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.

中文翻译：

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基于神经网络和模拟退火的逆向技术优化热处理炉热源控制参数

本研究建议使用基于人工神经网络 (ANN) 的替代框架以及模拟退火 (SA) 方法来逆估计热处理炉中热源控制参数的最佳值。特别是，已经考虑了带有燃气加热器的二维辐射炉，并估计了一般高斯加热曲线的热源控制参数，以在试样上实现更好的热通量均匀性。为了加快前向辐射传输计算，开发了基于 ANN 的代理并与 SA 结合。发现辐射传输解决方案和人工神经网络的最大差异小于 6%。结果表明，通量的均匀性在很大程度上取决于样品的发射率及其总长度，对样品温度和气体浓度的依赖性最小。使用辐射传递求解器对最佳加热曲线的交叉验证显示了局部热通量预测的极好匹配。总的来说，基于 ANN-SA 的组合算法被证明是解决热源控制参数优化问题的准确而快速的工具。