TY - JOUR

T1 - Transforming data to information

T2 - A parallel hybrid model for real‐time state estimation in lignocellulosic ethanol fermentation

AU - Lopez, Pau Cabaneros

AU - Abeykoon Udugama, Isuru Sampath Bandara

AU - Thomsen, Sune Tjalfe

AU - Roslander, Christian

AU - Junicke, Helena

AU - Mauricio‐Iglesias, Miguel

AU - Gernaey, Krist

PY - 2021

Y1 - 2021

N2 - Operating lignocellulosic fermentation processes to produce fuels and chemicalsis 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 pectroscopy. 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 seven 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.

AB - Operating lignocellulosic fermentation processes to produce fuels and chemicalsis 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 pectroscopy. 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 seven 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.

U2 - 10.1002/bit.27586

DO - 10.1002/bit.27586

M3 - Journal article

C2 - 33002188

VL - 118

SP - 579

EP - 591

JO - Biotechnology and Bioengineering

JF - Biotechnology and Bioengineering

SN - 0006-3592

IS - 2

ER -