Uncertainty analysis as a tool to consistently evaluate lignocellulosic bioethanol processes at different system scales

Other conference contribution, 2018

Lignocellulosic processes are highly prone to batch-to batch variability, e.g. of raw materials and enzyme activities. This
variability can be propagated throughout system scales during process development and optimization, influencing the outputs of
bioreaction models, techno-economic analyses and life cycle assessments. As these outputs are the main decision variables
for designing and developing lignocellulose-based processes, tools are required to evaluate the influences of process variation at
different system scales.
Uncertainty analysis quantifies the effects of model input variations on model outputs. It is an effective tool to consistently
propagate process variation throughout scales and analyse its influence on model outputs. As an example, we use a model
describing multi-feed simultaneous saccharification and co-fermentation (SSCF) of wheat straw. During the process enzymes
hydrolyse the lignocellulosic material to release glucose which can be converted by microorganisms into ethanol. To investigate
the impact of batch-to-batch variability in enzyme cocktails, we collected literature data on the enzymatic activity of Cellic
CTec2. Retrieved data were propagated in models at bioreactor, techno-economic analysis and life cycle assessment scale. We
show how uncertainty analysis can be used to guide process development by comparing different modes of operation. The
method can identify economically feasible process ranges with low environmental impact while increasing the robustness of
bioprocesses with high variation in raw material inputs. Furthermore, uncertainty analysis could help to identify relevant
parameters to choose as response variables in experimental designs.