Predictive usage mining for life cycle assessment

Abstract

The usage modeling in life cycle assessment (LCA) is rarely discussed despite the magnitude of environmental impact from the usage stage. In this paper, the usage modeling technique, predictive usage mining for life cycle assessment (PUMLCA) algorithm, is proposed as an alternative of the conventional constant rate method. By modeling usage patterns as trend, seasonality, and level from a time series of usage information, predictive LCA can be conducted in a real time horizon, which can provide more accurate estimation of environmental impact. Large-scale sensor data of product operation is suggested as a source of data for the proposed method to mine usage patterns and build a usage model for LCA. The PUMLCA algorithm can provide a similar level of prediction accuracy to the constant rate method when data is constant, and the higher prediction accuracy when data has complex patterns. In order to mine important usage patterns more effectively, a new automatic segmentation algorithm is developed based on change point analysis. The PUMLCA algorithm can also handle missing and abnormal values from large-scale sensor data, identify seasonality, and formulate predictive LCA equations for current and new machines. Finally, the LCA of agricultural machinery demonstrates the proposed approach and highlights its benefits and limitations.