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Integrating multi-objective optimization with computational fluid dynamics to optimize boiler combustion process of a coal fired power plant

机译:将多目标优化与计算流体动力学相集成,以优化燃煤电厂的锅炉燃烧过程

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摘要

The dominant role of electricity generation and environment consideration have placed strong requirements on coal fired power plants, requiring them to improve boiler combustion efficiency and decrease carbon emission. Although neural network based optimization strategies are often applied to improve the coal fired power plant boiler efficiency, they are limited by some combustion related problems such as slagging. Slagging can seriously influence heat transfer rate and decrease the boiler efficiency. In addition, it is difficult to measure slag build-up. The lack of measurement for slagging can restrict conventional neural network based coal fired boiler optimization, because no data can be used to train the neural network. This paper proposes a novel method of integrating non-dominated sorting genetic algorithm (NSGA Ⅱ) based multi-objective optimization with computational fluid dynamics (CFD) to decrease or even avoid slagging inside a coal fired boiler furnace and improve boiler combustion efficiency. Compared with conventional neural network based boiler optimization methods, the method developed in the work can control and optimize the fields of flue gas properties such as temperature field inside a boiler by adjusting the temperature and velocity of primary and secondary air in coal fired power plant boiler control systems. The temperature in the vicinity of water wall tubes of a boiler can be maintained within the ash melting temperature limit. The incoming ash particles cannot melt and bond to surface of heat transfer equipment of a boiler. So the trend of slagging inside furnace is controlled. Furthermore, the optimized boiler combustion can keep higher heat transfer efficiency than that of the non-optimized boiler combustion. The software is developed to realize the proposed method and obtain the encouraging results through combining ANSYS 14.5, ANSYS Fluent 14.5 and CORBA C++.
机译:发电和环境因素的主导作用对燃煤电厂提出了强烈要求,要求它们提高锅炉的燃烧效率并减少碳排放。尽管基于神经网络的优化策略通常用于提高燃煤电厂锅炉的效率,但它们受到一些与燃烧相关的问题(例如结渣)的限制。结渣会严重影响传热速率并降低锅炉效率。另外,很难测量炉渣的堆积。由于没有数据可用于训练神经网络,因此缺乏对结渣的测量会限制基于传统神经网络的燃煤锅炉优化。提出了一种基于非支配排序遗传算法(NSGAⅡ)的多目标优化与计算流体力学(CFD)相结合的新方法,以减少甚至避免燃煤锅炉炉内结渣,提高锅炉燃烧效率。与传统的基于神经网络的锅炉优化方法相比,本工作开发的方法可以通过调节燃煤电厂锅炉的一次和二次空气的温度和速度,来控制和优化烟道气特性场,例如锅炉内部的温度场。控制系统。锅炉水冷壁附近的温度可以保持在灰烬熔化温度的极限内。进入的灰烬颗粒不能熔化并结合到锅炉的传热设备表面。因此,可以控制炉内结渣的趋势。此外,优化的锅炉燃烧可以保持比非优化的锅炉燃烧更高的传热效率。通过结合ANSYS 14.5,ANSYS Fluent 14.5和CORBA C ++来开发软件,以实现所提出的方法并获得令人鼓舞的结果。

著录项

  • 来源
    《Applied Energy》 |2014年第1期|658-669|共12页
  • 作者

    Xingrang Liu; R.C. Bansal;

  • 作者单位

    School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia,Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa;

    School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia,Department of Electrical, Electronic and Computer Engineering, University of Pretoria, Pretoria 002, South Africa;

  • 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
  • 关键词

    Multi-objective optimization; Carbon emission; Coal fired power plant combustion optimization; Slagging and fouling; ANSYS Fluent; CORBA;

    机译:多目标优化;碳排放;燃煤电厂燃烧优化;结渣和结垢;ANSYS流利;科巴;

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