Life cycle impacts of ethanol production from spruce wood chips under high gravity conditions

Journal article, 2016

Background Development of more sustainable biofuel production processes is ongoing, and technology to run these processes at a high dry matter content, also called high-gravity conditions, is one option. This paper presents the results of a life cycle assessment (LCA) of such a technology currently in development for the production of bio-ethanol from spruce wood chips. Results The cradle-to-gate LCA used lab results from a set of 30 experiments (or process configurations) in which the main process variable was the detoxification strategy applied to the pretreated feedstock material. The results of the assessment show that a process configuration, in which washing of the pretreated slurry is the detoxification strategy, leads to the lowest environmental impact of the process. Enzyme production and use are the main contributors to the environmental impact in all process configurations, and strategies to significantly reduce this contribution are enzyme recycling and on-site enzyme production. Furthermore, a strong linear correlation between the ethanol yield of a configuration and its environmental impact is demonstrated, and the selected environmental impacts show a very strong cross-correlation (r^2 > 0.9 in all cases) which may be used to reduce the number of impact categories considered from four to one (in this case, global warming potential). Lastly, a comparison with results of an LCA of ethanol production under high-gravity conditions using wheat straw shows that the environmental performance does not significantly differ when using spruce wood chips. For this comparison, it is shown that eutrophication potential also needs to be considered due to the fertilizer use in wheat cultivation. Conclusions The LCA points out the environmental hotspots in the ethanol production process, and thus provides input to the further development of the high-gravity technology. Reducing the number of impact categories based only on cross-correlations should be done with caution. Knowledge of the analyzed system provides further input to the choice of impact categories.