An Integrated Life Cycle Assessment Modeling Framework to Advance Biofuels and Bioproducts Production

Life cycle assessment (LCA) has been widely used to evaluate the potential environmental impact associated with biofuels and bioproducts production. Traditionally, LCA studies adopt a static approach and apply rigid factors throughout the life cycle inventory (LCI) and life cycle impact assessment (LCIA) stages. This practice, however, often overlooks large uncertainty, particularly in the effect of multiple scales of system, various process parameters, and data 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 established in Python, which integrates biorefinery simulation, design basis algorithms, and LCA to elucidate optimal pathways for the production of biofuels and bioproducts. To demonstrate, a case study for sugarcane ethanol production will be presented. The integrated model has the ability to size and predict performances of each unit process based on detailed design decisions and operating characteristics, and thus simulate the dynamic life cycle inventory inputs. Further, multiple 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.