Software sensors in bioprocess engineering

Chéruy, A.

doi:10.1016/s0168-1656(96)01644-6

Cited by 126 publications

(55 citation statements)

References 4 publications

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“…A soft sensor (or software sensor) utilizes one or several hardware sensor signals with mathematical models for acquiring on-line process data that cannot easily be measured in real time [1][2][3]. A variety of combinations of soft sensor is reported for process monitoring and control applications in industry [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Gustavsson

Mandenius

2012

Bioprocess Biosyst Eng

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A soft sensor approach is described for controlling metabolic overflow from mixed acid fermentation and glucose overflow metabolism in a fed-batch cultivation for production of recombinant green fluorescence protein (GFP) in Escherichia coli. The hardware part of the sensor consisted of a near-infrared in situ probe that monitored the E. coli biomass and an HPLC analyzer equipped with a filtration unit that measured the overflow metabolites. The computational part of the soft sensor used basic kinetic equations and summations for estimation of specific rates and total metabolite concentrations. Two control strategies for media feeding of the fed-batch cultivation were evaluated: (1) controlling the specific rates of overflow metabolism and mixed acid fermentation metabolites at a fixed pre-set target values, and (2) controlling the concentration of the sum of these metabolites at a set level. The results indicate that the latter strategy was more efficient for maintaining a high titer and low variability of the produced recombinant GFP protein.

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Section: Introductionmentioning

confidence: 99%

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Gustavsson

Mandenius

2012

Bioprocess Biosyst Eng

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“…Hence, there is a need for some feedback mechanisms based on on-line measurements. On-line adaptation is possible when the state variables can be measured online 10 (directly using hardware sensors or indirectly by soft sensors [31,32]). …”

Section: B Bioprocess Adaptive Controlmentioning

confidence: 99%

Bioprocess Modeling and Control

Caramihai¹,

Severin²

2013

Biomass Now - Sustainable Growth and Use

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“…Due to the great importance of this process and the increase in industrial applications of enzyme technology, studies focusing on bioprocess monitoring and control have been conducted (Chéruy, 1997). Particularly for bioreactors, there is still a lack of reliable, durable and low-cost sensors for measuring on-line key variables such as concentration of cells, substrates and products.…”

Section: Introductionmentioning

confidence: 99%

“…Various authors have presented results of computational algorithms that use the available on-line measurements to infer important state variables (see, for instance, Linko et al 1997 andChéruy, 1997).…”

Section: Introductionmentioning

confidence: 99%

A fuzzy logic algorithm for identification of the harvesting threshold during PGA production by Bacillus megaterium

Nucci

Silva

Gomes

et al. 2005

Braz. J. Chem. Eng.

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-Penicillin G acylase (PGA) is an important enzyme used as biocatalyst in the production of semisynthetic β-lactam antibiotics. Many microorganisms produce this enzyme and recombinant Escherichia coli has been preferred use for industrial applications. Bacillus megaterium is one of the microorganisms that excretes this enzyme into the medium. As a consequence, separation and purification steps are simplified. Online measurement of enzyme activity during cultivation using in-situ sensors is a difficult task in the industrial environment due to the lack of robust and inexpensive instrumentation. This work presents the results of a fuzzy logic algorithm used to determine the moment of maximum enzyme concentration during Bacillus megaterium cultivations in an aerated and stirred, automated lab-scale bioreactor. The fuzzy algorithm was written in Fortran, compiled as a dynamic link library and implemented on a platform developed in MS-Visual Basic. Data were exchanged in real time between the platform and the supervisory system, which was coupled to the bioreactor. It was possible to determine the moment at which maximum enzyme activity was reached in several bioreactor assays. At this point, the end of the process was indicated to the operator. The results illustrate the importance of using reliable computational intelligence-based algorithms in biochemical reactions.

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Software sensors in bioprocess engineering

Cited by 126 publications

References 4 publications

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Soft sensor control of metabolic fluxes in a recombinant Escherichia coli fed-batch cultivation producing green fluorescence protein

Bioprocess Modeling and Control

A fuzzy logic algorithm for identification of the harvesting threshold during PGA production by Bacillus megaterium

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