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Reduction of elemental mercury in coal-fired boiler flue gas with computational intelligence approach

机译：利用计算智能方法减少燃煤锅炉烟气中的元素汞

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摘要

Mercury is an important pollutant emitted from coal-fired power plants. Elemental mercury (Hg-0) is harder to be removed than oxidized mercury (Hg2+) and particulate bound mercury (Hg-p) in the flue gas at back-end of furnace. In this study, a method based on computational intelligence was proposed to enhance Hg-0 removal efficiency. It was realized by improving the transformation efficiency of Hg-0 into Hg2+ and Hg-p and then removing them by air pollution control devices. First, relationships between Hg-0 concentrations at the stack and variables like open values of secondary air, open values of over fire air, oxygen at the exit of economizer, load, coal qualities and so on were modeled with aid of tuned PCA-support vector machine. Then, manipulated variables and regulated variables were optimized by particle swarm optimization algorithm to enhance transformation efficiency of He. A field thermal adjustment test was carried out on some 600 MW unit and the proposed method was applied to that unit and compared with ACO. Results showed that removal efficiencies were enhanced greatly in general. The increment of removal efficiency can reach up to 14.71%. Besides, optimal strategies can be found in few iterations, making it suitable for online applications. (C) 2018 Elsevier Ltd. All rights reserved.
机译：汞是燃煤电厂排放的重要污染物。与炉尾烟气中的氧化汞（Hg2 +）和颗粒结合汞（Hg-p）相比，难去除元素汞（Hg-0）。在这项研究中，提出了一种基于计算智能的方法来提高Hg-0去除效率。它是通过提高Hg-0转化为Hg2 +和Hg-p的转化效率，然后通过空气污染控制装置将其去除而实现的。首先，借助调整后的PCA支持，对烟囱中Hg-0浓度与变量之间的关系进行建模，这些变量包括二次空气的打开值，着火的空气的打开值，省煤器出口的氧气，负载，煤质等。向量机。然后，通过粒子群算法对操纵变量和调节变量进行优化，以提高He的转化效率。在约600 MW机组上进行了现场热调节测试，并将拟议的方法应用于该机组并与ACO进行了比较。结果表明，总体上去除效率大大提高。去除效率的提高可以达到14.71％。此外，可以在几次迭代中找到最佳策略，使其适合在线应用程序。 （C）2018 Elsevier Ltd.保留所有权利。

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