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

Abstract

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The combustion process of boilers under deep peak shaving is a multivariate process which has complex characteristics such as super multivariability, being nonlinear, and large delay. It is difficult to handle complex data and calculate appropriate distributed results. To this end, this study applies 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 and designing appropriate states and reward values, and the simulation results show that this method can be used to optimize the boiler combustion system. It can effectively reduce NOX emissions and improve the boiler combustion efficiency.

Keywords

• data center
• deep reinforcement learning
• deep peak shaving
• the combustion system
• A3C