Heterogeneous feature ensemble modeling with stochastic configuration networks for predicting furnace temperature of a municipal solid waste incineration process

Considering the accuracy, generalization ability, stability, and training efficiency of a furnace temperature model in the process of municipal solid waste incineration, a heterogeneous feature ensemble modeling method for furnace temperature is proposed in this paper. First, heterogeneous features are generated according to the operation mechanism of the waste incineration process, and the training subset of the furnace temperature- and grate temperature-based model is determined from the historical data of this process. Second, the base model pools of furnace temperature and grate temperature are constructed by a regularized stochastic configuration network, and a set of optimal base models are retained by selective base model technology. Then, a negative correlation learning strategy is employed to establish a simultaneous training ensemble model of furnace temperature, and a regularized stochastic configuration network is used to establish a secondary training ensemble model of furnace temperature. The final output of the furnace temperature is obtained by the average value of the output of the above two ensemble models. Finally, a comparative experiment is carried out using the historical data of a waste incineration plant. The results show that the furnace temperature model established in this paper has advantages in accuracy, generalization ability, stability, and training efficiency. It can be applied to the field of furnace temperature prediction and control in the waste incineration process.

Automated summary

In this paper, a heterogeneous feature ensemble method is proposed for modeling furnace temperature in the process of municipal solid waste incineration. By constructing multiple base models and utilizing a negative correlation learning strategy, accurate prediction and control of furnace temperature are achieved.