公开(公告)号：US11976817B2

公开(公告)日：2024-05-07

申请号：US17038723

申请日：2020-10-26

Applicant: Beijing University of Technology

Inventor： Junfei Qiao ,  Zihao Guo ,  Jian Tang

IPC: F23G5/00 ,  F23G5/50 ,  G01N33/00

CPC classification number: F23G5/50 ,  G01N33/0036 ,  F23G2207/10 ,  F23G2208/00

Abstract: A method for detecting a dioxin emission concentration of a municipal solid waste incineration process based on multi-level feature selection. A grate furnace-based MSWI process is divided into a plurality of sub-processes. A correlation coefficient value, a mutual information value and a comprehensive evaluation value between each of original input features of the sub-processes and the DXN emission concentration are obtained, thereby obtaining first-level features. The first-level features are selected and statistically processed by adopting a GAPLS-based feature selection algorithm and according to redundancy between different features, thereby obtaining second-level features. Third-level features are obtained according to the first-level features and statistical results of the second-level features. A PLS algorithm-based DXN detection model is established based on model prediction performance and the third-level features. The obtained PLS algorithm-based DXN detection model is applied to detect the DXN emission concentration of the MSWI process.

公开(公告)号：US12002014B2

公开(公告)日：2024-06-04

申请号：US16967408

申请日：2019-12-02

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Jian Tang ,  Junfei Qiao ,  Zihao Guo ,  Haijun He

IPC: G06Q10/30 ,  G05B13/02 ,  G05B13/04 ,  G06N20/10

CPC classification number: G06Q10/30 ,  G05B13/027 ,  G05B13/042 ,  G05B13/048 ,  G06N20/10

Abstract: Disclosed is a soft measurement method of DXN emission concentration based on multi-source latent feature selective ensemble (SEN) modeling. First, MSWI process data is divided into subsystems of different sources according to industrial processes, and principal component analysis (PCA) is used to separately extract the subsystems' latent features and conduct multi-source latent feature primary selection according to the threshold value of the principal component contribution rate preset by experience. Using mutual information (MI) to evaluate the correlation between the latent features of the primary selection and DXN, and adaptively determine the upper and lower limits and thresholds of the latent feature reselection; finally, based on the reselected latent features, a least squares-support vector machine (LS-SVM) algorithm with a hyperparameter adaptive selection mechanism is used to establish DXN emission concentration sub-models for different subsystems, and based on branch and bound (BB) and prediction error information entropy weighting algorithm to optimize the selection of sub-models and calculation weights coefficient, a SEN soft measurement model of DXN emission concentration is constructed.