Soft measurement method for dioxin emission concentration in municipal solid waste incineration process

Patent metadata

Field	Value
Publication number	US-12002014-B2
Application number	US-201916967408-A
Country	US
Kind code	B2
Filing date	Dec 2, 2019
Priority date	Mar 24, 2019
Publication date	Jun 4, 2024
Grant date	Jun 4, 2024

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.

First claim

What is claimed is:

1. A soft measurement method of dioxin emission concentration in a municipal solid waste incineration (MSWI) process, comprising steps of:
using a latent feature extraction and primary selection module to divide data associated with the municipal solid waste incineration process into subsystems of different sources according to industrial processes;
using a principal component analysis (PCA) to separately extract latent features of the subsystems;
conducting a primary selection of the latent features according to a threshold value of a principal component contribution rate preset by experience;
using a latent feature evaluation and reselection module, measuring, based on mutual information (MI), a correlation between the latent features selected via the primary selection and dioxin (DXN);
adaptively determining an upper limit, a lower limit and thresholds of reselection of the latent features; and
using an adaptive selective ensemble modeling module, further comprising:
based on latent feature reselection, using a least squares-support vector machine (LS-SVM) algorithm with a hyperparameter adaptive selection mechanism to establish DXN emission concentration sub-models for different subsystems; and
adopting a strategy optimization based on a branch and bound (BB) and a prediction error information entropy weighting algorithm to select the DXN emission concentration sub-models and calculating weight coefficients to construct a DXN emission concentration selective ensemble soft measurement model, wherein the DXN emission concentration selective ensemble (SEN) soft measurement model is used to optimize the municipal solid waste incineration process to reduce emission of the DXN by the municipal solid waste incineration process.

2. The soft measurement method of dioxin emission concentration in the municipal solid waste incineration process according to claim 1 , wherein the step of using the latent feature extraction and primary selection module further comprises the steps of:
for an ith subsystem, using the PCA to extract the latent features of high-dimensional input process variables comprising normalizing input data X i to mean=0 and variance=1, and decomposing X i into:
X i =t 1 FeAll i ( p 1 FeAll i ) T +L+t m FeAll i i ( p m FeAll i i ) T +L+t M FeAll i i ( p M FeAll i ) T   (3);

wherein, t m FeAll i i and p m FeAll i i represent a score and load vector of m FeAll i th principal component (PC), T represents a transpose, and m FeAll i th represents a number of the latent features extracted for the ith subsystem, with a calculation formula as follows:
M FeAll i =rank( X i )  (4);

wherein based on the calculation formula, all the latent features extracted from the input data X i are expressed as:
T i =[t 1 FeAll i ,L, t m FeAll i i ,L, t M FeAll i i ]  (5)

Wherein, T i ∈R N×M FeAll i represents a score matrix, which is an orthogonal mapping of the input data X i in a direction of a load matrix P i , wherein P i is expressed by formula:
P i =[p 1 FeAll i ,L, p m FeAll i i ,L, p M FeAll i i ]  (6);

wherein P i ∈R M×M FeAll i ,
wherein the latent features extracted from the input data X i are further expressed as:

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Assignees

• Univ Beijing Technology

Inventors

• Tang Jian
• Qiao Junfei
• Guo Zihao
• He Haijun

Classifications

• G06Q10/30 Primary

  Administration of product recycling or disposal · CPC title

• G05B13/027

  using neural networks only · CPC title

• G05B13/042

  in which a parameter or coefficient is automatically adjusted to optimise the performance · CPC title

• G05B13/048

  using a predictor · CPC title

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title