In the present work the inverse problem of simultaneous heat and mass transfer modeled by Luikov equations is studied using a hybrid combination of the Levenberg-Marquardt (LM), Simulated Annealing (SA) and Artificial Neural Network (ANN) methods. The direct and inverse problems are described, formulated and solved. After the use of an experiment design technique, the hybrid combination ANN-LM-SA yielded good estimates for the heat and mass transfer problem of interest. The proper choice of the set of parameters to be estimated allowed the design of an experiment with higher sensitivity coefficients. One ANN was used to generate the initial guess for the LM, another one to approximate the gradient needed by LM, and, finally, the global minimum was searched using the SA. The experimental data considered in the inverse problem was generated using the solution for the direct problem with the addition of noise.
inverse problem; design of experiment; drying; Luikov equations; heat and mass transfer
