优化炉燃烧参数基于进化教学-学习优化算法,以减少NOx排放度
Yunpeng Ma1, Shilin Liu1, Shan Gao1
1School of Information Engineering, Tianjin University of Commerce, Tianjin, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
本研究介绍了一种基于进化教学优化算法 (ETLBO),以减少炉的氧化 (NOx) 排放. ETLBO有效地优化了燃烧参数,在减少热电厂的NOx度方面表现出卓越的准确性.
科学领域:
- 环境工程 环境工程
- 燃烧科学 燃烧科学
- 人工智能的人工智能
背景情况:
- 炉的高氧化 (NOx) 排放对热电厂构成重大环境挑战.
- 优化炉燃烧参数对于减轻NOx污染至关重要.
研究的目的:
- 开发和验证用于减少炉NOx排放的先进优化算法.
- 为了提高燃烧参数优化的效率和准确性.
主要方法:
- 一个基于进化教学和学习的优化算法 (ETLBO) 被开发出来,它结合了混乱映射和遗传进化原则.
- 该ETLBO算法与20届IEEE进化计算大会对融合速度和准确性的测试函数进行了基准测试.
- 应用ETLBO来优化炉燃烧参数,包括煤供应和空气设置.
主要成果:
- 与现有算法相比,ETLBO在大多数基准测试函数上表现出更高的收精度.
- 应用ETLBO成功降低了炉中的NOx排放度.
- 该算法在优化关键燃烧参数方面被证明是有效的.
结论:
- ETLBO是一种高效和精确的优化工具,用于减少热电厂炉的NOx排放.
- 开发的算法为燃烧过程中的环境合规性和运营效率提供了有前途的解决方案.
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