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Biomedical subjects

J Glassey

Publications and source records attributed to J Glassey.

4 recordsLinked to original sources

Physiological state-specific models in estimation of recombinant Escherichia coli fermentation performance.

Implementation of advanced control strategies in bioprocesses is often hindered by the lack of on-line measurements reflecting the physiological state of the culture. Although a number of techniques have been used to estimate key variables from data monitored on-line, these often do not explicitly take into account changes in physiological state and information on many aspects of physiological state that may not be present in on-line data. Here we demonstrate that data obtained from chemical fingerprinting methods, such as pyrolysis mass spectrometry, can be used to identify changes in the physiological state during cultivation. This information can be utilized for the estimation of the physiological state and can enable physiological state-specific-model development for on-line bioprocess control.

Biotechnology↗

Issues in the development of an industrial bioprocess advisory system.

The background and motivation for the construction of a fault detection and advisory system for an industrial fermentation process plant are described. Here, the knowledge extracted from the operators (implemented in the form of production rules) is integrated with multivariate data-based methods for fault detection. The industrial benefits arising from this integrated system include: (1) reduced variability, (2) increased mean performance levels, (3) reduced operator-training time and (4) knowledge management in the broader organization.

Algorithms↗

Fermentation seed quality analysis with self-organising neural networks

Industrial fermentation processes operate under well defined operating conditions to attempt to minimise production variability. Variability occurs for many reasons but a long held belief is that variation in the state of the seed is highly influential. In this paper a seed stage (a batch process) of an industrial antibiotic fermentation is considered and the performance of the main production fermentations is correlated with the quality of the seed using an unsupervised Kohonen self-organising feature map (SOM). It is shown that using only seed information poor performance in the final stage fermentations can be predicted. Data from industrial penicillin G fermenters is used to demonstrate the procedure. Copyright 1999 John Wiley & Sons, Inc.

Journal Article↗

Bioprocess supervision: neural networks and knowledge based systems.

Supervision of highly non-linear and time variant bioprocesses is of considerable importance to bioindustries as a means of achieving improved productivity and reduced process variability. Artificial intelligence methodologies, including artificial neural networks and knowledge based systems, can contribute significantly to the achievement of these objectives.

Biosensing Techniques↗