This paper describes the training and implementation of Artificial Neural Network models for the true-time, on-line estimation of crucial biological variables in a fed-batch yeast fermentation procedure. The neural networks are generic nonlinear models that are configured and trained to act as software program sensors for biomass concentration.
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In order to effectively customise a neural sensor it is required to give considerable consideration to the choice of network inputs. The job of selecting these inputs is somewhat eased by know-how of the yeast’s metabolism supplemented exactly where necessary by a statistical analysis of the obtainable inputs and outputs. In this study, the inputs that were chosen reflected both the yeast’s metabolic activity and its price of development.