电站锅炉省煤器受热面的积灰监测

doi:10.16085/j.issn.1000-6613.2018-1190

摘要/Abstract

摘要：

为了实现电站锅炉省煤器受热面的污染情况在线监测，采用支持向量机算法对省煤器的清洁吸热量进行预测，使用灰狼算法（GWO）和遗传算法对模型进行参数寻优并进行预测精确度的对比。再由清洁吸热量计算清洁因子，根据清洁因子的变化来判断省煤器的积灰状态。以某660MW机组为例，将短吹后的数据作为清洁数据样本进行训练和验证，结果表明灰狼算法比遗传算法预测精度更高，所需训练时间更短。最后利用上述模型来预测长吹前省煤器的清洁吸热量，绘制出清洁曲线图。该模型能较好表现省煤器的积灰情况，为受热面积灰在线监测提供依据。

关键词: 电站燃煤锅炉, 省煤器, 积灰, 支持向量机, 灰狼算法, 清洁因子

Abstract:

To realize online monitoring heating surface pollution of an economizer in coal-fired power plant boiler, support vector machine (SVM) algorithm was used to predict the clean heat absorption of the economizer. At the same time, the gray wolf algorithm (GWO) and genetic algorithm was used for parameters optimization, and prediction accuracy in two models was compared. According to the cleaning heat absorption, the cleaning factor was calculated. The economizer’s fouling was judged according to the change of cleaning factor. Take a 660MW unit as an example, the data after short blow was taken as clean samples for training and validation. The results showed that GWO has higher prediction accuracy than genetic algorithm (GA), and the training time of GWO is shorter. Finally, this model was used to predict the clean heat absorption of an economizer before long blowing, then the clean factor curve was drawn. The fouling in an economizer heating surface can be performed well. Thus, a basis for an economizer fouling on-line monitoring is offered．

Key words: coal-fired power plant boiler, economizer, fouling, support vector machine, grey wolf optimization algorithm, cleaning factor

中图分类号:

TK224

肖海平,陈裕辉,葛金林,王晓宁,席建林,刘利宁. 电站锅炉省煤器受热面的积灰监测[J]. 化工进展, 2019, 38(03): 1573-1578.

Haiping XIAO,Yuhui CHEN,Jinlin GE,Xiaoning WANG,Jianlin XI,Lining LIU. Online monitoring heating surface pollution of a boiler economizer in coal-fired power plant[J]. Chemical Industry and Engineering Progress, 2019, 38(03): 1573-1578.

图/表 11

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序号	实际吸热量 /kJ·kg^-1	工质流量 /t·h^-1	负荷 /MW	省煤器温度/℃		省煤器压力/MPa		给煤量 /t·h^-1	氧量 /%
序号	实际吸热量 /kJ·kg^-1	工质流量 /t·h^-1	负荷 /MW	入口工质	出口口工质	进口	出口	给煤量 /t·h^-1	氧量 /%
1	1798.8	1735	592.8	297	321.3	28.43	28.41	64.7	2.2
2	1733.7	1736	593.2	296	321.4	28.45	28.43	64.8	2.3
3	1661.4	1736	594.3	296	321.4	28.45	28.43	64.8	2.3
4	1661.3	1733	595.20	297	321.5	28.39	28.38	64.9	2.2
5	1703.6	1729	595.7	296	321.5	28.33	28.31	64.8	2.2
6	1605.2	1732	595.8	297	321.5	28.38	28.36	64.8	2.1
7	1615.8	1734	596.3	297	321.6	28.41	28.39	65.0	2.1
8	1603.7	1734	596.9	296	321.6	28.41	28.39	64.9	2.2
9	1580.5	1728	597.8	296	321.6	28.32	28.29	65.1	2.2
10	1591.0	1736	597.4	297	321.5	28.45	28.43	65.1	2.3
...
333	1856.9	1738	598.8	297	323.0	28.48	28.46	67.7	2.7
334	1864.7	1736	600.8	297	323.1	28.45	28.43	67.6	2.7
335	1877.8	1748	600.7	297	323.3	28.64	28.62	67.6	2.8
336	1832.5	1744	600.5	298	323.6	28.58	28.56	67.7	2.8

序号	实际吸热量 /kJ·kg^-1	工质流量 /t·h^-1	负荷 /MW	省煤器温度/℃		省煤器压力/MPa		给煤量 /t·h^-1	氧量 /%
序号	实际吸热量 /kJ·kg^-1	工质流量 /t·h^-1	负荷 /MW	入口工质	出口口工质	进口	出口	给煤量 /t·h^-1	氧量 /%
1	1798.8	1735	592.8	297	321.3	28.43	28.41	64.7	2.2
2	1733.7	1736	593.2	296	321.4	28.45	28.43	64.8	2.3
3	1661.4	1736	594.3	296	321.4	28.45	28.43	64.8	2.3
4	1661.3	1733	595.20	297	321.5	28.39	28.38	64.9	2.2
5	1703.6	1729	595.7	296	321.5	28.33	28.31	64.8	2.2
6	1605.2	1732	595.8	297	321.5	28.38	28.36	64.8	2.1
7	1615.8	1734	596.3	297	321.6	28.41	28.39	65.0	2.1
8	1603.7	1734	596.9	296	321.6	28.41	28.39	64.9	2.2
9	1580.5	1728	597.8	296	321.6	28.32	28.29	65.1	2.2
10	1591.0	1736	597.4	297	321.5	28.45	28.43	65.1	2.3
...
333	1856.9	1738	598.8	297	323.0	28.48	28.46	67.7	2.7
334	1864.7	1736	600.8	297	323.1	28.45	28.43	67.6	2.7
335	1877.8	1748	600.7	297	323.3	28.64	28.62	67.6	2.8
336	1832.5	1744	600.5	298	323.6	28.58	28.56	67.7	2.8

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