Deep Learning Models for SO Distribution in a 30 MW Boiler via Computational Fluid Dynamics Simulation Data.

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Abstract

The distribution of SO2 in a boiler is an important factor affecting tube corrosion in a furnace. To investigate the correlation between SO2 distribution and numerous variables (e.g., temperature, O2 distribution, etc.), a hybrid deep learning model is developed via the computational fluid dynamics (CFD) simulation data. First, the combustion process under typical working conditions is simulated to output the training data set. Then, a LASSO algorithm is adopted to select input variables with a high correlation with SO2 distribution. Finally, a deep belief network combined with a restricted belief machine and a fully connected layer is developed to describe the nonlinear relationship. The proposed model is the first work to use a deep learning algorithm to obtain the correlation between SO2 distribution and other products of combustion. The results show that O2 concentration has the highest influence on SO2 distribution.© 2022 The Authors. Published by American Chemical Society.