Modélisation sémantique des bases de données d'inventaires en cycle de vie. (Semantic modelling of life cycle inventory databases)

Environmental impact assessment of goods and services is nowadays a major challenge for both economic and ethical reasons. Life Cycle Assessment provides a well accepted methodology for modeling environmental impacts of human activities. This methodology relies on the decomposition of a studied system into interdependent processes in a step called Life Cycle Inventory. Every process has several environmental impacts and the composition of those processes provides the cumulated environmental impact for the studied human activities. Several organizations provide processes databases containing several thousands of processes with their interdependency links that are used by LCA practitioners to do an LCA study. Understanding and audit of those databases requires to analyze a huge number of processes and their dependency relations. But those databases can contain thousands of processes linked together. We identified two problems that the experts faces using those databases : – organize the processes and their dependency relations to improve the comprehensibility ; – calculate the impacts and, if it is not possible, find why it is not feasible. In this thesis, we : – show that there are some semantic similarities between the processes and their dependency relations and propose a new way to model the dependency relations in an inventory database. In our approach, we semantically index the processes using an ontology and we use a multi-layers model of the dependency relations. We also study a declarative approach of this multi-layers approach ; – propose a method to calculate the environmental impacts of the processes based on linear algebra and graph theory, and we study the conditions of the feasibility of this calculation when we have a cyclic model. We developed a prototype based on this approach that showed some convincing results on different use cases. We tested our prototype on a case study based on a data set extracted from the National Renewable Energy restricted to the electricity production in the United-States.