Instrumentation of Biotechnological Processes

Sonnleitner, Bernhard

doi:10.1007/3-540-48773-5_1

Cited by 27 publications

(30 citation statements)

References 404 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Even macroscopic variables such as the cell mass concentration and those of extracellular metabolites are difficult to monitor easily and rapidly by hardware devices (Mandenius 2004;Sonnleiter 2000). Off-line measurements can require durations of a few minutes to a couple of hours for each sample.…”

Section: Neural Network For Bioreactorsmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Models of the Quantitative Behavior of Microbial Systems

Patnaik

2008

Food Bioprocess Technol

View full text Add to dashboard Cite

Under different environmental conditions, microbial systems display complex behavioral patterns that are difficult to express quantitatively by mechanistic methods. Therefore, two alternate approaches based on different forms of intelligence have emerged. One approach uses methods of artificial intelligence (AI) such as neural networks, expert systems, and genetic algorithms to describe cellular behavior. The second methodology, which leads to the class of cybernetic models, relies on intelligence postulated to be possessed by the cells themselves. While both AI and cybernetic methods have been effective in many applications where mechanistic models are inadequate, all three methods have strengths and weaknesses. This recognition has led to the development of hybrid systems that combine two or more approaches for different aspects of a microbial system. However, the optimum design of hybrid systems still remains heuristic. The rationale and the developments from mechanistic to hybrid models are discussed here, and it is suggested that eventually a truly intelligent system should be self-evolving to maintain itself at the optimum configuration at all times.

show abstract

Section: Neural Network For Bioreactorsmentioning

confidence: 99%

“…Limitations of instrumental methods (Mandenius 2004;Sonnleiter 2000) make it difficult to follow the timedependence of all variables of interest. In particular, assays of intracellular concentrations may require several minutes to a few hours; this duration may be sufficiently long to allow a disturbance to disrupt a fermentation process.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Models of the Quantitative Behavior of Microbial Systems

Patnaik

2008

Food Bioprocess Technol

View full text Add to dashboard Cite

show abstract

“…Measurement, monitoring, modelling and control (M 3 C) are critical to biochemical engineering due to the complexity inherent in the biological systems and molecules used [1–3]. The recent interest in M 3 C methodology from the regulatory authorities for pharmaceuticals has been clearly stated in the Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidance and recommendation documents [4–5], as well as in the quality guidelines from the International Conference on Harmonisation (ICH) [6].…”

Section: Background Of the Workhopmentioning

confidence: 99%

“…The literature reveals that a wide variety of measurement techniques have been applied to monitoring bioprocesses [1–3]. Table 1 summarizes prominent and frequently reported results.…”

Section: Scientific and Technological State‐of‐the‐art And Challengesmentioning

confidence: 99%

Soft sensors in bioprocessing: A status report and recommendations

Luttmann

Bracewell

Cornelissen

et al. 2012

Biotechnology Journal

192

115

View full text Add to dashboard Cite

The following report with recommendations is the result of an expert panel meeting on soft sensor applications in bioprocess engineering that was organized by the Measurement, Monitoring, Modelling and Control (M3C) Working Group of the European Federation of Biotechnology ‐ Section of Biochemical Engineering Science (ESBES). The aim of the panel was to provide an update on the present status of the subject and to identify critical needs and issues for the furthering of the successful development of soft sensor methods in bioprocess engineering research and for industrial applications, in particular with focus on biopharmaceutical applications. It concludes with a set of recommendations, which highlight current prospects for the extended use of soft sensors and those areas requiring development.

show abstract

“…However, the on-line measurement of biomass concentration has rarely been featured in common industrial bioprocess control systems, mainly due to the high costs and technical complexity of the state-of-the-art biomass sensors. In the case of biomass measurements, there are only a few sensors, which are generally based on the measurement of physical, chemical, and photometrical variables and occasionally on biological ones (Sonnleitner, 2000). As an alternative, a software sensor represents an on-line estimator based on deterministic, statistical mathematical methods, artificial intelligence techniques, or a combination of the two (Lee et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

On-line estimation of biomass and intracellular protein for recombinant Escherichia coli cultivated in batch and fed-batch modes

Wang

2007

Journal of the Chinese Institute of Chemical Engineers

View full text Add to dashboard Cite

Instrumentation of Biotechnological Processes

Cited by 27 publications

References 404 publications

Intelligent Models of the Quantitative Behavior of Microbial Systems

Intelligent Models of the Quantitative Behavior of Microbial Systems

Soft sensors in bioprocessing: A status report and recommendations

On-line estimation of biomass and intracellular protein for recombinant Escherichia coli cultivated in batch and fed-batch modes

Contact Info

Product

Resources

About