Abstract
Optimizing the operation of coal-fired power plants to reduce nitrogen oxide (NO x ) emissions requires accurate modeling of the NO x emission process. The careful selection of input parameters not only forms the basis of accurate modeling, but can also be used to reduce the complexity of the model. The present study employs the least squares support vector machine-supervised learning method to model NO x emissions based on historical real time data obtained from a 1000-MW once-through boiler. The initial input parameters are determined by expert knowledge and operational experience, while the final input parameters are obtained by sensitivity analysis, where the variation in model accuracy for a given set of data is analyzed as one or several input parameters are successively omitted from the calculations, while retaining all other parameters. Here, model accuracy is evaluated according to the mean relative error (MRE). This process reduces the parameters required for NO x emission modeling from an initial number of 33 to 7, while the corresponding MRE is reduced from 3.09% to 2.23%. Moreover, a correlation of 0.9566 between predicted and measured values was obtained by applying the model with just these seven input parameters to a validation dataset. As such, the proposed method for selecting input parameters serves as a reference for related studies.
概要
目的
采用最小二乘支持向量机建立煤粉锅炉NOx 排放 模型,即建立输入参数与NOx 之间的关系。合理 选择输入参数不仅会降低模型的复杂度,而且会 提高模型的精度。为此,本文探讨各输入参数对 模型的影响,并最终保留合适数量的输入参数建 立NOx 排放模型。
创新点
1. 采用最小二乘支持向量机建立NOx 排放模型; 2. 通过敏感性分析确定模型的最终输入参数。
方法
1. 根据专家知识及运行经验确定NOx 排放模型的 初始输入参数(图2);2. 根据锅炉的运行历史数 据,采用最小二乘支持向量机建立NOx排放模型; 3. 采用敏感性分析方法确定NOx排放模型的最终 输入参数(图11),并用其进行建模以验证模型 的有效性。
结论
1. 采用最小二乘支持向量机建立的1000 MW 超 超临界前后墙对冲锅炉NOx 排放模型,可靠性和 精度较高;2. 经过敏感性分析,NOx 排放模型的 输入参数由初始的33 个降为7 个,模型的复杂度 降低且精度提高。
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Project supported by the Science and Technology Plan Project of Zhejiang Province (No. 2014C33018), China
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Wang, Yl., Ma, Zy., You, Hh. et al. Development of a NO x emission model with seven optimized input parameters for a coal-fired boiler. J. Zhejiang Univ. Sci. A 19, 315–328 (2018). https://doi.org/10.1631/jzus.A1600787
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DOI: https://doi.org/10.1631/jzus.A1600787
Keywords
- Nitrogen oxide (NO x )
- Coal-fired boiler
- Least squares support vector machine
- Input parameters
- Sensitivity analysis