LiDAR measurement based on model predictive control for boiler combustion optimization

doi:10.11949/0438-1157.20231345

Abstract

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

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

CLC Number:

TK 3

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.

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.

Figures/Tables 17

References 30

1	闫庆友, 陶杰. 中国生物质发电产业效率评价[J]. 运筹与管理, 2015, 24(1): 173-178, 208.
	Yan Q Y, Tao J. Evaluation of China biomass power generation lndustry efficiency[J]. Operations Research and Management Science, 2015, 24(1): 173-178, 208.
2	向柏祥. 100 MWe再热生物质循环流化床锅炉开发[D]. 北京:清华大学, 2015.
	Xiang B X. Development of a 100 MWe reheat biomass-fired circulating fluidized bed boiler[D]. Beijing: Tsinghua University, 2015.
3	王晓炜, 张志耀, 许晓峰, 等. 生物质循环流化床锅炉燃烧波动分析与对策[J]. 工业锅炉, 2023(4): 21-23.
	Wang X W, Zhang Z Y, Xu X F, et al. Analysis and countermeasures to combustion fluctuation of biomass-fired CFB boiler[J]. Industrial Boilers, 2023(4): 21-23.
4	樊博璇, 陈桂明, 常亮, 等. 激光雷达技术在军事领域应用现状及发展趋势[J]. 航天制造技术, 2021(3): 66-72.
	Fan B X, Chen G M, Chang L, et al. Application status and development trend of lidar technology in military field[J]. Aerospace Manufacturing Technology, 2021(3): 66-72.
5	蒙庆华, 林辉, 王革, 等. 激光雷达工作原理及发展现状[J]. 现代制造技术与装备, 2019(10): 155-157.
	Meng Q H, Lin H, Wang G, et al. Laser radar operating principle and development status[J]. Modern Manufacturing Technology and Equipment, 2019(10): 155-157.
6	叶飞, 倪云兵, 贺亦斌. 激光雷达料位计在水泥厂料仓监测中的应用[J]. 水泥工程, 2023(3): 53-55.
	Ye F, Ni Y B, He Y B. Application of lidar level meter in cement silo monitoring[J]. Cement Engineering, 2023(3): 53-55.
7	张开萍. 生物质循环流化床燃烧系统建模及机组蓄能研究[D]. 北京:华北电力大学, 2023.
	Zhang K P. Modeling of biomass circulating fluidized bed combustion system and research on unit energy storage[D]. Beijing: North China Electric Power University, 2023.
8	张开萍, 高明明, 龙江, 等. 生物质直燃式循环流化床锅炉燃烧系统建模及预测研究[J]. 动力工程学报, 2023, 43(4): 452-460.
	Zhang K P, Gao M M, Long J, et al. Modeling and prediction of biomass direct fired circulating fluidized bed boiler combustion system[J]. Journal of Chinese Society of Power Engineering, 2023, 43(4): 452-460.
9	王乐钟. 基于大数据的锅炉状态参数预报研究[D]. 北京: 华北电力大学, 2023.
	Wang L Z. Research on prediction of boiler state parameter based on big data[D]. Beijing: North China Electric Power University, 2023.
10	高明明, 于浩洋, 吕俊复, 等. 循环流化床氮氧化物排放预测模型及优化控制研究[J]. 洁净煤技术, 2020, 26(3): 46-51.
	Gao M M, Yu H Y, Lv J F, et al. Study on prediction model and optimal control of nitrogen oxides emission of circulating fluidized bed[J]. Clean Coal Technology, 2020, 26(3): 46-51.
11	Li K, Thompson S, Peng J X. Modelling and prediction of NO _x emission in a coal-fired power generation plant[J]. Control Engineering Practice, 2004, 12(6): 707-723.
12	许霖杰. 超/超临界循环流化床锅炉数值模拟研究[D]. 杭州: 浙江大学, 2018.
	Xu L J. Numerical model development of an ultra/supercritical circulating fluidized bed boiler[D]. Hangzhou: Zhejiang University, 2018.
13	熊彬. 300 MW循环流化床锅炉主蒸汽压力和床温控制算法的研究[D]. 长沙: 长沙理工大学, 2013.
	Xiong B. Research on control algorithm of main steam pressure and bed temperature of 300 MW circulating fluidized bed boiler[D]. Changsha: Changsha University of Science & Technology, 2013.
14	尤海辉. 循环流化床垃圾焚烧炉燃烧优化试验研究[D]. 杭州: 浙江大学, 2021.
	You H H. Experimental study on combustion optimization of circulating fluidized bed waste incinerator[D]. Hangzhou: Zhejiang University, 2021.
