公开(公告)号：US20240078410A1

公开(公告)日：2024-03-07

申请号：US18450945

申请日：2023-08-16

Applicant: Beijing University of Technology

Inventor： Junfei Qiao ,  Haoshan Duan ,  Xi Meng ,  Jian Tang

IPC: G06N3/045 ,  F23G5/50

CPC classification number: G06N3/045 ,  F23G5/50 ,  F23G2207/30 ,  F23G2207/60

Abstract: A dynamic modular neural network (DMNN) for NOx emission prediction in MSWI process is provided. First, the input variables are smoothed and normalized. Then, a feature extraction method based on principal component analysis (PCA) was designed to realize the dynamic division of complex conditions, and the prediction task to be processed was decomposed into sub-tasks under different conditions. In addition, aiming each sub-tasks, a long short-term memory (LSTM)-based sub-network is constructed to achieve accurate prediction of NOx emissions under various working conditions. Finally, a cooperative strategy is used to integrate the output of the sub-networks, further improving the accuracy of prediction model. Finally, merits of the proposed DMNN are confirmed on a benchmark and real industrial data of a municipal solid waste incineration (MSWI) process. The problem that the NOx emission of MSWI process is difficult to be accurately predicted due to the sensor limitation is effectively solved.