Abstract
A neural network-based strategy for detection of feedstock variations in a continuous pulp digester is presented. A feedforward two-layer perceptron network is trained to detect and isolate unmeasured variations in the feedstock. Training and validation data sets are generated using a rigorous first principles model. The most important issue discussed here is the design of the data set required to train the artificial neural network. Efficiency and limitation of such an approach are demonstrated using simulations.
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