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- Cabaneros Lopez, Pau, et al.
(författare)
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Transforming data to information : A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation
- 2021
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Ingår i: Biotechnology and Bioengineering. - : Wiley. - 0006-3592 .- 1097-0290. ; 118:2, s. 579-591
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Tidskriftsartikel (refereegranskat)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.
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| 2. |
- Lopez, Pau Cabaneros, et al.
(författare)
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Towards a digital twin : a hybrid data-driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation
- 2020
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Ingår i: Biofuels, Bioproducts and Biorefining. - : Wiley. - 1932-104X .- 1932-1031. ; 14:5, s. 1046-1060
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Tidskriftsartikel (refereegranskat)abstract
- The high substrate variability and complexity of fermentation media derived from lignocellulosic feedstock affects the concentration profiles and the length of the fermentation. Failing to account for such variability raises operational and scheduling issues and affects the overall performance of these processes. In this work, a hybrid soft sensor was developed to monitor and forecast the evolution of cellulose-to-ethanol fermentation. The soft sensor consisted of two modules (a data-driven model and a kinetic model) connected sequentially. The data-driven module used a partial-least-squares model to estimate the current state of glucose from spectroscopic data. The kinetic model was recursively fitted to known concentrations of glucose to update the long-horizon predictions of glucose, xylose, and ethanol. This combination of real-time data update from an actual fermentation process to a high-fidelity kinetic model constitutes the basis of the digital twin concept and allows for a better real-time understanding of complex inhibition phenomena caused by different inhibitors commonly found in lignocellulosic feedstocks. The soft sensor was experimentally validated with three different cellulose-to-ethanol fermentations and the results suggested that this method is suitable for monitoring and forecasting fermentation when the measurements provide reasonably good estimates of the real state of the system. These results would allow the flexibility of the operation of cellulosic processes to be increased, and would permit the scheduling to be adapted to the inherent variability of such substrates.
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