Prediction of NOx emissions for municipal solid waste incineration processes using attention modular neural network
Real-time and accurate measurement of NOx emissions is indispensable to achieve closed-loop control of the denitrification process during municipal solid waste incineration(MSWI).To this end,this paper proposes a NOx emission prediction method for the MSWI process based on attention modular neural network(AMNN).First,it simulates the"divide and conquer"characteristics of the brain network in processing complex tasks,and uses the fuzzy C-means(FCM)clustering algorithm to divide the task to be predicted into multiple subtasks,thereby reducing the complexity of the prediction task.Second,to handle the sub-tasks efficiently,a self-organizing fuzzy neural network(SOFNN)is designed to construct the sub-models,in which a growing and pruning algorithm and an improved second-order learning algorithm work together to ensure both the learning efficiency and accuracy.Then,the attention mechanism is utilized to integrate the sub-models during the testing or application stages,which can further improve the generalization performance of this AMNN-based prediction model.Finally,the proposed prediction method is verified by Mackey-Glass time series and the real data from a MSWI plant in Beijing.
municipal solid waste incinerationmodular neural networkattention mechanismNOx emissions prediction
万方数据
维普
