HYBRID CONNECTIONIST MODELING OF THE KINETICS OF THERMAL-DECOMPOSITION PROCESSES

The kinetics of thermogravimetric decomposition reactions are often di fficult to model explicitly, because model parameters can depend on pr ocess conditions in an ill-defined way. Implicit kinetic models, such as those based on neural nets, often require extensive data and are no t usually suitable for extrapolation of experimental data. In this pap er it is shown that by combining explicit phenomenological models with artificial neural nets, more accurate modelling and extrapolation of these types of processes can be achieved.