TY - JOUR
T1 - Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentations
AU - Lopez, Pau Cabañeros
AU - Udugama, Isuru A.
AU - Thomsen, Sune Tjalfe
AU - Roslander, Christian
AU - Junicke, Helena
AU - Mauricio Iglesias, Miguel
AU - Gernaey, Krist V.
N1 - This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
PY - 2021
Y1 - 2021
N2 - 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 fermentations in real-time. The hybrid approach uses a continuous-discrete extended Kalman filter (CD-EKF) to reconciliate the predictions of a data-driven model and a kinetic model and to estimate the concentration of glucose, xylose, and ethanol. The data-driven model is based on partial-least-squares (PLS) regression, and predicts in real-time the concentration of glucose, xylose, and ethanol from spectra collected with attenuated-total-reflectance mid-infrared spectroscopy (ATR-MIR). 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. This article is protected by copyright. All rights reserved.
AB - 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 fermentations in real-time. The hybrid approach uses a continuous-discrete extended Kalman filter (CD-EKF) to reconciliate the predictions of a data-driven model and a kinetic model and to estimate the concentration of glucose, xylose, and ethanol. The data-driven model is based on partial-least-squares (PLS) regression, and predicts in real-time the concentration of glucose, xylose, and ethanol from spectra collected with attenuated-total-reflectance mid-infrared spectroscopy (ATR-MIR). 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. This article is protected by copyright. All rights reserved.
U2 - 10.1002/bit.27586
DO - 10.1002/bit.27586
M3 - Journal article
C2 - 33002188
SN - 0006-3592
VL - 118
SP - 579
EP - 591
JO - Biotechnology and Bioengineering
JF - Biotechnology and Bioengineering
IS - 2
ER -