Principal component ANN for modelling and control of baker's yeast production.
Modelling of baker's yeast production by the principal component based artificial neural networks (ANN) is presented. The models are derived for their application in adaptive control of fermentation by the internal model control (IMC) method. Modelling data are from industrial production in 40 m3 deep jet bioreactor and from computer simulations. The modelling effort is focused on selection of ANN structure and model verification. Principal component analysis of process variables results in projection of patterns to a space of low dimension, which enables determination of ANN structure, removes data colinearity and random components of measurement signals, and model degradation by over-training is eliminated. In view of IMC application, the models for prediction of the controlled variable (ethanol partial pressure) and the inverse model for manipulative variable (molasses feed rate) are determined. The models are tested for their predictability in the time horizon from 1 to 20 min. ANN models are derived with average relative errors for untrained patterns in the range from 1 to 10%.