Modelling and uncertainty assessment for Simultaneous Saccharification and Co-Fermentation (SSCF) processes
Conference poster, 2017
Industrial and economical feasible bioethanol production via simultaneous saccharification and co-fermentation (SSCF) from lignocellulosic raw materials requires tools to cope with process variation. Variation is introduced by batch-to-batch variation of raw material or enzyme composition and may have detrimental effects on product yields and the choice of process strategies. SSCF process models including the SSCF sub-processes of enzymatic hydrolysis and fermentation increase the understanding of underlying processes and allow for the optimization of the SSCF process. However, due to batch-to batch variations the models are only applicable in narrow limits. More important, limitations in measuring techniques caused by the nature of the substrate and the current model structures contribute to ill-posed parameter estimation problems resulting in large uncertainties about the parameter estimates.
Here we identify possible sources of uncertainty for SSCF processes and propose a general strategy to minimize this uncertainty, thus broadening the applicability of SSCF models. This allows for the expansion of model usage not only to describe and optimize SSCF processes, but to develop robust control strategies, most favourable as closed-loop controls. Consistent uncertainty reduction throughout all process stages will contribute to robust process designs and predictable ethanol yields from SSCF processes.