Robust stochastic configuration network based perception model for furnace temperature in municipal solid waste incineration process

Addressing the challenging issue of reduced accuracy in furnace temperature perception models due to asymmetric outliers in operational data of the municipal solid waste incineration process, we propose a novel robust stochastic configuration network perception model for furnace temperature in municipal solid waste incineration processes. A skewed t distribution with the heavy-tailed characteristic is adopted to model the prior distribution of outliers in the operational data of the solid waste incineration process. Subsequently, the maximum likelihood estimation method is employed to solve the output weights of the furnace temperature perception model. Additionally, the output weights and prior distribution position hyperparameters are updated by the expectation-conditional maximization algorithm. Comparative experimental results demonstrate the superiority of our proposed method in terms of both accuracy in furnace temperature perception and robustness against outliers. Our approach holds significant potential for application in critical fields such as furnace temperature prediction and optimal control during the incineration process.