Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks
Modeling and optimization of the NOx emission characteristics of a tangentially fired boiler with artificial neural networks
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DOI：
10.1016/j.energy.2003.08.004
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发表时间：
2004
期刊：
影响因子：
9
通讯作者：
Hao Zhou;K. Cen;Jianren Fan
中科院分区：
文献类型：
--
作者：
Hao Zhou;K. Cen;Jianren Fan
介绍了一种用人工神经网络（ANN）预测大容量煤粉锅炉氮氧化物（NOx）排放特性的方法。通过参数化现场试验研究了NOx排放和碳燃尽特性。研究了二次风流量、煤质、锅炉负荷、配风方式和喷嘴倾斜度对二次风特性的影响。在实验结果的基础上，采用人工神经网络对NOx排放特性和碳燃尽特性进行了建模。与计算流体动力学（CFD）等建模技术相比，人工神经网络方法更方便、直观，在各种工况下都能取得良好的预测效果。采用改进的遗传算法（GA），使用微GA技术进行搜索，以确定人工神经网络模型的最优解，确定当前操作条件下的最佳设定点，这可以建议操作员采取正确的行动，以减少NOx排放。
The present work introduces an approach to predict the nitrogen oxides (NOx) emission characteristics of a large capacity pulverized coal fired boiler with artificial neural networks (ANN). The NOx emission and carbon burnout characteristics were investigated through parametric field experiments. The effects of over-fire-air (OFA) flow rates, coal properties, boiler load, air distribution scheme and nozzle tilt were studied. On the basis of the experimental results, an ANN was used to model the NOx emission characteristics and the carbon burnout characteristics. Compared with the other modeling techniques, such as computational fluid dynamics (CFD) approach, the ANN approach is more convenient and direct, and can achieve good prediction effects under various operating conditions. A modified genetic algorithm (GA) using the micro-GA technique was employed to perform a search to determine the optimum solution of the ANN model, determining the optimal setpoints for the current operating conditions, which can suggest operators’ correct actions to decrease NOx emission.