NOx emissions prediction for MSWI process based on dynamic modular neural network

Accurate prediction of nitrogen oxides (NOx) is crucial for improving the efficiency of denitrification systems in municipal solid waste incineration (MSWI) process. Due to the change of feed composition and operation mode, it is difficult to predict NOx emissions with complex dynamics. For this reason, a dynamic modular neural network (DMNN) is proposed for NOx emissions prediction in MSWI process. First, a principal component analysis (PCA)-based dynamic task decomposition method is proposed, and then the original task with time varying characteristic is divided into several sub-tasks for effectual handing. Next, an adaptive long short-term memory (ALSTM) network is designed driven by the corresponding sub-task. Then, the nonlinearity between dominant variables and NOx value is learned to guarantee the prediction accuracy. Finally, the merits of proposed DMNN are confirmed on a benchmark and real industrial data of a MSWI process. The experimental results further demonstrate the superiority and potential of DMNN for industrial applications.