Product identification in industrial batch fermentation using a variable forgetting factor

Gjerkeš, Henrik; Malenšek, Jože; Sitar, Anže; Golobič, Iztok

doi:10.1016/j.conengprac.2011.06.011

Cited by 8 publications

(6 citation statements)

References 42 publications

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“…The aim of such modeling is to make use of all available information and knowledge of the process. Various hybrid models for fermentation processes have been reported in literature [58][59][60][61][62]. Based on the hybrid modeling methods, it is possible to develop more efficient feeding control methods than that based on independent mechanistic and data-driven models.…”

Section: Open Issues and Future Steps Of Feeding Controlmentioning

confidence: 99%

Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process

et al. 2014

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Section: Open Issues and Future Steps Of Feeding Controlmentioning

confidence: 99%

Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process

et al. 2014

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“…This variance-based weighting is based on the weighting strategy of the Kalman filter [12]. For all three submodels remaining for combination, the fault-tolerant prediction 𝑦 𝑒𝑛𝑠𝑒𝑚𝑏𝑙𝑒𝑑 resulted in Equation (5).…”

Section: Normalized Variance-based Weighting Of Submodelsmentioning

confidence: 99%

“…However, several secondary variables are linked to biomass concentration and can be used for prediction in soft sensor models. Existing literature suggests the online measurement of turbidity [ 1 ], infrared [ 2 , 3 ], or fluorescence spectra [ 4 ], as well as O 2 and CO 2 concentration in the exhaust gas [ 5 , 6 ]. Furthermore, actuator information such as the addition of pH correction agents [ 6 ] can be suitable, as well.…”

Section: Introductionmentioning

confidence: 99%

Ensemble‐based adaptive soft sensor for fault‐tolerant biomass monitoring

Siegl

Brunner

Geier

et al. 2022

Engineering in Life Sciences

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The accuracy and precision of soft sensors depend strongly on the reliability of underlying model inputs. These inputs (particularly readings of hardware sensors) are frequently subject to faults. This study aims to develop an adaptive soft sensor capable of reliable and robust biomass concentration predictions in the presence of faulty model inputs for a Pichia pastoris bioprocess. Hence, three soft sensor submodels were developed based on three independent model inputs (base addition, CO 2 production, and mid-infrared spectrum). An ensemble-based algorithm combined the submodels to form an ensemble model, that is, an adaptive soft sensor, to achieve fault-tolerant prediction. The algorithm's basic steps are as follows: the initial determination of submodel reliability is followed by selecting appropriate submodels to generate a reliable prediction via variance-based weighting of the submodels. The adaptive soft sensor demonstrated high robustness and accuracy in biomass prediction in the presence of multiple simulated sensor faults (RMSE = 0.43 g L −1 ) and multiple real sensor faults (RMSE = 0.70 g L −1 ).

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“…There are already classical ways for measuring parameters such as pH and dissolved oxygen that provide essential and very general information about the running process. Other techniques have been developed to provide more detailed information on the bioprocess, for example exhaust gas analysis, which allows conclusions about the kind of metabolic activity (e.g., respiratory quotient) (Anderlei et al, 2004; Erickson et al, 1978; Gjerkes et al, 2011). Impedance measurement can be used for online determination of viable biomass (Knabben et al, 2010a, 2011; Maskow et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Non‐invasive online detection of microbial lysine formation in stirred tank bioreactors by using calorespirometry

Regestein

Maskow

Tack

et al. 2013

Biotech & Bioengineering

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Non-invasive methods for online monitoring of biotechnological processes without compromising the integrity of the reactor system are very important to generate continuous data. Even though calorimetry has been used in conventional biochemical analysis for decades, it has not yet been specifically applied for online detection of product formation at technical scale. Thus, this article demonstrates a calorespirometric method for online detection of microbial lysine formation in stirred tank bioreactors. The respective heat generation of two bacterial strains, Corynebacterium glutamicum ATCC 13032 (wild-type) and C. glutamicum DM1730 (lysine producer), was compared with the O2 -consumption in order to determine whether lysine was formed. As validation of the proposed calorespirometric method, the online results agreed well with the offline measured data. This study has proven that calorespirometry is a viable non-invasive technique to detect product formation at any time point.

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Product identification in industrial batch fermentation using a variable forgetting factor

Cited by 8 publications

References 42 publications

Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process

Hybrid intelligent control of substrate feeding for industrial fed-batch chlortetracycline fermentation process

Ensemble‐based adaptive soft sensor for fault‐tolerant biomass monitoring

Non‐invasive online detection of microbial lysine formation in stirred tank bioreactors by using calorespirometry

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