AI-Based Support for Experimentation in an Environmental Biotechnological Process

Choinski, Dariusz; Metzger, Mieczysław; Nocoń, Witold; Polaków, Grzegorz; Skupin, Piotr

doi:10.1007/978-3-642-35455-7_15

Cited by 1 publication

(2 citation statements)

References 24 publications

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“…Then, based on the given conditions, we will discuss the problem of model selection of the bioprocess and estimation of the key process parameters. The presented results are based on our earlier researches [4,[9][10][11].…”

Section: Primary and Secondary Growth Of Microorganismsmentioning

confidence: 99%

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Direct metabolic activity measurement for unstable bioprocess experiment control

Choinski¹,

Skupin²

2015

Int. J. DNE

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Paper deals with an algorithm which allows the automatic selection of the best operating point of biological system. This task is one of the subjects of research in the field of metabolic engineering, which deals with control related issues, in particular, with modelling of biological phenomena, as well as, monitoring of the unstable states of biomass growth. The possibility of using specific biosensors and microfluidic system for monitoring, optimizing and controlling of a bioreactor is presented in this paper. To ensure proper experiment control of the bioreactor, a real-time measurement of parameters at the macroscale level and metabolic activity of microorganism cells at the microscale level are relevant. Therefore, much attention has been paid to the description and modelling of cyclical changes in metabolic states. For the determination of key process parameters, a microcalorimeter for measuring the heat of reaction has been applied. The biosensor provides additional information, which is useful in development of an interface for monitoring the bioreactor by a decomposition of measurements including the scale of process. Finally, the paper discusses the problem of model selection describing the bioprocess at the microscale level.

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Section: Primary and Secondary Growth Of Microorganismsmentioning

confidence: 99%

“…These effects are usually the result of many cyclical changes and are not visible on a macroscopic scale, but the results of the different stages can be observed only at the micro level. For this reason, the number of variables necessary for observation and modelling, even for simple controls, is significant (Choinski et al, [4]). …”

Section: Introductionmentioning

confidence: 99%

Direct metabolic activity measurement for unstable bioprocess experiment control

Choinski¹,

Skupin²

2015

Int. J. DNE

View full text Add to dashboard Cite

show abstract

AI-Based Support for Experimentation in an Environmental Biotechnological Process

Cited by 1 publication

References 24 publications

Direct metabolic activity measurement for unstable bioprocess experiment control

Direct metabolic activity measurement for unstable bioprocess experiment control

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