(51d) Introducing a New Quantitative Life Cycle Assessment Module for Biofuels and Bioproducts

Authors: 
Shi, R. - Presenter, University of Illinois at Urbana-Champaign
Cortes-Pena, Y., University of Illinois at Urbana-Champaign
Guest, J., University of Illinois at Urbana-Champaign
Life cycle assessment (LCA) has been widely used to evaluate the potential environmental impact associated with a product or service. Traditionally, LCA studies adopt a static approach and apply rigid factors throughout the life cycle inventory (LCI) and life cycle impact assessment (LCIA) stages, as well as fixed time horizon for the impact analysis. This practice, however, often overlooks large uncertainty, particularly in the effect of multiple scales of system, various process parameters, and inventory sources, and significantly limits the generalizability of findings and the transferability of the results to other LCAs. In this study, we propose a new quantitative LCA approach by utilizing an open-source biorefinery modeling platform, Bioprocess Simulation and Techno-Economic Analysis Modules with Life Cycle Assessment (BioSTEAM.LCA), to evaluate the environmental impact of bioenergy and bioproduct production. This modeling platform is established in Python, enabling complete flexibility for user-defined processes and products as well as robust uncertainty and sensitivity analyses. To demonstrate the capabilities of the software, a case study for sugarcane ethanol production will be presented. The integrated modeling platform has the ability to size and predict the performances of each unit process from pretreatment through distillation based on detailed design decisions and operating characteristics (such as operating pressure, distillation stages, heat transfer coefficient, etc.), and thus simulate the dynamic life cycle inventory inputs during operation phase of each process. A fermentation process model is also calibrated and integrated in the integrated tool to characterize the influence of individual design decisions for fermentation (such as batch time, inoculum volume) on the technical and environmental performance (in contrast with the typical approach of static fermentation efficiency assumptions). Further, multiple open source and commercial LCI databases and LCIA methods are available in this computational platform to create a flexible user-defined LCA system model, and the implications of varying combinations of inventory databases and impact assessment methods can also be quantified. Finally, by coupling with uncertainty analysis and sensitivity analysis, this quantitative LCA platform provides rapid and robust LCA results of candidate products, analyzes the tradeoffs among productivity, economic benefits, and environmental impacts, while identifying the critical process parameters that can advance systems-scale sustainability as a whole.

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