Combustion optimization study of pulverized coal boiler based on proximal policy optimization algorithm-Reference-Cited by-同舟云学术

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Combustion optimization study of pulverized coal boiler based on proximal policy optimization algorithm

Published:2024-10 Issue: Volume:254 Page:123857
ISSN:1359-4311
Container-title:Applied Thermal Engineering
language:en
Short-container-title:Applied Thermal Engineering

Author:

Wu Xuecheng,Zhang Hongnan,Chen Huafeng,Wang Shifeng,Gong Lingling

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Science and Technology Innovation 2025 Major Project of Ningbo Municipality

Publisher

Elsevier BV

Reference35 articles.

1. A systematic comparison of machine learning methods for modeling of dynamic processes applied to combustion emission rate modeling;Tuttle;Appl. Energy,2021

2. Artificial intelligence for the modeling and control of combustion processes: a review;Kalogirou;Prog. Energy Combust. Sci.,2003

3. ANN–GA approach for predictive modeling and optimization of NOx emission in a tangentially fired boiler;Ilamathi;Clean Techn. Environ. Policy,2013

4. Combustion optimization of ultra supercritical boiler based on artificial intelligence;Shi;Energy,2019

5. Deep Bidirectional Learning Machine for Predicting NOx Emissions and Boiler Efficiency from a Coal-Fired Boiler;Li;Energy Fuel,2017

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