle Assessment
Volume 9, Issue 4 , pp 227-235
- Cover Date
- Print ISSN
- Online ISSN
- Additional Links
- life cycle inventory (LCI)
- simplified LCA
- Industry Sectors
- Author Affiliations
- 1. Siemens VDO Automotive AG, Siemensstr. 12, 93055, Regensburg, Germany
- 2. Mechanical Engineering Department, Imperial College of Science, Technology and Medicine, Exhibition Road, SW7 2BX, Great Britain, London
- 3. Dept. of Precision Engineering, Kimura, Mohri & Suzuki Laboratory, The University of Tokyo, Hongo 7-3-1, 113-8656, Bunkyo-ku, Tokyo, Japan