Evaluation of Input Data Quality in Standardized LCA for System Improvements in Continuous Manufacturing Systems

Other conference contribution, 2024

Background & Purpose:The rise in demands for environmental information concerning products has now seen the use of LCA within the EPD framework rise among product producers, particularly in the construction sector. Although some construction products are manufactured in repetitive or batch processes due to their specifications, others are produced in continuous processes due to their relationship to a customizable criterion, for example, size or mass. Aggregates are manufactured using continuous processes, however many of the system inputs occur at discrete intervals which can be challenging for allocation. Further for the data collection, even inputs that are used continuously in the process can be collected at discrete intervals (e.g. electricity bills once a month). Therefore, not only are there temporal variations in the inputs to the system but also temporal variations in the data  collection which all lead to assumptions being made while modelling the LCI for a product and can affect the data quality. This contribution explores these variations and their potential impact on data quality for identifying system improvements at the manufacturing level.
Methods: The evaluation is based on observations from three research projects to identify the need for preparing and processing input and output data.
Results: The results show multiple sources for input and output data coming at various time intervals throughout a one year period. The granularity varies for key input and output data.
Conclusions: The results indicate automating data collection is needed to reduce the data collection burden on operators if LCA results are to be used for system improvements at the manufacturing level. However, there are questions around what level of granularity and processing of the data is needed to be useful in identifying improvements at a finer scale than one year. Further, it raises challenges with quality questions that need to be addressed so that the data can be trustworthy for operators considering the various sources for the data that are observed.