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Coal combustion is the primary source of air pollution in China, especially the heavy metals (HMs) concentrated in the submicron particles. This paper discusses the enhancement of HMs absorption efficiencies during coal combustion experiments. By adding the modified Ca-based sorbents, the orthogonal design was used in this experiment. Combining with the analysis of variance and capturing rate of HMs, the optimal combination was arrived at. BP neural network model was established and trained using orthogonal capturing tests data to reach the best compositions. Confirmatory trial was conducted, thus the BP neural network model can be used to solve complex nonlinear optimization problems. [ABSTRACT FROM AUTHOR]