Combining neural network and genetic algorithms to optimize low NO_x pulverized coal combustion

Zhou Hao; Cen Kefa; Mao Jianbo

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Combining neural network and genetic algorithms to optimize low NO_x pulverized coal combustion

机译：结合神经网络和遗传算法优化低NO_x煤粉燃烧

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摘要

The present work introduces a way of optimizing the low NO_x combustion usign the neural network and genetic algorithms for pulverized coal burned utility boiler. The NO_x emission characteristics of a 600 MW capacity boiler operated under different conditions is experimentally investigated and on the basis of experimental results, the artificial neural network is used to described its NO_x emission property to develop a neural network based model. A genetic algorithm is employed to perform a search to determine the optimum solution of the neural network model, identifying appropriate setpoints for the current operating conditions and the low NO_x emission of the pulverized coal burned boiler is achieved.

机译：本文介绍了一种利用神经网络和遗传算法优化粉煤燃烧电站锅炉低NO_x燃烧的方法。通过实验研究了在不同条件下运行的600 MW容量锅炉的NO_x排放特性，并在实验结果的基础上，利用人工神经网络描述了其NO_x排放特性，从而建立了基于神经网络的模型。采用遗传算法进行搜索，以确定神经网络模型的最佳解决方案，为当前的运行条件确定合适的设定点，从而实现粉煤燃烧锅炉的低NO_x排放。

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来源
《Fuel》 |2001年第15期|p.2163-2169|共7页
作者
Zhou Hao; Cen Kefa; Mao Jianbo;
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作者单位

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类燃料化学工业（总论）;热力工程、热机;
关键词
NO_x emission; neural network; genetic algorithms; coal combustion;

机译：NO_x排放;神经网络;遗传算法;燃煤;

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1. Multi-objective optimization of the coal combustion performance with artificial neural networks and genetic algorithms [J] . H. Zhou, K. Cen, J. Fan International journal of energy research . 2005,第6期

机译：人工神经网络和遗传算法对燃煤性能的多目标优化
2. The combustion and NO_x emission characteristics of the ultra-low volatile fuel using the novel pulverized coal self-sustained preheating combustion technology [J] . Song Wenhao, Ouyang Ziqu, Wang Minghao, Fuel . 2020,第Jul1期

机译：采用新型粉煤自持续预热燃烧技术的超低挥发性燃料的燃烧和NO_X排放特性
3. Optimization of separated overfire air to reduce NO_x emissions under combustion stability for the retrofit of a 500 MW tangentially pulverized coal boiler [J] . Jiang Yu, Ahn Seokgi, Oh Dong-Hun, Fuel . 2021,第Apra1期

机译：分离的过火空气优化，减少500 MW切向煤锅炉改造的燃烧稳定性下的NO_X排放
4. DEVELOPMENT OF NEW TYPE PULVERIZED COAL BURNER FOR LOW NO_x AND LOW UNBURNED CARBON COMBUSTION [C] . M. Kimoto, H. Makino, H. Tsuji, International symposium on coal combustion;ISCC; 19950918-21;19950918-21; Beijing(CN);Beijing(CN) . 1995

机译：低NO_x低碳燃烧新型煤粉燃烧器的研制。
5. Selecting neural network topologies: A hybrid approach combining genetic algorithms and neural networks. [D] . Taylor, Christopher M. 2005

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8. Hierarchical Neural Network Based Data Processing System for Ground- Penetrating Radar. An End of Year Report for CH/1049/6: Application of Neural Networks Coupled With Genetic Algorithms to Optimize Soil Cleanup Operations in Cold Climates [R] . Sullivan, J. M. 1997

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Combining neural network and genetic algorithms to optimize low NO_x pulverized coal combustion

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