AUTHOR=Wei-Jie Tang , Hai-Tao Wang , Ping-Ji Liu , Feng-Lei Qian 

TITLE=Combustion Optimization Under Deep Peak Shaving Based on DYNA-A3C With Dynamic Weight

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.953387

DOI=10.3389/fenrg.2022.953387

ISSN=2296-598X

ABSTRACT=The combustion process of boilers under deep peak shaving conditions is a multivariate process with big data, and has complex characteristics such as super multivariable, nonlinear and large lag. It is difficult to handle complex data and calculate appropriate distributed results. To this end, this paper applies using the A3C method based on the dynamic weight DYNA-structure to the boiler combustion system. This method trains and optimizes the boiler combustion system by establishing a data center, designing appropriate states and reward values, and the simulation results show that this method is used to optimize the boiler combustion system. It can effectively reduce NOX emissions and improve boiler combustion efficiency.