循环流化床锅炉燃烧过程预测控制与经济性能优化

doi:10.11949/j.issn.0438-1157.20151939

化工学报 ›› 2016, Vol. 67 ›› Issue (3): 695-700.DOI: 10.11949/j.issn.0438-1157.20151939

循环流化床锅炉燃烧过程预测控制与经济性能优化

谢磊¹, 毛国明², 金晓明¹, 苏宏业¹

1. 浙江大学工业控制技术国家重点实验室, 智能系统与控制研究所, 浙江杭州 310027;
2. 浙江华能玉环电厂, 浙江玉环 317604

收稿日期:2015-12-21 修回日期:2016-01-05 出版日期:2016-03-05 发布日期:2016-01-12
通讯作者: 谢磊
基金资助:
国家自然科学基金项目(61134007,61374121)。

Predictive control and economic performance optimization of CFBB combustion process

XIE Lei¹, MAO Guoming², JIN Xiaoming¹, SU Hongye¹

1. State Key Laboratory of Industrial Control Technology, Institute of Cyber-System and Control, Zhejiang University, Hangzhou 310027, Zhejiang, China;
2. Zhejiang Huaneng Yuhuan Power Plant, Yuhuan 317604, Zhejiang, China

Received:2015-12-21 Revised:2016-01-05 Online:2016-03-05 Published:2016-01-12
Contact: 67
Supported by:
supported by the National Natural Science Foundation of China (61134007, 61374121).

摘要/Abstract

摘要：

针对循环流化床锅炉燃烧过程多变量耦合、时滞、非线性等复杂过程特性和经济运行要求,提出了一种结合经济性能优化与多变量控制的双层预测控制策略,可在实现主蒸汽压力、料床温度和烟气含氧量约束控制的基础上,优化燃烧过程的经济性能。工业应用实例表明,该控制策略不仅能有效提高燃烧过程主要工艺参数的平稳性,而且能降低单位蒸汽的燃煤消耗量,具有显著的经济效益。

关键词: 循环流化床锅炉, 预测控制, 过程控制, 优化, 煤燃烧

Abstract:

For the combustion process of industrial circulating fluidized bed boiler (CFBB) with nonlinearity, time-variation, strong delay and multivariable coupling, considering how to satisfy the economic target with decreasing power consumption, an two level multivariable predictive control (MPC) method was proposed. The method based on the constrained control of main steam pressure, bed temperature and flue gas oxygen content is constructed to optimize the economic target. The industrial application result demonstrates that the proposed method can decrease the coal consumption of unit steam while assuring the steady of the main process parameters of the boiler combustion and bring high economical profit.

Key words: circulating fluidized bed boiler, predictive control, process control, optimization, coal combustion

中图分类号:

TP273

谢磊, 毛国明, 金晓明, 苏宏业. 循环流化床锅炉燃烧过程预测控制与经济性能优化[J]. 化工学报, 2016, 67(3): 695-700.

XIE Lei, MAO Guoming, JIN Xiaoming, SU Hongye. Predictive control and economic performance optimization of CFBB combustion process[J]. CIESC Journal, 2016, 67(3): 695-700.

