Software sensors for bioprocesses

Bogaerts, Philippe; Wouwer, Alain Vande

doi:10.1016/s0019-0578(07)60005-6

Cited by 61 publications

(24 citation statements)

References 13 publications

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“…Examples of software algorithms that can be seen as soft sensors are, e.g., Kalman filters [109], neural networks [110] or fuzzy computing [111]. Implementing a soft sensor solution requires a sound understanding of the process as a whole and specifically of the variable measurements.…”

Section: Basic Design Characteristics Of Current Dga Monitorsmentioning

confidence: 99%

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Monitoring the Depth of Anaesthesia

Musizza

Ribarič

2010

Sensors

117

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One of the current challenges in medicine is monitoring the patients’ depth of general anaesthesia (DGA). Accurate assessment of the depth of anaesthesia contributes to tailoring drug administration to the individual patient, thus preventing awareness or excessive anaesthetic depth and improving patients’ outcomes. In the past decade, there has been a significant increase in the number of studies on the development, comparison and validation of commercial devices that estimate the DGA by analyzing electrical activity of the brain (i.e., evoked potentials or brain waves). In this paper we review the most frequently used sensors and mathematical methods for monitoring the DGA, their validation in clinical practice and discuss the central question of whether these approaches can, compared to other conventional methods, reduce the risk of patient awareness during surgical procedures.

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Section: Basic Design Characteristics Of Current Dga Monitorsmentioning

confidence: 99%

“…Their common denominator is the reduction of information, which comes from one or more complex signals, into a single feature, which reflects the state of consciousness. This concept puts DGA monitoring systems in the domain of software sensors [109,110]. …”

Section: Basic Design Characteristics Of Current Dga Monitorsmentioning

confidence: 99%

Monitoring the Depth of Anaesthesia

Musizza

Ribarič

2010

Sensors

117

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“…The work was extended by many others, e.g. by Bogards and Van de Wouwer [23] and Venkateswarlu [24] to name only two of them. The Kalman-Filter works recursively and provides one-step-ahead estimates of the state variables.…”

Section: Dynamic State Estimators For Biomass and Its Specific Growthmentioning

confidence: 99%

Bioreactor control improves bioprocess performance

Simutis

Lübbert

2015

Biotechnology Journal

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The performance of bioreactors is not only determined by productivity but also by process quality, which is mainly determined by variances in the process variables. As fluctuations in these quantities directly affect the variability in the product properties, combatting distortions is the main task of practical quality assurance. The straightforward way of reducing this variability is keeping the product formation process tightly under control. Purpose of this keynote is to show that there is enough evidence in literature showing that the performance of the fermentation processes can significantly be improved by feedback control. Most of the currently used open loop control procedures can be replaced by relatively simple feedback techniques. It is shown by practical examples that such a retrofitting does not require significant changes in the well-established equipment. Feedback techniques are best in assuring high reproducibility of the industrial cultivation processes and thus in assuring the quality of their products. Many developments in supervising and controlling industrial fermentations can directly be taken over in manufacturing processes. Even simple feedback controllers can efficiently improve the product quality. It's the time now that manufacturers follow the developments in most other industries and improve process quality by automatic feedback control.

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“…The way of reasoning in PAT has been supported by other regulatory bodies 30,31 and the academic research has been active in pursuing these directions in a variety of aspects such as Quality-by-Design, on-line sensor development and statistical experimental design. 32,33 FDA's PAT guidance 29 mentions six goals that should be accomplished by using the PAT tools: (1) reducing production cycling time; (2) preventing rejection of batches; (3) enabling real time release; (4) increasing automation; (5) improving efficiency of energy and material use; and (6) facilitating continuous processing. These goals are expected to be applicable to all pharmaceutical production, including manufacture of bio-therapeutic protein-based drugs (proteins, antibodies), 34 gene therapy vectors as well as cell therapy products.…”

Section: Introductionmentioning

confidence: 99%

Mini‐review: soft sensors as means for PAT in the manufacture of bio‐therapeutics

Mandenius

Gustavsson

2014

J of Chemical Tech & Biotech

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This mini‐review discusses how soft sensors can contribute to accomplish FDA's process analytical technology (PAT) ambitions in the manufacture of bio‐therapeutics. Focus is on applications with protein‐based drugs (proteins, antibodies), but also gene therapy vectors as well as cell cultures are considered where chemical and bio‐analytical as well as mathematical and statistical methods are used as tools. An overview of existing soft sensor alternatives and how these can be configured to meet typical industrial needs is provided. It is noted how several of these needs coincide with the PAT regulatory incentives but do also address process economic aspects of bio‐therapeutic manufacture. Evaluation of soft sensor alternatives is highlighted in relation to the production targets, quality attributes and the specification of these as well as shortcomings and needs for further improvements. © 2014 Society of Chemical Industry

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Software sensors for bioprocesses

Cited by 61 publications

References 13 publications

Monitoring the Depth of Anaesthesia

Monitoring the Depth of Anaesthesia

Bioreactor control improves bioprocess performance

Mini‐review: soft sensors as means for PAT in the manufacture of bio‐therapeutics

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