MODELING OF A WOODCHIP REFINER USING ARTIFICIAL NEURAL-NETWORK

Neural networks can be an attractive alternative to mathematical modelling of complex and poorly understood processes if input/output data can easily be obtained. Woodchip refining falls into this category. The mechanism of the refining process is still being studied and no thorough models have yet been developed. A feed-forward neural network is proposed for modelling of woodchip refiners. The outputs predicted by the neural network are compared with industrial refiner data. It is also shown that a modified neural network structure can be used to optimize refiner operation and product quality. The advantages and disadvantages of neural network model application in simulation and optimization of industrial processes are discussed.