Heat and mass transfer models in convection drying of clay slabs

Drying kinetics data for convection drying of industrially prepared roof tile clay slab were approximated with different mathematical models. Applied conventional model analysis enables evaluation of main transport properties: effective diffusion coefficient, mass and heat transfer coefficients, thermal conductivity, drying constant, and exponential model parameters. A neural network-based drying model was established using backpropagation algorithm for dynamics modelling of moisture content and temperature of thin clay sample. Obtained results confirm the assumption that both, the heat and mass transfers, are under external conditions. Very small values of Biot numbers confirm that fact. Drying air temperature and initial moisture content of clay strongly influence the drying kinetics and transport properties. The dependence between the drying air temperature and evaluated transport properties shows an exponential trend. Tomas and Skansi exponential model parameter, n, is independent from temperature. At lower values of initial moisture content of clay higher drying rates are achieved, which results with higher values of calculated transport properties. It was shown that neural network as an alternative method has potential for modelling the drying process and predicting drying dynamics based on experimental data.