The mechanism of the wood-chip refining process is still being studied
, and no thorough model has yet been developed. Neural networks can be
an attractive alternative to mathematical modeling of complex process
es if a sufficient amount of input-output data is available. This arti
cle examines the use of a feed-forward neural network to model a wood-
chip refiner. The network's predicted outputs compared faborably with
industrial refiner data. It is also shown that the network structure c
an be modified to optimize refiner operation and product quality. Adva
ntages and disadvantages of applying neural-networks models to simulat
e and optimize industrial processes are discussed.