Dioxin Emission Soft Sensing for Real-Time Incineration Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for controlling dioxin emissions in municipal solid waste incineration processes face challenges due to the difficulty in building accurate emission concentration models, high costs of detection equipment, and the inability to provide real-time feedback for optimized operation, especially with small sample data and high-dimensional characteristics.
Innovation Solution
A multi-source latent feature selective ensemble (SEN) soft measurement method using principal component analysis (PCA) for latent feature extraction, mutual information for feature evaluation, and an adaptive least squares-support vector machine (LS-SVM) with hyperparameter selection for building sub-models to estimate dioxin emission concentrations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If offline direct detection based on monthly or seasonal cycles is used, then detection accuracy is improved, but real-time feedback capability deteriorates
Solution Approach 1:
The patent introduces indicator substances (HCl, SO2, NOx, CO, O2, and temperature) as intermediaries that correlate with dioxin emission concentrations. These indicators can be detected in real-time and serve as proxies for dioxin levels, enabling timely feedback without direct dioxin measurement
Solution Approach 2:
The patent creates a soft measurement model that copies the relationship between indicator substances and dioxin concentrations. By modeling this relationship using machine learning algorithms (SVM, random forest, neural networks), the system replicates dioxin concentration estimation using readily available sensor data from the incineration process
2Manufacturing precision
If advanced flue gas treatment devices and direct detection methods are used, then dioxin emission control precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical detection equipment with data-driven soft measurement models. Instead of using sophisticated sensors and analysis instruments to directly measure dioxins, the system uses standard process sensors combined with machine learning algorithms to estimate concentrations
Solution Approach 2:
The patent shifts from measuring dioxin concentration directly to measuring correlated parameters (temperature, gas composition, flow rates) that are easier and cheaper to detect. By changing the measurement parameters from toxic substance concentration to physical-chemical process variables, the system achieves control without complex equipment
3Measurement precision
If multiple machine learning algorithms are compared for small sample data, then modeling accuracy is improved, but computational time and complexity increase
Solution Approach 1:
The patent applies a selective approach to algorithm comparison by focusing on three representative algorithms (SVM, random forest, neural networks) that cover different modeling paradigms. Rather than exhaustively testing all possible algorithms, the study selects a manageable subset that provides sufficient comparison while controlling computational resources
Solution Approach 2:
The patent segments the hyperparameter optimization process by implementing automated grid search and cross-validation procedures for each algorithm. This systematic segmentation of the optimization task into standardized steps reduces manual intervention time and enables efficient comparison across different algorithms
Data Source
AI summary
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.