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.

公开(公告)号：US12271665B2

公开(公告)日：2025-04-08

申请号：US18796867

申请日：2024-08-07

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Xi Meng ,  Qizheng Hou ,  Junfei Qiao

IPC: G06F30/25 ,  F23G5/50

Abstract: Provided is an intelligent operational optimization method in the municipal solid waste incineration process, which belongs to both the field of municipal solid waste treatment and the field of intelligent optimization. The method includes: constructing a sample data set by collecting historical data in the municipal solid waste incineration process; with optimization objectives of nitrogen oxide emissions and combustion efficiency, establishing an SORBF neural network-based indicator model to characterize the mapping relationship between operational variables and optimization objectives in the municipal solid waste incineration process; and setting the established indicator model as evaluation functions of a multi-objective optimization algorithm, and obtaining optimal setting values of the operational variables by the multi-objective particle swarm optimization algorithm, so as to improve combustion efficiency while reducing concentrations of nitrogen oxide emissions.