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August 24, 2025ProcessesOpen Access

A Comprehensive Study of Machine Learning for Waste-to-Energy Process Modeling and Optimization

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Authors

JZJianzhao ZhouJLJingyuan LiuJRJingzheng Ren

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Overview

Comprehensive analysis demonstrates that machine learning improves carbon emission factor and optimization speed in waste-to-energy systems.

Key Points

  • Machine learning models reduced carbon emission factor to approximately 1.1 kg CO2-eq/kg MW, showcasing significant carbon reduction potential.
  • The most effective approach combined Gaussian process regression with particle swarm optimization, achieving optimizations in under one second.
  • Detailed simulations were performed while integrating life cycle assessment and diverse operational conditions to create a robust dataset for training.
  • The study indicates that machine learning techniques can surpass traditional methods, offering faster and more stable optimization capabilities in waste conversion processes.

Cite This Study

Zhou et al. (2025) studied this question.

synapsesocial.com/papers/68af79a47567bf4f94ff16fbhttps://doi.org/10.3390/pr13092691
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