融合激光雷达料位测算的锅炉燃烧优化模型预测控制

doi:10.11949/0438-1157.20231345

摘要/Abstract

摘要：

提出了一种融合激光雷达料位测算的锅炉燃烧优化控制策略。该控制策略通过激光雷达对生物质的入炉给料量进行实时在线监测，并采用最大互信息系数(MIC)方法分析其对于主汽压力、燃烧室温度、出口烟气氧量等关键参数的时延长度，结合经过特征筛选的锅炉特征参数，构成模型样本集；通过粒子群优化(PSO)算法进行参数优化，获得燃烧过程外源自回归(ARX)模型，并采用模型预测控制的方法优化锅炉燃烧。以700 t/d生物质燃烧发电锅炉为测试对象，该控制策略在主汽压力与燃烧室温度约束下实现了烟气氧量优化。实验结果与对比分析表明：(1)该预测模型具有较高的精度与泛用性，能满足预测控制的要求；(2)相较于PID与模糊控制，该控制算法拥有更高的控制性能，在仿真结果中氧量与其设定值的平均偏移能控制在±25%以内。该控制策略理论上具有较好的实践意义，可为生物质发电锅炉的智能化稳定燃烧提供解决方案。

关键词: 生物质锅炉, 激光雷达, 动态建模, 模型预测控制, 算法

Abstract:

A data-driven predictive control method with laser radar material level measurement was proposed for the combustion process of a power station boiler. This control strategy uses laser radar to monitor the amount of biomass fed into the furnace online in real time, and uses the maximum mutual information coefficient (MIC) method to analyze its effect on key parameters such as main steam pressure, combustion chamber temperature, and outlet flue gas oxygen content. Combined with the characteristic parameters of distributed control system (DCS) after screening, the model data set was constructed. Based on the model data set, an auto-regressive with extra inputs model optimized by particle swarm optimization algorithm was established. Based on the boiler key parameter model, the proposed control method attempts to minimize the flue gas oxygen content deviation under the constraints of main steam pressure and combustion chamber temperature. Taking 700 t/d biomass combustion power generation boiler as the test object, the experimental results based on the actual production data, as well as the comparative analysis demonstrate: (1) The predictive model can accurately predict the boiler key parameters and meet the demands of boiler combustion process control and optimization; (2) Compared with PID control and fuzzy control, the model predictive control algorithm shows higher control performance, and the average deviation of oxygen content from its set value in the simulation results can be controlled within ±25%. The proposed model predictive control method has good practical significance in theory, and can provide reference for the optimization and transformation of biomass boiler combustion in the future.

Key words: biomass boiler, laser radar, dynamic modeling, model-predictive control, algorithm

中图分类号:

TK 3

董益斌, 熊敬超, 王敬宇, 汪守康, 王亚飞, 黄群星. 融合激光雷达料位测算的锅炉燃烧优化模型预测控制[J]. 化工学报, 2024, 75(3): 924-935.

Yibin DONG, Jingchao XIONG, Jingyu WANG, Shoukang WANG, Yafei WANG, Qunxing HUANG. LiDAR measurement based on model predictive control for boiler combustion optimization[J]. CIESC Journal, 2024, 75(3): 924-935.

图/表 17

图1 生物质循环流化床锅炉结构示意图

Fig.1 Structure diagram of biomass circulating fluidized bed boiler

图2 生物质循环流化床锅炉智能控制逻辑

Fig.2 Intelligent control logic of biomass circulating fluidized bed

表1 激光雷达主要技术参数

Table 1 Main technical parameters of LiDAR

参数	数值	参数	数值
探测距离/m	0~40	距离分辨率/cm	1 cm
保护距离	≥40 m@70%反射率	水平场视角/(°)	270
数据采样率/kHz	45	测量精度/cm	±2
波长/nm	940	工作温度/℃	-25~60

图3 给料皮带与激光雷达

Fig.3 Feed belt and LiDAR

图4 给料准确性测试平台及测试结果

Fig.4 Radar test platform and test results

表2 实时给料测试误差

Table 2 Feeding amount detection error

编号	实际体积 V_r/cm³	测试1 V₁/cm³	测试2 V₂/cm³	测试3 V₃/cm³	平均误差/%	标准差/cm³
1	5713.00	5825.65	5798.36	5905.78	2.25	45.59
2	4438.00	4521.52	4568.32	4592.24	2.71	29.37
3	6192.00	6249.37	6298.47	6258.63	1.23	21.30
4	4952.00	5032.18	5088.69	5074.95	2.25	24.06
5	5087.00	5198.61	5154.27	5225.52	2.05	29.37

图5 出口烟气氧量预测特征数据二步筛选

Fig.5 Two-step screening of characteristic data of flue gas oxygen prediction

表3 锅炉参数终选

Table 3 Boiler parameters final selection

类别	名称	符号	单位
输入参数
状态参数	炉室差压左	PA1	MPa
	炉室差压右	PA2	MPa
	燃烧器下部压力左2	PB2	Pa
	燃烧器下部压力左1	PB1	Pa
	燃烧器下部压力右1	PB3	Pa
	燃烧器下部压力右2	PB4	Pa
	主汽流量	F	t/h
	总给水流量	FW	m³/h
可控参数	4#皮带频率	S4	Hz
	3#皮带频率	S3	Hz
	2#皮带频率	S2	Hz
	1#皮带频率	S1	Hz
	一次风机总流量	FA	m³/h
	二次风总流量左	FA1	m³/h
	二次风总流量右	FA2	m³/h
雷达反馈给料参数	4#皮带给料流量	FS4	m³/s
	3#皮带给料流量	FS3	m³/s
	2#皮带给料流量	FS2	m³/s
	1#皮带给料流量	FS1	m³/s
输出参数	燃烧室温度	T	℃
	主蒸汽压力	p	MPa
	出口烟气含氧量	O	%

图6 三个被控参数的不同时延下的MIC值

Fig.6 MIC values under different delay of key parameters

表4 各关键参数与给料速率间的纯延迟时间及对应的MIC值

Table 4 The delay time between key parameters and feed rate， and the corresponding MIC value

变量	给料响应时延/s	对应MIC值
燃烧室温度	320	0.25
主蒸汽压力	160	0.19
出口烟气氧量	120	0.50

图7 不同模型对三种参数的预测值

Fig.7 The predicted values of key parameters by different models

图8 不同模型对三种参数的预测误差

Fig.8 Prediction error of three parameters by different models

表5 不同模型对三种参数的预测误差评价指标

Table 5 The prediction evaluation indicators of three parameters by different models

模型	主蒸汽压力			燃烧室温度			出口烟气氧量
模型	RMSE/MPa	MAE/MPa	MAPE/%	RMSE/℃	MAE/℃	MAPE/%	RMSE/%	MAE/%	MAPE/%
PSO-ARX	0.01	0.03	0.13	0.81	1.07	0.09	0.65	0.88	3.70
MLP	0.17	0.13	1.42	14.58	11.63	1.38	0.52	0.42	2.35
PLS	0.11	0.09	0.97	11.04	9.20	1.08	1.18	0.89	4.92
LSSVM	0.06	0.033	0.28	4.34	1.70	0.20	1.07	0.49	2.78
ARX(NL)	0.09	0.06	0.65	14.24	10.88	1.29	2.74	2.07	5.28

图9 预测控制器结构示意图

Fig.9 Prediction controller structure diagram

图10 控制仿真结果

Fig.10 The results obtained with the model predictive control

图11 给料测试平台

Fig.11 Feeding test site

图12 模拟给料与实际给料对比

Fig.12 Comparison between simulated feed and actual feed

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