Approximate life cycle assessment of classified products using artificial neural network and statistical analysis in conceptual product design

Abstract

In the early phases of the product life cycle, Life Cycle Assessment (LCA) has been used to support decision-making for conceptual product design; the best alternative can be selected based on its estimated LCA and its benefits. Both the lack of detailed information and time for a full LCA for a various range of design concepts demonstrate the need for a new approach to environmental analysis. The paper suggests a novel approximate LCA methodology for the conceptual design stage by grouping products according to their environmental characteristics and by mapping product attributes to impact driver index. The relationship is statistically verified by exploring the correlation between total impact indicator and energy impact category. A neural network approach is developed to predict an approximate LCA of grouping products in conceptual design. Trained learning algorithms for the known characteristics of existing products will quickly give the result of LCA for new design products. Training is generalized by using product attributes for an ID in a group as well as other product attributes for other IDs in other groups. The neural network model with back propagation algorithm is used and the results are compared with those of multiple regression analysis. The proposed approach does not replace full LCA but it provides some useful guidelines for the design of environmentally conscious products in the conceptual design phase.