Automated Emission Source Identification via Tree Classification
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Solution Overview
Problem
Existing source apportionment methods for air pollution rely heavily on artificial identification of factor profiles, which is subjective, time-consuming, and prone to deviations, limiting their effectiveness and real-time applicability.
Innovation Solution
A method integrating measured source profiles and factor profiles to generate labeled and unlabeled data sets, constructing and optimizing a tree classification model, and coupling it with a pseudo-labeling algorithm to automatically identify emission sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If artificial identification of factor profiles is used, then expert knowledge can be applied to identify emission sources, but the process is subjective, time-consuming, and prone to deviations
Solution Approach 1:
The patent replaces the manual mechanical process of expert identification with an automated computational system. A tree classification model is constructed to automatically identify factor profiles by comparing measured source profiles with reference profiles, eliminating the need for expert manual analysis and achieving both high accuracy and real-time processing capability
Solution Approach 2:
The system enables self-service by allowing the classification model to automatically perform identification without requiring expert intervention. The model uses predefined classification rules and algorithms to independently analyze factor profiles and determine emission sources, making the process objective and reproducible
2Reliability
If artificial identification of factor profiles is used, then expert experience can guide the identification process, but the results vary between different people causing deviations
Solution Approach 1:
The patent transforms the identification process from a subjective parameter-based approach to an objective parameter-based approach. By converting expert knowledge into quantifiable classification parameters and rules within the tree model, the system ensures consistent results regardless of who operates it, while maintaining the essential expertise through structured parameter definitions
Solution Approach 2:
The patent segments the complex identification process into distinct classification steps within the tree model structure. Each node in the tree represents a specific classification criterion, breaking down the overall identification task into manageable, objective sub-tasks that can be systematically evaluated without requiring holistic expert judgment at each step
3Ease of operation
If artificial identification is used, then parameter adjustment can be performed manually, but users spend a lot of time adjusting parameters
Solution Approach 1:
The patent performs preliminary action by pre-configuring the tree classification model with all necessary classification rules, parameters, and reference profiles before actual identification begins. This upfront preparation eliminates the need for manual parameter adjustment during operation, as the model is already optimized and ready to process data immediately
Solution Approach 2:
The patent replaces the manual parameter adjustment mechanism with an automated model configuration system. The tree classification model uses predetermined algorithms and structures that automatically determine optimal parameters based on the data characteristics, eliminating the need for users to manually tweak parameters while maintaining ease of use through simple model selection
4Adaptability or versatility
If artificial identification is used, then in-depth understanding of emission source characteristics is required, but this limits popularization and application of the receptor model
Solution Approach 1:
The patent replaces the need for expert knowledge with an automated classification system. The tree model encapsulates emission source characteristics in structured rules and reference profiles, allowing users without specialized knowledge to apply the model effectively. The system handles the complexity internally while presenting a user-friendly interface
Solution Approach 2:
The patent creates a universal identification framework that can be applied across different emission sources and scenarios. The tree classification model uses standardized procedures and reference profiles that can accommodate various types of sources, making the receptor model broadly applicable without requiring users to develop source-specific expertise for each application
Data Source
AI summary
A method for automatically identifying emission sources in a source apportionment process of pollutants is provided, which relates to the field of air pollution prevention and control. The method includes: integrating measured source spectrum data and factor spectrum data to generate a labeled data set and an unlabeled data set, respectively; preprocessing the labeled data set to generate a continuous labeled data set; constructing a tree classification model based on the continuous labeled data set; optimizing the tree classification model to determine the optimized tree classification model; coupling the optimized tree classification model and a pseudo-labeling algorithm to generate an integrated model based on the unlabeled data set to automatically identify factor profiles in the unlabeled data set; and determining types of the emission sources based on the factor profiles. The factor profiles can be automatically identified, so that types of emission sources can be quickly determined.