Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation.

ABSTRACT: Operating lignocellulosic fermentation processes to produce fuels and chemicals is challenging due to the inherent complexity and variability of the fermentation media. Real-time monitoring is necessary to compensate for these challenges, but the traditional process monitoring methods fail to deliver actionable information that can be used to implement advanced control strategies. In this study, a hybrid-modeling approach is presented to monitor cellulose-to-ethanol (EtOH) fermentations in real-time. The hybrid approach uses a continuous-discrete extended Kalman filter to reconciliate the predictions of a data-driven model and a kinetic model and to estimate the concentration of glucose (Glu), xylose (Xyl), and EtOH. The data-driven model is based on partial least squares (PLS) regression

SUBMITTER: Cabaneros Lopez P 

PROVIDER: S-EPMC7894558 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature