Towards a digital twin: a hybrid data-driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation

Abstract

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.