Multiblock dynamic enhanced canonical correlation analysis for industrial MSW combustion state monitoring

Combustion state monitoring in industrial municipal solid waste incineration (MSWI) power generation process is very difficult. There are two main reasons for this dilemma: (1) there is a large time lag between the combustion chamber and the steam generation stage; (2) reactions continue to occur and raw waste is fed into the incinerator continuously, so there are strong correlations and remarkable dynamic characteristics among process variables. The complex correlations in time and in space cause difficulties in combustion state monitoring for incinerators. Those correlations are common in industrial processes because the large scale equipment is usually needed and a whole process is realized by equipment in sequence. To overcome those difficulties, a multi-block dynamic enhanced canonical correlation analysis (MBDCCA) method is proposed to help monitor the MSWI process. Firstly, canonical correlation analysis (CCA) is extended to MBDCCA to model the MSWI process. Secondly, a combustion state online monitoring approach is designed based on the proposed MBDCCA algorithm. Finally, the proposed method is applied in an industrial MSWI plant to demonstrate the effectiveness.

Automated summary

Combustion state monitoring in industrial MSWI power generation process is challenging due to the time lag between the combustion chamber and steam generation stage, as well as the continuous addition of raw waste. To overcome these difficulties, a MBDCCA method is proposed, which extends CCA and incorporates dynamic enhancement. An online monitoring approach based on MBDCCA algorithm is designed and applied in an industrial MSWI plant to demonstrate its effectiveness.