RT Journal Article
T1 Towards a digital twin: a hybrid data-driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation
A1 Cabaneros López, Pau
A1 Udugama, Isuru A.
A1 Thomsen, Sune T.
A1 Roslander, Christian
A1 Junicke, Helena
A1 Mauricio Iglesias, Miguel
A1 Gernaey, Krist V.
K1 Real-time monitoring
K1 Digital Twin
K1 Lignocellulosic ethanol
K1 kinetic models
K1 Data-driven models
K1 yeast fermentation
AB The high substrate variability and complexity of fermentation media derived from lignocellulosic feedstocks affect the concentration profiles and the length of the fermentations. Not accounting 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 fermentations. The soft sensor consisted of two modules (a data-driven 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 fit to the 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 concepts and allows for the 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 to monitor and forecast fermentations when the measurements provide reasonably good estimates of the real states of the system. These results would allow increasing the flexibility of the operation of cellulosic processes and adapting the scheduling to the inherent variability of such substrates.
PB Wiley
YR 2020
FD 2020
LK http://hdl.handle.net/10347/23294
UL http://hdl.handle.net/10347/23294
LA eng
NO Lopez, P.C., Udugama, I.A., Thomsen, S.T., Roslander, C., Junicke, H., Mauricio‐Iglesias, M. and Gernaey, K.V. (2020), Towards a digital twin: a hybrid data‐driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation. Biofuels, Bioprod. Bioref., 14: 1046-1060. doi:10.1002/bbb.2108
NO This is the peer reviewed version of the following article: Lopez, P.C., Udugama, I.A., Thomsen, S.T., Roslander, C., Junicke, H., Mauricio‐Iglesias, M. and Gernaey, K.V. (2020), Towards a digital twin: a hybrid data‐driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation. Biofuels, Bioprod. Bioref., 14: 1046-1060. doi:10.1002/bbb.2108, which has been published in final form at https://doi.org/10.1002/bbb.2108. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions
NO This project has been partially supported by the Energy Technology Development and Demonstration Program (EUDP) in association with the project ‘Demonstration of 2G ethanol production in full scale’ (grant no. 64015‐0642), and has been realized together with Maabjerg Energy Center (MEC) and Novozymes A/S. The support of the BIOPRO2 Strategic Research Center (grant agreement no. 4105‐00020B) is gratefully acknowledged. The authors wish to acknowledge the support provided by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska‐Curie grant agreement number 713683 (COFUNDfellowsDTU), by the Danish Council for Independent Research in the frame of the DFF FTP research project GREENLOGIC (grant agreement number 7017‐00175A), and by Novo Nordisk Fonden in association with the Fermentation‐Based Biomanufacturing education initiative. Miguel Mauricio Iglesias belongs to the Galician Competitive Research Group ED431C 2017/029 and the CRETUS Strategic Partnership (AGRUP2017/01)
DS Minerva
RD 28 abr 2026