The municipal solid waste incineration(MSWI)process usually relies on operating experts to observe the flame inside furnace for recognizing the combustion states.Then,by combining the experts'own experience to modify the control strategy to maintain the stable combustion.Thus,this manual mode has disadvantages of low intelligence and the subjectivity and randomness recognition results.The traditional methods are difficult to apply to the MSWI process,which has the characteristics of strong pollution,multiple noise,and scarcity of samples un-der abnormal conditions.To solve the above problems,a combustion states recognition method of MSWI process based on mixed data enhancement is proposed.Firstly,combustion states are labeled by combining the experience of domain experts and the design structure of furnace grate.Next,a deep convolutional generative adversarial net-work(DCGAN)consisting of two levels of coarse and fine-tuning was designed to acquire multi-situation flame im-ages.Then,the Fréchet inception distance(FID)is used to adaptively select generated samples.Finally,the sample features are enriched at the second time by using non-generative data enhancement strategy,and a convolutional neural network is constructed based on the mixed enhanced data to recognize the combustion state.Experiments based on actual operating data of a MSWI plant show that this method effectively improves the generalization and robustness of the recognition network and has good recognition accuracy.
关键词
城市固废焚烧/深度卷积生成对抗网络/燃烧状态识别/非生成式数据增强/混合数据增强
Key words
Municipal solid waste incineration(MSWI)/deep convolutional generative adversarial network(DCGAN)/combustion states recognition/non-generation data enhancement/mixed data enhancement
维普
万方数据