Modular stochastic configuration network prediction interval for furnace temperature in municipal solid waste incineration

Abstract

The high dimensionality of characteristic variables and the presence of numerous uncertain factors affecting furnace temperature during municipal solid waste incineration can lead to poor accuracy and generalization ability for furnace temperature prediction. This paper adopts the modular neural network modeling approach and incorporates Gaussian process regression analysis into stochastic configuration networks to propose a method for establishing a furnace temperature prediction interval model. First, a Gaussian mixture model is used to decompose the complex task into several subtasks. Then, considering the differences among the subtasks, Gaussian process regression with different kernel functions is combined with a stochastic configuration network to form corresponding base models, which are trained and learned. The prediction interval results are obtained through blending ensemble methods. Finally, the effectiveness of the proposed method is tested using historical data obtained from the municipal solid waste incineration process. The results indicate that the furnace temperature prediction model demonstrates advantages in terms of accuracy and generalization ability, making it applicable to the field of process parameter modeling.