Zhu, J.; Feng, C.; Zhao, Z.; Yang, H.; Liu, Y. Application of Deep Learning Algorithm in Optimization Control of Electrostatic Precipitator in Coal-Fired Power Plants. Processes2024, 12, 477.
Zhu, J.; Feng, C.; Zhao, Z.; Yang, H.; Liu, Y. Application of Deep Learning Algorithm in Optimization Control of Electrostatic Precipitator in Coal-Fired Power Plants. Processes 2024, 12, 477.
Zhu, J.; Feng, C.; Zhao, Z.; Yang, H.; Liu, Y. Application of Deep Learning Algorithm in Optimization Control of Electrostatic Precipitator in Coal-Fired Power Plants. Processes2024, 12, 477.
Zhu, J.; Feng, C.; Zhao, Z.; Yang, H.; Liu, Y. Application of Deep Learning Algorithm in Optimization Control of Electrostatic Precipitator in Coal-Fired Power Plants. Processes 2024, 12, 477.
Abstract
The new energy structure needs to balance energy security and dual carbon goals, which has brought major challenges to coal-fired power plants, the pollution reduction and carbon emissions reduction of coal-fired power plants will be a key task in the future. This article proposes an optimization operation of electrostatic precipitators(ESP), based on the working mechanism and historical data of ESP, voltage-current characteristic model and outlet dust concentration prediction model are constructed by deep learning algorithm, Particle Swarm Optimization(PSO) is used to achieve the optimal energy consumption while ensuring stable outlet dust concentration. By training with historical data collected on site, accurate prediction of secondary current and outlet dust concentration of ESP has been achieved, the Mean Absolute Percentage Error(MAPE) of secondary current is less than 1.5%, and the MAPE of outlet dust concentration of ESP on the test set is less than 5.2%. Finally, the optimization experiment is carried out in a 330MW coal-fired power plants, the results showed that the fluctuation of the outlet dust concentration is more stable, and the energy saving is about 43% after optimization, according to the annual operation of 300 days, the annual average carbon reduction is approximately 2621.34 tons. This method is effective and can be applied widely.
Keywords
pollution reduction and carbon emissions reduction; deep learning; energy saving; concentration prediction; particle swarm optimization
Subject
Environmental and Earth Sciences, Pollution
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
