通过强化学习增强可持续煤炭混合的多目标优化
Zhongfeng Li1,2, Lei Liu1, Zhenlong Zhao1
1School of Electrical Engineering, Yingkou Institute of Technology, Yingkou, Liaoning, People's Republic of China.
这项研究引入了一种优化发电厂煤炭混合的新算法,大大降低了成本和环境影响. 增强Q学习的NSGA-II (QLNSGA-II) 算法提高了燃煤发电的效率和可持续性.
科学领域:
- * 多目标优化算法
- * 能源系统中的计算智能
- * 燃烧工程和环境科学
背景情况:
- 热电厂的煤炭混合带来了复杂的经济,运营和环境挑战.
- 现有的方法往往难以平衡竞争目标,如降低成本和控制排放.
- * 混合参数的动态调整对于实时性能优化至关重要.
研究的目的:
- * 开发和验证一个新的算法,Q学习增强NSGA-II (QLNSGA-II),用于多目标的煤炭混合.
- *将Q学习的自适应政策优化与NSGA-II的精英选择进行动态参数调整.
- 建立基于物理的目标函数,考虑灰聚变热力学和污染物排放动力学.
主要方法:
- * 开发QLNSGA-II算法,将Q学习用于自适应速率调整和NSGA-II用于多目标优化.
- * 实现基于物理的目标功能,包括燃烧效率和NOx排放限制.
- 通过基准测试 (WFG,UF套件) 和工业试验在发电厂进行验证.
主要成果:
- * QLNSGA-II在基准套件上表现出优异的表现,将逆转代际距离 (IGD) 提高了12. 7%,高体积 (HV) 提高了9. 3%.
- 工业验证显示,燃料成本降低了14.7%,废渣发生率降低了41%.
- 显著的环境效益包括减少硫含量 (24.8%),增加净热率 (6.9%),以及每年大幅节省成本和排放量.
结论:
- * QLNSGA-II为复杂的多目标煤炭混合问题提供了可扩展和强大的解决方案.
- 该算法有效地平衡了燃煤发电的经济,运营和环境因素.
- 这种方法在提高能源部门的效率和可持续性方面取得了重大进展.
更多相关视频
10:36Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
Published on: November 3, 2023
08:17Coupling Carbon Capture from a Power Plant with Semi-automated Open Raceway Ponds for Microalgae Cultivation
Published on: August 14, 2020
相关概念视频
Maximum Power Flow and Line Loadability
Multi-input and Multi-variable systems
In the absence...
Distributed Loads: Problem Solving
Reinforcement Schedules
Once a behavior is learned,...
Thermal expansion and Thermal stress: Problem Solving
To solve the problem, first, identify the known and unknown quantities. The initial length (L) of the bridge is 1275 m, the coefficient of linear expansion (α) for steel is 12 x 10-6/°C, and the change in...
Maxwell-Boltzmann Distribution: Problem Solving
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
