Wood dielectric loss factor prediction with artificial neural networks

An artificial neural network that can predict the dielectric properties of wood was developed and tested with experimental data. The network was capable of accurately predicting the loss factor of two wood species not only as a function of ambient electro-thermal conditions but also as a function of basic wood chemistry. This way, an important predictive tool is created that allows optimization of dielectric heating and drying for many wood species without significant experimentation should their chemical composition be known under variable temperatures, moisture contents and electric filed characteristics.