PW06 Life-Cycle Assessment
Exhibit Hall
8:00 AM - Wednesday

(PW073) Life Cycle Assessment of Municipal Solid Waste Gasification with Uncertainty Analysis.

Halog, Anthony1, Sagisaka, Masayuki2, 1 National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan2 National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki, Japan

ABSTRACT- Various types of uncertainty and variability are often mentioned as crucial factors complicating the clear interpretation of Life Cycle Assessment (LCA) outcomes. One study reported that more than half of performed LCA studies made no reference with uncertainty. Variability is understood as stemming from inherent variations in the real world while uncertainty comes from inaccurate measurements, lack of data, model assumptions that used to convert the real world into LCA outcomes. Due to these limiting factors, uncertainty analysis is gradually gaining importance in performing LCA studies, but still its use is not a common practice among LCA practitioners. An LCA study that takes into account variability and uncertainty has been conducted for a municipal solid waste gasification plant. The methodology used involves the determination of suitable probability distributions, Monte Carlo Simulation and/or Latin Hypercube Sampling, importance analysis and finally interpretation of final LCA outputs. Monte Carlo and Latin Hypercube methods are the common probabilistic simulation used to address data inaccuracy and variability in objects and sources. The main difference between the two is that the latter involves the segmentation of uncertainty distribution into a number of non-overlapping intervals of equal probability. To reduce the number of parameters considered, only the essential ones of life cycle inventories for solid waste gasification are considered in this study. The variations of the chosen parameters in the inventory of waste gasification process chain have been characterized in the form of probability distributions. In the final step, life cycle analyses of other configurations of waste gasification technology are experimented and its inventories are compared. In this way, it is possible to take into account regional variation as well as technological variability. Using appropriate probability distributions of the essential parameters, the results of the inventory analysis is transformed from a mere single concrete value into probability distributions of mean value of the output parameters, thus, producing a more robust and convincing LCA outcomes.

Key words: Life Cycle Inventory, Probabilistic Simulation, Solid Waste Gasification, Uncertainty Analysis

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