Inverse estimation of number and location of discrete heaters in radiant furnaces using artificial neural networks and genetic algorithm

The inverse problem of the determination of optimum number and location of discrete heaters in a three dimensional radiant heating furnace is solved by developing an artificial neural network based model to replace the radiative transfer equation solver and coupling it with genetic algorithm. The furnace medium is considered to be participating with a mixture of H 2 O and CO 2 . For the purpose of estimation of the best configuration of the heaters to achieve a desired uniform heat flux at the design surface, the inverse problem has been treated as an optimization problem. A generalized radiative transfer code based on Finite Volume Method and spectral line based weighted sum of gray gases method has been developed and validated with the available benchmarks. This code serves as the basis to develop the neural network based model for the flux estimation at design surface. The ANN function has been tested upon 5 random test cases. The ANN based replacement of RTE for this problem gives an error less than 4% for the local heat flux values at the order of 10 3 times less computation time. A design case is then considered where the desired flux levels should be uniform on the design surface in a very tight tolerance. Results obtained show that this novel method of ANN coupled with GA proves to be a fast, robust and efficient tool for solving such types of inverse problems.