Extension of the neural networks operating range by the application of dimensionless numbers in prediction of heat transfer coefficients

The paper presents a study aimed at extending the neural network mapping ability. In traditional modelling, operational process parameters (gas/material temperature, air velocity, etc.) are the inputs and outputs to and from the network. In this approach dimensionless numbers (Re, Ar, H/d) were used as inputs to predict the heat transfer coefficient in a fluidised bed drying process. To produce the data set necessary to train the networks, drying trials of different materials in a fluidised bed were carried out. A series of simulations were performed and several neural networks structures were tested to find an optimal topology of the network. Training data set contained information only about two materials. The networks were tested using data obtained for the third product. Performance of the network was satisfactory, however further improvement of mapping ability may be expected after filtration of the testing data.