Development of a simultaneous allocation and data reconciliation procedure in life cycle inventory analysis using fuzzy mathematical programming

• Main Author: Ilagan, Ethel R.
• Format: text
• Language: English
• Published: Animo Repository 2009
• Online Access: https://animorepository.dlsu.edu.ph/etd_masteral/3795
• Institution: De La Salle University

Life cycle inventory analysis (LCI) is a quantitative process in LCA which involves detailed accounting of materials and computation of environmental burdens, resource consumption, energy consumption and emissions associated with the entire (cradle-to-grave) life cycle of product. This process requires a reliable data. Data is normally collected from diverse sources (e.g. publications, databases and site-specific measurements). The combined data may result in violations of mass and energy balances due to uncertainties brought about by data gaps and errors caused by imprecise measurements. Data quality is one of the major issues in conducting LCI study which can be addressed by data reconciliation method. Another equally important methodological concern in LCI arises when multiple products are involved and is known as the allocation problem. The problem is how to apportion each of the materials and environmental burdens to each product. This research developed a model that solves both data reconciliation and allocation problems simultaneously using fuzzy mathematical programming. The devised model is demonstrated on illustrative and industry case studies involving (1) closed-loop recycling and (2) open-loop recycling utilizing an aluminum industry data. The results obtained were compared to conventional sequential allocation method, via partitioning using allocation factors on mass basis and data reconciliation method specifically fuzzy data reconciliation developed by Tan, et al. (2007). The devised model achieves comparable results with the stepwise approach, reconciliation followed by allocation method in both illustrative and industry case studies. Since existing LCA models do not involve data reconciliation methods, practitioners may either overestimate or underestimate inventory results if the LCI data utilized is inconsistent with material balances. Thus, the developed approach is recommended to be included in LCA methodology.