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Soft sensors for on-line biomass measurements
Lei Zhi Chen1, Sing Kiong Nguang, Xue Mei Li
1Department of Electrical and Electronic Engineering, The University of Auckland, Private Bag, 92019 Auckland, New Zealand.
Bioprocess and Biosystems Engineering
|March 3, 2004
Abstract:
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%.