An ANFIS model for environmental performance measurement of transportation

Abstract

Fuzzy logic has also been applied to life cycle assessment (LCA) mainly to assess uncertain values or to use on individuals' judgments as input data in LCA studies. This paper presents an environmental performance measurement using an adaptive neuro-fuzzy inference system (ANFIS) in an LCA model for comparing alternative transportation fuels. The most promising fuels include compressed natural gas (CNG) and biodiesel. The potential environmental benefits of these alternative fuels can be measured using LCA methodology. The methodology allows quantitative information on the material and energy flows to be integrated with qualitative information reflecting such aspects as the social acceptability of different types of environmental damage. The proposed ANFIS model is used to represent uncertainties in the data so that the model can predict both the magnitude of the environmental impacts of the alternative fuels and the corresponding desirable levels of these estimates. Results of a case study show biodiesel to be superior to both CNG and diesel in terms of overall environmental impact. © 2012 Springer-Verlag.