On‐line estimation of the specific growth rate in the bacitracin fermentation process

Golobič, Iztok; Gjerkeš, Henrik; Malenšek, Jože

doi:10.1002/aic.690451211

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…ε 0 controls the values of the time dependent forgetting factor, which makes it possible for the linear model to follow the dynamic and non-linear process of bacitracin fermentation. A detailed description of the algorithm can be found in 10 . Figure 3 shows the specific growth rates, estimated on-line with the measurable input signals CER, OUR and q r .…”

Section: Resultsmentioning

confidence: 99%

Software Sensor for Biomass Concentration Monitoring During Industrial Fermentation

Golobič

Gjerkeš

Bajsić

et al. 2000

Instrumentation Science & Technology

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For the on-line monitoring of the parameters and states of aerobic fermentation batch processes three parallel software sensors for biomass concentration monitoring were developed, which use mutually independent measured input signals: the oxygen uptake rate, the carbon dioxide excretion rate and the reaction heat flow. The software sensors are based on a linear model, where the parameters of the model are estimated using the recursive least squares method. The model was upgraded with a mechanism for a time-varying forgetting factor to enable good tracking of the estimated parameters during rapid and large changes in the non-linear and, in certain phases of the production, very dynamic process. The estimates given by all three software sensors are in good agreement with the measurements acquired off-line. The developed software sensors make it possible to achieve accurate process identification, mutual control of individual sensors and fault detection in the process. In this way, better supervision and more accurate monitoring of the industrial fermentation process is possible, where reliability of the operation is of key importance.

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Section: Resultsmentioning

confidence: 99%

Software Sensor for Biomass Concentration Monitoring During Industrial Fermentation

Golobič

Gjerkeš

Bajsić

et al. 2000

Instrumentation Science & Technology

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“…Some authors have shown the capacity of linear estimators to adequately estimate the fermentation process without increasing the model structure complexity 42, 43. In this way, some approaches have been developed to carry out the estimation from measurement of oxygen31 or carbon dioxide in the exhaust gas of the fermentor 32. It has been applied to a type of processes where a sole substrate for growth and induction was added in a noncontinuous mode.…”

Section: Estimation Algorithmsmentioning

confidence: 99%

“…The nonlinear dynamics of the specific growth rate can be included in the estimator, considering a variable forgetting factor (VFF) λ( t ) to conform the proposed estimator. The time‐varying forgetting factor prevents the constant reduction in the value of the covariance matrix during the dynamic process 32. The calculation of the VFF is based on model error and both the data vector and covariance matrix.…”

Section: Estimation Algorithmsmentioning

confidence: 99%

“…In addition, estimation of parameters by the recursive least squares (RLS) method has been proposed, especially for the specific growth rate and for the process state, thus, achieving unbiased convergence and making possible the estimation of changing process parameters 31, 32. The main advantage of the RLS methods is that, it does not require an accurate knowledge of the system because a linear model that only depends on the online measures is considered.…”

Section: Introductionmentioning

confidence: 99%

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State and specific growth estimation in heterologous protein production by Pichia pastoris

Barrigón¹,

Ramon²,

Rocha³

et al. 2011

AIChE Journal

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Estimation of biomass, substrate, and specific growth rate (μ) by two nonlinear observers (nonlinear observer‐based estimator—NLOBE, asymptotic observer with second‐order dynamics tuning—AO‐SODE) and a linear estimator (recursive least squares with variable forgetting factor—RLS‐VFF) is presented. Heterologous protein production in Pichia pastoris PAOX1 (Mut+) and PFLD1‐based systems is closely related to μ and has been addressed due to its high relevance in modern biotechnology and bioprocess engineering. μ was estimated by online gas analyses or substrate measurements, biomass, and substrate considering yield coefficients and mass balances. In simulation studies, NLOBE showed high sensitivity to tuning and initialization variables. Validation experiments demonstrated AO‐SODE performs better than the RLS‐VFF for moderate to rapid changes of μ and model parameters being known. If low changes on μ are presented, for instance, in substrate regulation, RLS‐VFF comes up as the best option, because of its reduced requirements. © 2011 American Institute of Chemical Engineers AIChE J, 58: 2966–2979, 2012

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“…Glutamate fermentation process goes through a number of phases based on serial cell growth, substrate uptake and product formation. Besides, The production of glutamate is an aerobic process, the glutamate fermentation performance and the metabolic flux distribution are affected drastically by the concentrations of dissolved oxygen in the liquid phase in fermenter or oxygen concentrations in exhaust gas (Golobič and Gjerkeš, 1999;Xiao et al, 2006), the fed-batch process can be divided into 5 phases based on the detection of Inflection Point (IP) by online measured O 2 in the exhaust gas, the IP are easily identified through combining Moving Window (MW) with Pearson Correlation Coefficient (PCC). The phase division result agrees well with actual fermentation process, it depends only on on-line measurements and fermentation processes can be easily automated to work.…”

Section: Introductionmentioning

confidence: 99%

Multi-Phase Support Vector Regression Soft Sensor for Online Product Quality Prediction in Glutamate Fermentation Process

Zheng¹,

Pan²

2017

American Journal of Biochemistry and Biotechnology

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Glutamate fermentation is inherently nonlinear, multi-phase and an aerobic fermentation process. As long measurement delays and expensive apparatus cost, on-line measurement of the product concentration is not necessarily available. The present fermentation process monitoring and quality prediction involve manual interpretation of highly informative, however, the concentrations of substrates, biomass and products are only low frequency off-line measurements. In this paper, we propose a novel Multi-Phase Support Vector Regression (MPSVR) based soft sensor model for online quality prediction of glutamate concentration. The glutamate fermentation process can be divided into a sequence of five phases by detecting the trend variation events (also termed as singular points or inflection point) of online measured O 2 in the exhaust gas, the Inflection Point (IP) are easily identified through combining Moving Window (MW) with Pearson Correlation Coefficient (PCC). For each estimation phase, SVR soft sensor model are constructed and their performance is evaluated against fermentation data in a 5 L fermenter. The efficiency of the proposed soft sensor model for online product quality prediction has been demonstrated to be superior compared to that of reported techniques in a 5 L glutamate fermentation process.

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On‐line estimation of the specific growth rate in the bacitracin fermentation process

Cited by 6 publications

References 13 publications

Software Sensor for Biomass Concentration Monitoring During Industrial Fermentation

Software Sensor for Biomass Concentration Monitoring During Industrial Fermentation

State and specific growth estimation in heterologous protein production by Pichia pastoris

Multi-Phase Support Vector Regression Soft Sensor for Online Product Quality Prediction in Glutamate Fermentation Process

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