Design of experiments for global sensitivity analysis in life cycle assessment: The case of biodiesel in Vietnam - Khang, Dinh S. (Animo Repository, 2017)
Biodiesel has been widely proposed as an alternative to fossil fuels and its environmental impacts have been commonly assessed using life cycle assessment (LCA). However, the results of LCA can be affected by parameter uncertainties. The rigorous treatment of such uncertainties is thus essential to improve decision-making based on the LCA. In this work, the Latin hypercube design of experiments (DOE) approach is proposed for global sensitivity analysis in LCA. In this novel approach, the LCA input parameters are used as the factors for the experimental design. The LCA of biodiesel from different feedstocks, namely, jatropha, waste cooking oil (WCO), and fish oil (FO), under the current conditions in Vietnam was chosen as a test case. The LCA focuses on the global warming potential (GWP), photochemical ozone formation potential (POFP), acidification potential (AP), and eutrophication potential (EP) of the biodiesel system. These impact categories were then combined into an overall environmental impact (OEI) score using the analytic hierarchy process (AHP). The effect of changes in the LCA model parameters on the OEI was then observed through computational experiments using a Latin hypercube design. From the computational experiments, the input parameters significantly affecting the results of LCA were identified, and a proxy polynomial regression model was derived to enable global sensitivity analysis to be performed. The results show that agricultural yield, oil content of jatropha seed, transesterification yield, total transportation, and biodiesel blending fraction are the factors that have significant effects on the OEI of jatropha biodiesel; biodiesel blend fraction and total transportation are significant in the case of WCO diesel, while the only significant effect factor in FO biodiesel case is biodiesel blend fraction. Biodiesel blend fraction is the most significant parameter for all feedstocks. © 2016 Elsevier B.V.