Monitoring yeast fermentations by nonlinear infrared technology and chemometrics—understanding process correlations and indirect predictions

Pontius, Katrin; Junicke, Helena; Gernaey, Krist V.; Bevilacqua, Marta

doi:10.1007/s00253-020-10604-0

Cited by 11 publications

(14 citation statements)

References 52 publications

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“…Decoupling such correlations during the calibration (or training) of such models is crucial to reach reliable predictions even when the dynamics of the fermentation change. A common and efficient approach consists of taking samples at different times during the fermentation, and spiking them with different amounts of the analytes of interest to attain partially uncorrelated samples [114] ( Fig. 11e-h).…”

Section: Open-loop Data-driven Monitoring Of Cellulose To Ethanol Fermentioning

confidence: 99%

Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes

Gargalo

Udugama

Pontius

et al. 2020

Journal of Industrial Microbiology and Biotechnology

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The biomanufacturing industry has now the opportunity to upgrade its production processes to be in harmony with the latest industrial revolution. Technology creates capabilities that enable smart manufacturing while still complying with unfolding regulations. However, many biomanufacturing companies, especially in the biopharma sector, still have a long way to go to fully benefit from smart manufacturing as they first need to transition their current operations to an information-driven future. One of the most significant obstacles towards the implementation of smart biomanufacturing is the collection of large sets of relevant data. Therefore, in this work, we both summarize the advances that have been made to date with regards to the monitoring and control of bioprocesses, and highlight some of the key technologies that have the potential to contribute to gathering big data. Empowering the current biomanufacturing industry to transition to Industry 4.0 operations allows for improved productivity through information-driven automation, not only by developing infrastructure, but also by introducing more advanced monitoring and control strategies.

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Section: Open-loop Data-driven Monitoring Of Cellulose To Ethanol Fermentioning

confidence: 99%

Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes

Gargalo

Udugama

Pontius

et al. 2020

Journal of Industrial Microbiology and Biotechnology

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“…The PLS regression model for glucose was calibrated by creating a set of synthetic samples that contained the fermentation matrix and uncorrelated concentrations of glucose, xylose, and ethanol. 22,23 Xylose and ethanol were also taken into consideration within the calibration due to the overlap of their spectra with glucose, and due to the fact that their dynamic profiles are correlated during the fermentation. Not including them as uncorrelated variables in the calibration samples would result in interference of xylose and ethanol with the prediction of glucose.…”

Section: Pls Calibrationmentioning

confidence: 99%

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

López

Udugama

Thomsen

et al. 2020

Biofuels Bioprod Bioref

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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 © 2020 Society of Chemical Industry and John Wiley & Sons, Ltd.

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“…Free-floating wireless sensors are a quite novel and bold sensing technology, based upon non-invasive instrumented particles [45], which can provide access to process data in a harsh environment inside an agitated bioreactor [46] Another recent development is the use of advanced image analysis for process monitoring. For example, current studies have demonstrated that imaging and advanced image analysis, coupled with chemometrics and state-of-the-art machine learning algorithms, are promising to monitor fermentation [47].…”

Section: Pat Solutions and Robotics For Better Controlmentioning

confidence: 99%

Why Is Batch Processing Still Dominating the Biologics Landscape? Towards an Integrated Continuous Bioprocessing Alternative

et al. 2020

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Continuous manufacturing of biologics (biopharmaceuticals) has been an area of active research and development for many reasons, ranging from the demand for operational streamlining to the requirement of achieving obvious economic benefits. At the same time, biopharma strives to develop systems and concepts that can operate at similar scales for clinical and commercial production—using flexible infrastructures, such as single-use flow paths and small surge vessels. These developments should simplify technology transfer, reduce footprint and capital investment, and will allow to react readily to changing market pressures while maintaining quality attributes. Despite a number of clearly identified benefits compared to traditional batch processes, continuous bioprocessing is still not widely adopted for commercial manufacturing. This paper details how industry-specific technological, organizational, economic, and regulatory barriers that exist in biopharmaceutical manufacturing are hindering the adoption of continuous production processes. Based on this understanding, the roles of process systems engineering (PSE), process analytical technologies, and process modeling and simulation are highlighted as key enabling tools in overcoming these multi-faceted barriers in today’s manufacturing environment. Of course, we do recognize that there is also a need for a clear set of regulations to guide a transition of biologics manufacturing towards continuous processing. Furthermore, the role played by the emerging fields of process integration and automation as well as digitalization is explored, as these are the tools of the future to facilitate this transition from batch to continuous production. Finally, an outlook focusing on technology, management, and regulatory aspects is presented to identify key concerted efforts required to drive the broad adaptation of continuous manufacturing in biopharmaceutical processes.

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Monitoring yeast fermentations by nonlinear infrared technology and chemometrics—understanding process correlations and indirect predictions

Cited by 11 publications

References 52 publications

Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes

Towards smart biomanufacturing: a perspective on recent developments in industrial measurement and monitoring technologies for bio-based production processes

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

Why Is Batch Processing Still Dominating the Biologics Landscape? Towards an Integrated Continuous Bioprocessing Alternative

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