Soft sensors for on-line biomass measurements.
One of the difficulties encountered in control and optimisation of bioprocesses is the lack of reliable on-line sensors for their key state variables. This paper investigates the suitability of using on-line recurrent neural networks to predict biomass concentrations. Input variables of the proposed recurrent neural network are feed rate, liquid volume and dissolved oxygen. Experimental results revealed that the proposed neural network is able to predict biomass concentrations with an accuracy of +/-11%.
Department of Electrical and Electronic Engineering, The University of Auckland, Private Bag, 92019 Auckland, New Zealand.
Lei Zhi
LZ
Chen
Sing Kiong
SK
Nguang
Xue Mei
XM
Li
Xiao Dong
XD
Chen
D000465:Algorithms; D018533:Biomass; D019149:Bioreactors / Q000382:microbiology*; D003198:Computer Simulation; D003470:Culture Media; D005285:Fermentation; D016571:Neural Networks, Computer; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012107:Research Design; D012441:Saccharomyces cerevisiae / Q000254:growth & development*
D003470:Culture Media