Dataset Information

Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation.

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.

SUBMITTER: Cabaneros Lopez P

PROVIDER: S-EPMC7894558 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation.

Cabaneros Lopez Pau P Udugama Isuru A IA Thomsen Sune T ST Roslander Christian C Junicke Helena H Iglesias Miguel M MM Gernaey Krist V KV

Biotechnology and bioengineering 20201015 2

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- ...[more]

PMID: 33002188

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Project description:BackgroundPhenolic acids are lignin-derived fermentation inhibitors formed during many pretreatment processes of lignocellulosic biomass. In this study, vanillic, p-hydroxybenzoic, and syringic acids were selected as the model compounds of phenolic acids, and the effect of short-term adaptation strategies on the tolerance of S. cerevisiae to phenolic acids was investigated. The mechanism of phenolic acids tolerance in the adapted yeast strains was studied at the morphological and physiological levels.ResultsThe multiple phenolic acids exerted the synergistic inhibitory effect on the yeast cell growth. In particular, a significant interaction between vanillic and hydroxybenzoic acids was found. The optimal short-term adaptation strategies could efficiently improve the growth and fermentation performance of the yeast strain not only in the synthetic media with phenolic acids, but also in the simultaneous saccharification and ethanol fermentation of corncob residue. Morphological analysis showed that phenolic acids caused the parental strain to generate many cytoplasmic membrane invaginations with crack at the top of these sites and some mitochondria gathered around. The adapted strain presented the thicker cell wall and membrane and smaller cell size than those of the parental strain. In particular, the cytoplasmic membrane generated many little protrusions with regular shape. The cytoplasmic membrane integrity was analyzed by testing the relative electrical conductivity, leakage of intracellular substance, and permeation of fluorescent probe. The results indicated that the short-term adaptation improved the membrane integrity of yeast cell.ConclusionThe inhibition mechanism of phenolic acid might be attributed to the combined effect of the cytoplasmic membrane damage and the intracellular acidification. The short-term adaptation strategy with varied stressors levels and adaptive processes accelerated the stress response of yeast cell structure to tolerate phenolic acids. This strategy will contribute to the development of robust microbials for biofuel production from lignocellulosic biomass.

Project description:The effective reproduction number, Rt, is a key time-varying prognostic for the growth rate of any infectious disease epidemic. Significant changes in Rt can forewarn about new transmissions within a population or predict the efficacy of interventions. Inferring Rt reliably and in real-time from observed time-series of infected (demographic) data is an important problem in population dynamics. The renewal or branching process model is a popular solution that has been applied to Ebola and Zika virus disease outbreaks, among others, and is currently being used to investigate the ongoing COVID-19 pandemic. This model estimates Rt using a heuristically chosen piecewise function. While this facilitates real-time detection of statistically significant Rt changes, inference is highly sensitive to the function choice. Improperly chosen piecewise models might ignore meaningful changes or over-interpret noise-induced ones, yet produce visually reasonable estimates. No principled piecewise selection scheme exists. We develop a practical yet rigorous scheme using the accumulated prediction error (APE) metric from information theory, which deems the model capable of describing the observed data using the fewest bits as most justified. We derive exact posterior prediction distributions for infected population size and integrate these within an APE framework to obtain an exact and reliable method for identifying the piecewise function best supported by available epidemic data. We find that this choice optimises short-term prediction accuracy and can rapidly detect salient fluctuations in Rt, and hence the infected population growth rate, in real-time over the course of an unfolding epidemic. Moreover, we emphasise the need for formal selection by exposing how common heuristic choices, which seem sensible, can be misleading. Our APE-based method is easily computed and broadly applicable to statistically similar models found in phylogenetics and macroevolution, for example. Our results explore the relationships among estimate precision, forecast reliability and model complexity.

Dataset Information

Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation.

Publications

Transforming data to information: A parallel hybrid model for real-time state estimation in lignocellulosic ethanol fermentation.

Similar Datasets

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