| Summary: | The tangentially fired furnace has the advantages of sufficient combustion and low NOx emission, and the smoke black concentration in the furnace reflects the combustion situation of combustion equipment. The flame field and smoke black concentration field in the furnace are complex, so it is meaningful to study the flame field and the smoke black concentration field of the tangential furnace. In this article, the visual method was used to reconstruct the flame and smoke black concentration fields in the furnace. Due to the low resolution of industrial cameras, the final reconstruction effect is limited. Given this situation, this article proposes a strengthen edge characteristic super-resolution network (SECSR) algorithm suitable for the tangentially fired furnace flame images. The flame edge processing is added to the depth neural network, which enhances the ability of flame edge feature extraction. It constructs a generative adversarial network which can greatly improve the resolution of furnace flame image, to obtain high-resolution tangential furnace flame image. Secondly, based on the high-quality flame image, this article proposes an inversion algorithm of the black concentration field of the tangentially fired furnace smoke. The algorithm obtains the high-precision tangentially furnace flame temperature field through an inversion calculation to calculate the soot concentration field. In the future work, we will predict the variation trend of smoke black concentration in the furnace through the results of the inversion calculation model, to understand the combustion situation in the furnace and control the fuel consumption to reduce smoke black emission, which has important guiding significance for protecting the environment and saving resources.
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