Construction of flame image classification criteria and reference database for municipal solid waste incineration process

The flame combustion state inside the furnace is important feedback information for intelligent optimization control of the municipal solid waste incineration (MSWI) process. However, none of the existing studies on the identification of MSWI combustion state have proposed a combustion state classification criteria, which with actual physical significance and interpretability for the incineration characteristics of MSWI. As a result, it is difficult to build a recognition model for the MSWI process based on the domain expert cognitive mechanism and valid reference data. To solve this problem, combined with the experience of industry experts and research results in related fields, the construction of MSWI process flame combustion state classification criteria and benchmark database was studied in this paper. Firstly, the problem of combustion slate identification is described, and the existing methods of combustion state identification based on combustion lines arc analyzed. Next, the classification criteria are elaborated based on normal combustion, partial combustion, channeling combustion and smoldering combustion. Then, the image database of the flame-burning state which can be used for machine learning is constructed. Finally, the flame-burning image database is modeled and tested based on a variety of classical algorithms in the field of machine vision. The results show that the accuracy of most methods for flame slate classification is more than 80%. The validity of the proposed classification criteria and flame image database is greatly validated.