Benchmarking real-time monitoring strategies for ethanol production from lignocellulosic biomass

López, Pau Cabañeros; Feldman, Hannah; Mauricio‐Iglesias, Miguel; Junicke, Helena; Huusom, Jakob Kjøbsted; Gernaey, Krist V.

doi:10.1016/j.biombioe.2019.105296

Cited by 28 publications

(32 citation statements)

References 94 publications

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“…This is not necessarily true as most 'real-time' monitoring methods of fermentation processes tend to be noisy (especially those based on vibrational spectroscopy). 29 In fermentations 1 and 3, the soft sensor performed well because the PLS measurements of the glucose concentration were reasonably close to the real value. However, in fermentation 2, PLS prediction errors were confounded with the effect of the inhibitors and incorporated within the parameters of the kinetic model, resulting in deviating predictions.…”

Section: Discussion and Perspectives For Applicationmentioning

confidence: 80%

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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Section: Discussion and Perspectives For Applicationmentioning

confidence: 80%

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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“…Deng et al, (2019) potencializaram o rendimento da hidrólise da casca de arroz de 1,81 g.L -1 para 5,81 g.L -1 , após o uso do peróxido de hidrogênio como pré-tratamento. Os resultados demostraram uma remoção de 97,56% da lignina, 85,75% da hemicelulose e somente 0,56% da celulose.…”

Section: Conversão De Açúcar (Peso%) =unclassified

“…Disponível em: < http://www.tudorondonia.com.br/noticias/comunidadesribeirinhas-querem-alternativas-para-pescadores-do-rio-madeira-,2206.shtml>. Acesso em 02 set 2019…”

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A importância das águas do Rio Madeira aos povos ribeirinhos nas imediações de Porto Velho

Carneiro¹,

Dagios²

2020

Meio Ambiente, Sustentabilidade E Tecnologia – Volume 3

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“…In an industrial setting, physical techniques capable of real time measurement are preferred [19]. Common methods for in situ measurement of viable biomass include dielectric spectroscopy, infrared spectroscopy and fluorescence spectroscopy, NIR spectroscopy, and Raman spectroscopy as well as microscopy combined with image analysis [19][20][21][22][23]. However, inline sensors are prone to high measurement noise and require chemometric knowledge to establish meaningful measurement techniques or display limitations in other fields [20].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, turbidity probes are not feasible in combination with high particle background in complex media [24]; commercial dielectric spectroscopy probes can differentiate between viable cells and other solid particles, but cannot quantify the amount of dead cells and particle background. These techniques have also exhibited polarization problems when medium conductivity is high [20,21]. In particle-free medium, near-infrared spectroscopy (NIR) and Raman spectroscopy are powerful tools for a fast and non-invasive determination of substrate concentration, product formation, and viable biomass concentration [23,[25][26][27].…”

Section: Introductionmentioning

confidence: 99%

A robust flow cytometry-based biomass monitoring tool enables rapid at-line characterization of S. cerevisiae physiology during continuous bioprocessing of spent sulfite liquor

et al. 2020

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Assessment of viable biomass is challenging in bioprocesses involving complex media with distinct biomass and media particle populations. Biomass monitoring in these circumstances usually requires elaborate offline methods or sophisticated inline sensors. Reliable monitoring tools in an at-line capacity represent a promising alternative but are still scarce to date. In this study, a flow cytometry-based method for biomass monitoring in spent sulfite liquor medium as feedstock for second generation bioethanol production with yeast was developed. The method is capable of (i) yeast cell quantification against medium background, (ii) determination of yeast viability, and (iii) assessment of yeast physiology though morphological analysis of the budding division process. Thus, enhanced insight into physiology and morphology is provided which is not accessible through common online and offline biomass monitoring methods. To demonstrate the capabilities of this method, firstly, a continuous ethanol fermentation process of Saccharomyces cerevisiae with filtered and unfiltered spent sulfite liquor media was analyzed. Subsequently, at-line process monitoring of viability in a retentostat cultivation was conducted. The obtained information was used for a simple control based on addition of essential nutrients in relation to viability. Thereby, inter-dependencies between nutrient supply, physiology, and specific ethanol productivity that are essential for process design could be illuminated. Keywords Sustainable bioprocess solution. Yeast morphology. Viable/non-viable biomass populations. Particle background. Complex medium. Continuous bioprocessing with cell retention Published in the topical collection Advances in Process Analytics and Control Technology with guest editor Christoph Herwig. Charlotte Anne Vees and Lukas Veiter contributed equally to this work.

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Benchmarking real-time monitoring strategies for ethanol production from lignocellulosic biomass

Cited by 28 publications

References 94 publications

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

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

A importância das águas do Rio Madeira aos povos ribeirinhos nas imediações de Porto Velho

A robust flow cytometry-based biomass monitoring tool enables rapid at-line characterization of S. cerevisiae physiology during continuous bioprocessing of spent sulfite liquor

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