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 and predicts in real‐time the concentration of Glu, Xyl, and EtOH from spectra collected with attenuated total reflectance mid‐infrared spectroscopy. The estimations made by the hybrid approach, the data‐driven models and the internal model were compared in two validation experiments showing that the hybrid model significantly outperformed the PLS and improved the predictions of the internal model. Furthermore, the hybrid model delivered consistent estimates even when disturbances in the measurements occurred, demonstrating the robustness of the method. The consistency of the proposed hybrid model opens the doors towards the implementation of advanced feedback control schemes.
Highlights
The transition of fuels and chemicals production from nonrenewable resources to renewables is a key requirement in realizing a circular economy
S. cerevisiae produces glycerol to regenerate NAD+/NADH and to maintain the redox balance within the cells (Palmqvist et al, 1999). This was further confirmed by the off‐ line measurements with high‐performance liquid chromatography (HPLC), which showed that during the Glu consumption phase, glycerol reached a concentration of 3 g/L
The results showed that in all fermentations, the state estimations made with the hybrid model significantly improved the predictions of the data‐driven model
Summary
The transition of fuels and chemicals production from nonrenewable resources to renewables is a key requirement in realizing a circular economy. The complexity of the media and the highly correlated dynamics between the concentrations of many analytes results in complex spectra with overlapping peaks and require extensive data analysis to train reliable predictive models (Cervera et al, 2009; Krämer & King, 2016) This situation makes the measurements noisy and often unsuited for the implementation of advanced control schemes (Krämer & King, 2017). Due to the high complexity and limited availability of fermentation media, the calibration set for the PLS models solely contained synthetic samples that were purposely planned using a design of experiments approach, and no fermentation samples were included in it This calibration set was carried out to minimize the correlation between the concentration of Glu, Xyl, and EtOH and to distribute the leverage of each sample evenly in the design space. The developed approach was applied to monitor different cellulose‐to‐EtOH fermentations carried out at the bench scale, and the results obtained were compared to a scenario where only measurements are used to monitor the process
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