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Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Ā Building a Survival Tree
Constructing a survival tree begins...

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Related Experiment Video

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AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration.

Heng Xia1, Jian Tang1, Loai Aljerf2

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; Beijing Laboratory of Smart Environmental Protection, Beijing 100124, China.

Journal of Hazardous Materials
|September 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel whole-process machine learning model for measuring dioxin (DXN) emissions from municipal solid waste incineration (MSWI). The model accurately predicts DXN generation, adsorption, and emission, enabling better pollution control.

Keywords:
AI-based modelGeneration, adsorption and emission phasesMunicipal solid waste incineration (MSWI)Pollution emission modelingTree-based deep/broad learningWhole process dioxins (DXN) concentration

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Area of Science:

  • Environmental Science
  • Chemical Engineering
  • Artificial Intelligence

Background:

  • Municipal solid waste incineration (MSWI) is a significant source of dioxin (DXN) emissions.
  • Current DXN emission control strategies are hindered by the lack of real-time, whole-process online measurement capabilities.
  • Existing models primarily focus on stack emissions, neglecting DXN generation and absorption dynamics.

Purpose of the Study:

  • To develop a comprehensive, data-driven model for online measurement of DXN concentrations throughout the entire MSWI process.
  • To establish a novel framework for DXN modeling that integrates generation, adsorption, and emission phases.
  • To support optimal pollution reduction control through a mechanistic understanding of DXN behavior.

Main Methods:

  • Application of advanced tree-based machine learning algorithms, including deep and broad learning, adaptive deep forest regression, and fuzzy forest regression.
  • Analysis of diverse data characteristics (high-dimensional small samples, low-dimensional ultra-small size samples, medium-dimensional small samples) across different DXN phases.
  • Utilizing a deep understanding of the DXN mechanism to inform data characteristic determination and model construction.

Main Results:

  • A robust, whole-process tree-based model for DXN was developed and validated using nearly one year of authentic data from an MSWI plant in Beijing.
  • The model demonstrated effectiveness in handling various data complexities inherent in DXN generation, adsorption, and emission phases.
  • The proposed framework provides a novel approach to DXN modeling, moving beyond stack-focused analysis.

Conclusions:

  • The developed whole-process model offers a significant advancement for online DXN monitoring and control in MSWI.
  • This approach facilitates a deeper exploration of DXN mechanism characterization.
  • The framework provides essential support for optimizing pollution reduction strategies in waste incineration.