A combined neural network and simulated annealing based inverse technique to optimize the heat source control parameters in heat treatment furnaces

摘要

This study proposes the use of artificial neural network (ANN) based surrogate framework along with simulated annealing (SA) approach to inversely estimate the optimum values of heat source control parameters in a heat treatment furnace. In particular, a two-dimensional radiant furnace with gas fired heaters has been considered and the heat source control parameters for a general gaussian heating profile are estimated to achieve better heat flux uniformity at the specimen. To expedite the forward radiative transfer calculations, ANN based surrogate is developed and coupled with SA. The maximum difference in radiative transfer solution and ANN is found to be less than 6%. Results indicate that the uniformity of fluxes is largely dependent on the emissivity of the specimen and its overall length, the dependence on specimen temperature and gas concentration is minimal. Cross validation of optimum heating profiles with radiative transfer solver shows an excellent match in local heat flux predictions. Overall, combined ANN-SA based algorithm proves to be an accurate and fast tool in heat source control parameter optimization problem.