PubMed Health⌕ Search

Biomedical subjects

G A Montague

Publications and source records attributed to G A Montague.

6 recordsLinked to original sources
PubMed2002

An assessment of seed quality and its influence on productivity estimation in an industrial antibiotic fermentation.

This study investigates the benefits of including seed quality information into data-based models for final productivity estimation in an industrial antibiotic fermentation process. Multiway principal component analysis is applied to assess the seed quality using routinely gathered plant data. Multiway partial least-squares regression is then used to estimate the final productivity using data from the main fermentation only. The issue of selecting appropriate process variables as inputs is investigated. Subsequently, seed characteristics are included into the estimation models to assess the benefits of including information from this stage for productivity estimation. It is shown that it is possible to extract seed fermentation features related to the final productivity both at pilot and production scales. It is postulated that significant influential variations are mirrored in monitored variables during the main fermentation, and therefore seed quality is implicitly accounted for.

Anti-Bacterial Agents↗
PubMed2001

Process monitoring of an industrial fed-batch fermentation.

Market demand places great emphasis in industry on product quality. Consequently, process monitoring and control have become important aspects of systems engineering. In this article we detail the results of a 2-year study focusing on the development of a condition monitoring system for a fed-batch fermentation system operated by Biochemie Gmbh in Austria. We also demonstrate the suitability and limitations of current state of the art technologies in this field and suggest novel modifications and configurations to improve their suitability for application to a fed-batch fermentation system.

Algorithms↗
PubMed1997

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↗
PubMed1993

Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks).

The ability to supervise and control a highly non-linear and time variant bioprocess is of considerable importance to the biotechnological industries which are continually striving to obtain higher yields and improved uniformity of production. Two AI methodologies aimed at contributing to the overall intelligent monitoring and control of bioprocess operations are discussed. The development and application of a real-time knowledge-based system to provide supervisory control of fed-batch bioprocesses is reviewed. The system performs sensor validation, fault detection and diagnosis and incorporates relevant expertise and experience drawn from both bioprocess engineering and control engineering domains. A complementary approach, that of artificial neural networks is also addressed. The development of neural network modelling tools for use in bioprocess state estimation and inferential control are reviewed. An attractive characteristic of neural networks is that with the appropriate topology any non-linear functional relationship can be modelled, hence significantly reducing model-process mismatch. Results from industrial applications are presented.

Artificial Intelligence↗
PubMed1992

Enhancing bioprocess operability with generic software sensors.

This paper discusses the concept of generic model-based software sensors with particular reference to bioprocess application. The industrial need for software sensing is considered and two industrial bioprocess systems are used in order to highlight the operability problems which warrant their development. Alternative philosophies for formulation are considered and the relative merits of the methodologies discussed. In particular, two different procedures are presented--an adaptive linear model based method, and a method based upon artificial neural networks. The performances of these alternative approaches are studied by their application to two industrial demonstrator processes. The results serve to highlight the operability improvements that can be gained through software sensor development.

Biosensing Techniques↗