15	Zhang Z J, Zhang Y C, Shen W, et al. Multi-objective optimization of coal mill outlet temperature control using MPC[C]//2023 9th International Symposium on System Security, Safety, and Reliability (ISSSR). IEEE, 2023: 314-320.
16	Afram A, Janabi-Sharifi F. Supervisory model predictive controller (MPC) for residential HVAC systems: implementation and experimentation on archetype sustainable house in Toronto[J]. Energy and Buildings, 2017, 154: 268-282.
17	Balram P, Tuan L A, Carlson O. Comparative study of MPC based coordinated voltage control in LV distribution systems with photovoltaics and battery storage[J]. International Journal of Electrical Power & Energy Systems, 2018, 95: 227-238.
18	Yu X B, Yu X R, Lu Y Q, et al. Differential evolution mutation operators for constrained multi-objective optimization[J]. Applied Soft Computing, 2018, 67: 452-466.
19	陈炳基. 生物质循环流化床锅炉燃烧过程建模与优化控制[D]. 长沙: 长沙理工大学, 2018.
	Chen B J. Modeling and optimal control of combustion process of biomass circulating fluidized bed boiler[D]. Changsha: Changsha University of Science & Technology, 2018.
20	马莉, 孙万麟, 刘红. 基于模糊PID的火电机组主蒸汽温度控制系统设计[J]. 工业控制计算机, 2018, 31(9): 74-76.
	Ma L, Sun W L, Liu H. Main steam temperature control system of thermal power unit based on fuzzy PID[J]. Industrial Control Computer, 2018, 31(9): 74-76.
21	傅彩芬, 谭文. 循环流化床锅炉燃烧系统的控制研究[J]. 热能动力工程, 2016, 31(2): 66-73.
	Fu C F, Tan W. Study on control of combustion system of circulating fluidized bed boiler[J]. Journal of Engineering for Thermal Energy and Power, 2016, 31(2): 66-73.
22	刘博洋, 梁洪峰. 新型固态化激光雷达技术综述[J]. 科技创新导报, 2016, 13(13): 64-67.
	Liu B Y, Liang H F. Overview of new solid-state lidar technology[J]. Science and Technology Innovation Herald, 2016, 13(13): 64-67.
23	姜明男. 基于支持向量机和图像处理的大型固废焚烧炉控制参数预测[D]. 杭州: 浙江大学, 2022.
	Jiang M N. Control parameter prediction of large solid waste incinerator based on support vector machine and image processing[D]. Hangzhou: Zhejiang University, 2022.
24	吴康洛. 基于MIC变量选择的燃煤电站SCR脱硝系统动态建模方法研究[D]. 广州: 华南理工大学, 2022.
	Wu K L. Research on dynamic modeling method of SCR denitration system in coal-fired power station based on MIC variable selection[D]. Guangzhou: South China University of Technology, 2022.
25	Tang Z H, Wu X Y, Cao S X. Adaptive nonlinear model predictive control of the combustion efficiency under the NO _x emissions and load constraints[J]. Energies, 2019, 12(9): 1738.
26	Liukkonen M, Hälikkä E, Hiltunen T, et al. Dynamic soft sensors for NO _x emissions in a circulating fluidized bed boiler[J]. Applied Energy, 2012, 97: 483-490.
27	Kim B S, Kim T Y, Park T C, et al. Comparative study of estimation methods of NO _x emission with selection of input parameters for a coal-fired boiler[J]. Korean Journal of Chemical Engineering, 2018, 35(9): 1779-1790.
28	Leskens M. Improved economic performance of municipal solid waste combustion plants by model based combustion control[D]. Delft: Delft University of Technology, 2013.
29	李建善. 基于S7-400 PLC生活垃圾焚烧机械炉排炉控制系统设计与应用[D]. 杭州: 浙江大学, 2020.
	Li J S. Design and application of grate furnace control system for domestic waste incineration machinery based on S7-400 PLC[D]. Hangzhou: Zhejiang University, 2020.
30	杨更更. Modbus软件开发实战指南[M]. 北京: 清华大学出版社, 2017.
	Yang G G. The Practical Developing Guide for Modbus[M]. Beijing: Tsinghua University Press, 2017.

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

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

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

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

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	%