Optimization of polyhydroxyalkanoates fermentations with on-line capacitance measurement

Lan, Li; Wang, Zejian; Chen, Xuejun; Chu, Ju; Zhuang, Yingping; Zhang, Siliang

doi:10.1016/j.biortech.2014.01.042

Cited by 22 publications

(25 citation statements)

References 33 publications

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“…Colony-forming units (CFU) is thus applied to estimate the viable cells, but it is time consuming and difficult to know what happens in a fermentor as a function of time. Thus, online capacitance probe was used to get accurate determination of viable cells and reflects time-course of active biomass, effectively and efficiently [5]. With increasing amount of biosensors being available for detecting changes in a real bioprocess, the multi-scale analysis method using the correlation and tendency of process parameters will be a more powerful methodology in studying practices and developing optimized strategies.…”

Section: Theory Of Multi-scale Analysis For Fermentation Processmentioning

confidence: 99%

“…In the past four decades, a huge amount of advanced process monitoring techniques have been developed and applied for bioprocess monitoring and control, e.g., IR spectroscopy for online real-time measurement of glucose, glutamate, fructose, glutamine, proline, and ammonia [3]; Raman spectroscopy for measurement of glucose, acetate, formate, lactate and phenylalanine, and carotenoid production [4]; capacitance sensor for biomass measurement [5]; and online MS for measurement of real-time concentration of O 2 and CO 2 in exhaust gas [6]. These rich real-time data, together with the increasing mega data along with the advent of OMICS techniques, make tens of thousands of parameters can be monitored and analyzed simultaneously [7,8] at different scales.…”

Section: Introductionmentioning

confidence: 99%

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Advances and Practices of Bioprocess Scale-up

Xia

Wang

Lin

et al. 2015

Advances in Biochemical Engineering/Biotechnology

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: This chapter addresses the update progress in bioprocess engineering. In addition to an overview of the theory of multi-scale analysis for fermentation process, examples of scale-up practice combining microbial physiological parameters with bioreactor fluid dynamics are also described. Furthermore, the methodology for process optimization and bioreactor scale-up by integrating fluid dynamics with biokinetics is highlighted. In addition to a short review of the heterogeneous environment in large-scale bioreactor and its effect, a scale-down strategy for investigating this issue is addressed. Mathematical models and simulation methodology for integrating flow field in the reactor and microbial kinetics response are described. Finally, a comprehensive discussion on the advantages and challenges of the model-driven scale-up method is given at the end of this chapter.

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Section: Theory Of Multi-scale Analysis For Fermentation Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advances and Practices of Bioprocess Scale-up

Xia

Wang

Lin

et al. 2015

Advances in Biochemical Engineering/Biotechnology

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“…The first form of the observer is for the scenario in which residual biomass concentration can be measured independently of the accumulated product; see for instance [10,13].…”

Section: Observer For the Case With Residual Biomass Measurementmentioning

confidence: 99%

Modeling and estimation of production rate for the production phase of non-growth-associated high cell density processes

Jamilis

Garelli

Mozumder

et al. 2015

Bioprocess Biosyst Eng

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This paper addresses the estimation of the specific production rate of intracellular products and the modeling of the bioreactor volume dynamics in high cell density fed-batch reactors. In particular, a new model for the bioreactor volume is proposed, suitable to be used in high cell density cultures where large amounts of intracellular products are stored. Based on the proposed volume model, two forms of a high-order sliding mode observer are proposed. Each form corresponds to the cases with residual biomass concentration or volume measurement, respectively. The observers achieve finite time convergence and robustness to process uncertainties as the kinetic model is not required. Stability proofs for the proposed observer are given. The observer algorithm is assessed numerically and experimentally.

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“…Por ejemplo, la determinación en línea de la concentración de biomasa por densidadóptica es casi inservible por la opacidad del medio. Alternativamente se puede utilizar espectroscopía dieléctrica con buenos resultados [1], [2], sin embargo los sensores necesarios son costosos y no siempre están disponibles. Tampoco es fácil determinar las concentraciones de fuente de carbono o nitrógeno debido a que en general se busca utilizar como sustrato residuos provenientes de la industria y agricultura, por lo tanto hay muchas impurezas presentes y las fuentes no están completamente definidas.…”

Section: Introductionunclassified

Production rate estimation in processes with high cell concentration

Jamilis

Garelli

Battista

2015

2015 XVI Workshop on Information Processing and Control (RPIC)

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Resumen-This work focuses in the estimation of the specific production rate of intracellular products in fed-batch processes, for the case where high cell densities are present. Particularly, a new volume model is proposed for the high cell density case where a large amount of intracellular product is stored. Based on the model ant the on-line volume measurement, a high order sliding mode observer is proposed to estimate the specific production rate. The observer achieves finite time convergence and robustness against model disturbances and model uncertainty, while it does not require for a kinetic model of the production rate. The proposed algorithm is evaluated whith numerical simulations and experimentally.Index Terms-observador, modos deslizantes, alta concentración celular, estimación, volumen. I. INTRODUCCIÓNLos bioprocesos de alta densidad celular presentan dos problemas particulares cuando se trata de su monitoreo y control, en primer lugar algunos de los sensores y métodos de medida en línea comunes en bioprocesos estándar no pueden ser utilizados fácilmente. Por ejemplo, la determinación en línea de la concentración de biomasa por densidadóptica es casi inservible por la opacidad del medio. Alternativamente se puede utilizar espectroscopía dieléctrica con buenos resultados[1], [2], sin embargo los sensores necesarios son costosos y no siempre están disponibles. Tampoco es fácil determinar las concentraciones de fuente de carbono o nitrógeno debido a que en general se busca utilizar como sustrato residuos provenientes de la industria y agricultura, por lo tanto hay muchas impurezas presentes y las fuentes no están completamente definidas. Una medida con potencial utilidad es la del volumen del contenido del biorreactor ya que los sensores de nivel son comunes a nivel industrial. Su aplicación a procesos de alta densidad celular podría ser restringida en casos con producción de espuma excesiva, sin embargo existen tecnologías para abordar ese problema, como los sensores basados en radar, servomecanismos con capacidad de detectar fases con diferentes densidades (Honeywell Enraf R por ejemplo), sensores capacitivos y de conductividad u otros. El segundo problema es la escasez de modelos específicos, en particular de la dinámica de volumen del biorreactor. La gran cantidad de células, el elevado consumo de sustrato y generación de productos sumado a la alimentación de medios concentrados hacen que el volumen no se pueda obtener simplemente de la integración de caudales.Muchos de los procesos de alta densidad están asociados a la generación de productos de alto valor agregado a partir de residuos industriales y agrícolas. Tal es el caso de los procesos para producir el polímero biodegradable Polyhydroxybutyrato (PHB) mediante Cupriavidus necator [3] o la producción de lípidos para la industria del biodiesel mediante Rhodosporidium toruloides [4]. En este tipo de procesos es importante disponer de información de la velocidad a la que se está generando el producto deseado ya que es indicadora del rendimiento de...

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Optimization of polyhydroxyalkanoates fermentations with on-line capacitance measurement

Cited by 22 publications

References 33 publications

Advances and Practices of Bioprocess Scale-up

Advances and Practices of Bioprocess Scale-up

Modeling and estimation of production rate for the production phase of non-growth-associated high cell density processes

Production rate estimation in processes with high cell concentration

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