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首页> 外文会议>Challenges of Power Engineering and Environment >Experimental and Simulation Investigations of Combustion Optimization on a 125 MW Tangentially Anthracite-fired Boiler
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Experimental and Simulation Investigations of Combustion Optimization on a 125 MW Tangentially Anthracite-fired Boiler

机译：125 MW切向无烟煤锅炉燃烧优化的实验与仿真研究

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摘要

Experimental and simulation investigations of combustion optimization were performed on a 125 MW tangentially, anthracite- fired boiler. The experiments including forty cases by burning three pure and five blended coals were firstly carried out under the different O2 concentrations in flue gas and distribution modes for secondary air. The results show that a reverse V-shaped operating for the secondary air mode and O2 concentration of 3%-4% is favorable to decrease the unburned carbon content in the fly ash and obtain the high boiler efficiency. After that, the BP artificial neural network technology was adopted to develop a simple network model between coal quality, boiler operating conditions and the boiler efficiency. The most important and representative parameters such as Var and Qnet,ar from approximate analysis, distribution mode for the secondary air and O2 concentration in flue gas at the furnace exit, were chosen as input parameters. The distribution mode for secondary air was denoted by the ratio of the damper opening values between upper and lower secondary air. Then a preprocessor method of approximate optimizing, based on the above model, was proposed to optimize the combustion performance of the boiler and establish an information library of combustion optimization for the boiler. Finally, validation tests were conducted to check the applications of the optimum information library.
机译：在切向无烟煤的125 MW锅炉上进行了燃烧优化的实验和模拟研究。首先在烟气中O2浓度不同和二次空气分配方式不同的情况下，进行了40种燃烧三种纯煤和五种掺混煤的实验。结果表明，二次空气模式下反向V形运行和O2浓度为3％-4％有利于降低飞灰中未燃烧的碳含量，并获得高锅炉效率。之后，采用BP人工神经网络技术建立了煤质，锅炉运行条件和锅炉效率之间的简单网络模型。通过近似分析，炉膛出口处二次空气的分布模式和烟气中O2浓度等最重要和最具代表性的参数作为输入参数。二次空气的分配方式由上下二次空气的风门开度值之比表示。然后在上述模型的基础上，提出了一种近似优化的预处理器方法，以优化锅炉的燃烧性能，建立锅炉燃烧优化信息库。最后，进行验证测试以检查最佳信息库的应用。

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