The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 14, 2025

Filed:

Apr. 26, 2023
Applicant:

Beijing University of Technology, Beijing, CN;

Inventors:

Jian Tang, Beijing, CN;

Heng Xia, Beijing, CN;

Canlin Cui, Beijing, CN;

Junfei Qiao, Beijing, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 30/27 (2020.01); G06F 111/10 (2020.01);
U.S. Cl.
CPC ...
G06F 30/27 (2020.01); G06F 2111/10 (2020.01);
Abstract

The invention provides a soft measurement method for dioxin emission of grate furnace MSWI process based on simplified deep forest regression of residual fitting mechanism. The highly toxic pollutant dioxin (DXN) generated in the solid waste incineration process is a key environmental index which must be subjected to control. The rapid and accurate soft measurement of the DXN emission concentration is an urgent affair for reducing the emission control of the pollutants. The method comprises the following steps: firstly, carrying out feature selection on a high-dimensional process variable by adopting mutual information and significance test; then, constructing a simplified deep forest regression (SDFR) algorithm to learn a nonlinear relationship between the selected process variable and the DXN emission concentration; and finally, designing a gradient enhancement strategy based on a residual error fitting (REF) mechanism to improve the generalization performance of a layer-by-layer learning process. The method is superior to other methods in the aspects of prediction precision and time consumption.


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