The invention belongs to the technical field of ash
pollution monitoring, and discloses a transfer learning-based boiler heating surface ash
pollution quantitative characterization method, which comprises the following steps of: establishing a boiler-boiler
coupling model of a
research object boiler by virtue of analog
simulation software, and adopting a method of combining
simulation modeling with actual
verification. And the transfer learning-based boiler heating surface ash
dirt quantitative characterization method is researched. The model and the
dirt label which are established in the
simulation data and are used for verifying the accuracy are migrated to the same actual heating surface for
dirt quantitative characterization modeling, so that the modeling difficulty is reduced, the modeling speed is increased, the required data volume is small, and the dust deposition trend of the actual heating surface can be obtained through effective fitting. The method effectively solves the problem that the actual operation data of the boiler unit lacks a characterization dirt
label and cannot provide guidance for subsequent
soot blowing optimization, and has a certain application prospect.