TY  - JOUR
T1  - Bioprocess Monitoring Using Local Model Networks
AU - , Azzeddine Amri AU - , Messaoud Ramdani AU - , Mouldi Bedda 
JO  - Asian Journal of Information Technology
VL  - 6
IS  - 2
SP  - 230
EP  - 235
PY  - 2007
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2007.230.235
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2007.230.235
KW  - Multi-model
KW  -nonlinear dynamics modelling
KW  -fermentation process
KW  -LMN
KW  -soft-sensor
KW  -gluconic acid
AB  - Monitoring of fermentation processes is of great importance to ensure their safe operation and consistent high quality products. Unfortunately, some of the difficulties such as the lack of on-line sensors for indication of fermentation performance, the presence of significant nonlinear behaviour and difficulties in designing accurate mechanistic models limit our ability to provide adequate monitoring. The amount of time and cost involved in developing detailed fundamental models combined with the commercial pressure to reduce the time-to-market requires different modelling, monitoring and control techniques. The local modelling methodology can be used in the design of soft-sensors. In this study, we propose a Local Model Network (LMN) with improved learning scheme for the bioprocess monitoring. The validity of the approach is illustrated on a gluconic acid fermentation process for the design of a soft-sensor to provide an estimation of the product concentration.
ER  -