Furnace temperature modeling based on multi-model intelligent combination algorithm

doi:10.11949/0438-1157.20190037

Abstract

Abstract:

Furnace temperature is an important parameter which can reflect boiler combustion status. However, furnace temperature is affected by many parameters and the mechanism is complicated. Therefore, it is difficult to establish accurate prediction model. To solve this problem, a multi-model intelligent combination algorithm (MICA) is proposed to construct an accurate prediction model. First, the actual production data is pre-processed by wavelet denoising algorithm, and the model input variables were selected based on classification and regression trees algorithm and mechanism analysis. Then, several furnace temperature prediction models are constructed by many data-driven algorithms. Finally, a C4.5 algorithm is applied to combine these models into a multi-model intelligent combination model. The experimental results illustrate that the proposed algorithm can construct an accurate furnace temperature prediction model through actual operating data.

Key words: data-driven, furnace temperature, wavelet, intelligent combination, algorithm, model, prediction

摘要：

炉膛温度是表征锅炉燃烧状态的重要参数，但是影响炉膛温度的参数多、机理复杂，导致难以建立准确的预测模型。针对这一问题，提出一种多模型智能组合算法(multi-model intelligent combination algorithm, MICA)实现对炉膛温度的建模预测。首先，对实际运行生产数据进行小波降噪，并结合机理分析和分类回归树（classification and regression tree，CART）算法选取预测模型输入参数。然后，通过多种数据驱动方法构建锅炉炉膛温度预测模型。最后，采用决策树C4.5算法建立多模型智能组合预测模型。基于实际生产数据的实验结果表明，所提出算法能够建立准确的炉膛温度预测模型。

关键词: 数据驱动, 炉膛温度, 小波, 智能组合, 算法, 模型, 预测

CLC Number:

TK 227

Zhenhao TANG,Baokai ZHANG,Shengxian CAO,Gong WANG,Bo ZHAO. Furnace temperature modeling based on multi-model intelligent combination algorithm[J]. CIESC Journal, 2019, 70(S2): 301-310.

唐振浩,张宝凯,曹生现,王恭,赵波. 基于多模型智能组合算法的锅炉炉膛温度建模[J]. 化工学报, 2019, 70(S2): 301-310.

Figures/Tables 13

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序号	变量名	单位	变化范围	标签
1	烟气含氧量	%	[2.431—2.892]	F₁
2	主蒸汽流量	t/h	[183.078—251.084]	F₂
3	主蒸汽压力	MPa	[12.976—13.316]	SP
4	主蒸汽温度	℃	[533.611—542.525]	ST
5	机组负荷	MW	[109.863—145.578]	P_a
6	再热器压力	MPa	[1.290—1.813]	RP
7	燃料量	t/h	[132.382—155.044]	F₃
8	送风量	t/h	[143.255—176.080]	F₄
9	给水流量	t/h	[361.375—505.756]	F₅
10	炉膛负压	Pa	[-62.732—-20.063]	FP
11	过热减温水流量	t/h	[2.205—10.462]	F₆
12	锅炉蒸发量	t/h	[373.350—512.889]	F₇
13	再热蒸汽温度	℃	[525.254—542.707]	RT
14	再热减温水流量	t/h	[3.958—7.734]	F₈
15	排烟温度	℃	[161.903—171.807]	GT
16	送风挡板开度	%	[3.399—40.828]	F₉

序号	变量名	单位	变化范围	标签
1	烟气含氧量	%	[2.431—2.892]	F₁
2	主蒸汽流量	t/h	[183.078—251.084]	F₂
3	主蒸汽压力	MPa	[12.976—13.316]	SP
4	主蒸汽温度	℃	[533.611—542.525]	ST
5	机组负荷	MW	[109.863—145.578]	P_a
6	再热器压力	MPa	[1.290—1.813]	RP
7	燃料量	t/h	[132.382—155.044]	F₃
8	送风量	t/h	[143.255—176.080]	F₄
9	给水流量	t/h	[361.375—505.756]	F₅
10	炉膛负压	Pa	[-62.732—-20.063]	FP
11	过热减温水流量	t/h	[2.205—10.462]	F₆
12	锅炉蒸发量	t/h	[373.350—512.889]	F₇
13	再热蒸汽温度	℃	[525.254—542.707]	RT
14	再热减温水流量	t/h	[3.958—7.734]	F₈
15	排烟温度	℃	[161.903—171.807]	GT
16	送风挡板开度	%	[3.399—40.828]	F₉

序号	测量值	预测结果				类别
序号	测量值	BP	RBF	MLP	LSSVM	类别
1	803.9	802.4	803.8	802.3	803.5	2
2	805.1	804.9	803.6	803.9	804.5	1
3	805.6	804.2	804.9	804.1	805.0	4
4	803.2	802.4	802.6	801.4	803.0	4
?	?	?	?	?	?	?
298	796.2	795.7	796.6	796.3	796.4	3
299	796.4	795.1	795.9	795.8	795.6	2
300	796.8	796.2	795.5	796.4	796.6	4

序号	测量值	预测结果				类别
序号	测量值	BP	RBF	MLP	LSSVM	类别
1	803.9	802.4	803.8	802.3	803.5	2
2	805.1	804.9	803.6	803.9	804.5	1
3	805.6	804.2	804.9	804.1	805.0	4
4	803.2	802.4	802.6	801.4	803.0	4
?	?	?	?	?	?	?
298	796.2	795.7	796.6	796.3	796.4	3
299	796.4	795.1	795.9	795.8	795.6	2
300	796.8	796.2	795.5	796.4	796.6	4

时间	标签	输入变量	输出变量	训练集	测试集	验证集	采用间隔	备注（温度变化范围）
2016\03\01 08:00~03\02 02:18	T₁	10	1	700	300	100	60s	757℃上升至835℃
2016\03\03 10:00~03\04 06:18	T₂	10	1	700	300	100	60s	754~805℃范围内变化
2016\03\10 07:00~03\11 01:18	T₃	10	1	700	300	100	60s	845℃下降至779℃