参考文献 20

[1]	?ARKO ?, VOLKER S, MICHAEL G. Intelligent control of complex combustion processes[J]. Mechanical Engineering, 2003, 1(10): 1393-1406.
[2]	VLADIMÍR H, JI?Í F. Application of model predictive control to advanced combustion control[J]. Control Engineering Practice, 2005, 13: 671-680. DOI: 10.1016/j.conengprac.2004.01.005.
[3]	PETTERSSON A, ZEVENHOVEN M, STEENARI B-M, et al. Application of chemical fractionation methods for characterisation of biofuels, waste derived fuels and CFB co-combustion fly ashes[J]. Fuel, 2008, 87(15/16): 3183-3193. DOI: 10.1016/j.fuel.2008.05.030.
[4]	LIU R, JIN B, ZHONG Z. Comparison of two kinds of bed materials during CFB combustion of cotton stalk[J]. Chemical Engineering & Technology, 2007, 30(10): 1434-1439. DOI: 10.1002/ceat.200600396.
[5]	GUNGOR A, ESKIN N. Two-dimensional coal combustion modeling of CFB[J]. International Journal of Thermal Sciences, 2008, 47(2): 157-174. DOI: 10.1016/j.ijthermalsci.2007.01.017.
[6]	GUNGOR A. Two-dimensional biomass combustion modeling of CFB[J]. Fuel, 2008, 87(8/9): 1453-1468. DOI: 10.1016/j.fuel. 2007.08.013.
[7]	GAUVILLE P, FOUCHER J C, MOREAU D. Achievable combustion efficiency with Alstom CFB boilers for burning discarded coal[J]. Journal of the Southern African Institute of Mining and Metallurgy, 2012, 112(6): 437-447.
[8]	MAYNE D Q, RAWLINGS J B, RAO C V, et al. Constrained model predictive control: stability and optimality[J]. Automatica, 2000, 36(6): 789-814. DOI: 10.1016/S0005-1098(00)00173-4.
[9]	QIN S J, BADGWELL T A. A survey of industrial model predictive control technology[J]. Control Engineering Practice, 2003, 11(7): 733-764. DOI: 10.1016/S0967-0661(02)00186-7.
[10]	GOODWIN G C, KONG H, MIRZAEVA G, et al. Robust model predictive control: reflections and opportunities[J]. Journal of Control and Decision, 2014, 1(2): 115-148. DOI: 10.1080/23307706. 2014.913837.
[11]	MAYNE D Q. Model predictive control: recent developments and future promise[J]. Automatica, 2014, 50(12): 2967-2986. DOI: 10.1016/j.automatica.2014.10.128.
[12]	刘雨波, 罗雄麟, 许锋. 分布式预测控制全局协调及稳定性分析[J]. 化工学报, 2013, 64(4): 1318-1331. DOI: 10.3969/j.issn. 0438-1157. 2013.04.028. LIU Y B, LUO X L, XU F. Global coordination and stability analysis for distributed model predictive control system[J]. CIESC Journal, 2013, 64(4): 1318-1331. DOI: 10.3969/j.issn.0438-1157.2013.04.028.
[13]	邹涛, 王丁丁, 潘昊. 从区间模型预测控制到双层结构模型预测控制[J]. 化工学报, 2013, 64(12): 4474-4483. DOI: 10.3969/j.issn. 0438-1157.2013.12.031. ZOU T, WANG D D, PAN H. From zone model predictive control to double-layered model predictive control[J]. CIESC Journal, 2013, 64(12): 4474-4483. DOI: 10.3969/j.issn.0438-1157.2013.12.031.
[14]	HILMI A, HUSEYIN D, MIHAI C. Control of the bed temperature of a circulating fluidized bed boiler by using particle swarm optimization[J]. Advances in Electrical and Computer Engineering, 2012, 12(2): 27-32. DOI: 10.4316/AECE.2012.02005.
[15]	YING C M, JOSEPH B. Performance and stability analysis of LP-MPC and QP-MPC cascade control system[J]. AIChE Journal, 1999, 45(7): 1521-1534. DOI: 10.1002/aic.690450714.
[16]	ALEXEI N, CHRISTOPHER L E. Sensitivity analysis of LP-MPC cascade control systems[J]. Journal of Process Control, 2009, 19: 16-24. DOI: 10.1016/j.jprocont.2008.02.005.
[17]	RAO C V, RAWLINGS J B. Steady states and constraints in model predictive control[J]. AIChE Journal, 1999, 45(5): 1266-1278. DOI: 10.1002/aic.690450612.
[18]	赵超, 张登峰, 许巧玲, 等. 基于加权偏离度统计方法的预测控制性能评估算法[J]. 化工学报, 2012, 63(12): 3971-3977. DOI: 10.3969/j.issn.0438-1157.2012.12.033. ZHAO C, ZHANG D F, XU Q L, et al. Control performance assessment of model predictive control based on statistics analysis of weighted points[J]. CIESC Journal, 2012, 63(12): 3971-3977. DOI: 10.3969/j.issn.0438-1157.2012.12.033.
[19]	罗雄麟, 于洋, 许鋆. 化工过程预测控制的在线优化实现机制[J]. 化工学报, 2014, 65(10): 3984-3992. DOI: 10.3969/j.issn. 0438-1157. 2014.10.032. LUO X L, YU Y, XU J. Online optimization implementation on model predictive control in chemical process[J]. CIESC Journal, 2014, 65(10): 3984-3992. DOI: 10.3969/j.issn.0438-1157. 2014. 10.032.
[20]	WANG C, JIA L, TAN Y, et al. Carbonation of fly ash in oxy-fuel CFB combustion[J]. Fuel, 2007, 87(7): 1108-1114. DOI: 10.1016/j.fuel.2007.06.024.

循环流化床锅炉燃烧过程预测控制与经济性能优化

Predictive control and economic performance optimization of CFBB combustion process

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献 20

相关文章 15

编辑推荐

Metrics

本文评价

[1]	杨欣, 王文, 徐凯, 马凡华. 高压氢气加注过程中温度特征仿真分析[J]. 化工学报, 2023, 74(S1): 280-286.
[2]	康飞, 吕伟光, 巨锋, 孙峙. 废锂离子电池放电路径与评价研究[J]. 化工学报, 2023, 74(9): 3903-3911.
[3]	陈哲文, 魏俊杰, 张玉明. 超临界水煤气化耦合SOFC发电系统集成及其能量转化机制[J]. 化工学报, 2023, 74(9): 3888-3902.
[4]	曹跃, 余冲, 李智, 杨明磊. 工业数据驱动的加氢裂化装置多工况切换过渡状态检测[J]. 化工学报, 2023, 74(9): 3841-3854.
[5]	齐聪, 丁子, 余杰, 汤茂清, 梁林. 基于选择吸收纳米薄膜的太阳能温差发电特性研究[J]. 化工学报, 2023, 74(9): 3921-3930.
[6]	何松, 刘乔迈, 谢广烁, 王斯民, 肖娟. 高浓度水煤浆管道气膜减阻两相流模拟及代理辅助优化[J]. 化工学报, 2023, 74(9): 3766-3774.
[7]	邢雷, 苗春雨, 蒋明虎, 赵立新, 李新亚. 井下微型气液旋流分离器优化设计与性能分析[J]. 化工学报, 2023, 74(8): 3394-3406.
[8]	陈国泽, 卫东, 郭倩, 向志平. 负载跟踪状态下的铝空气电池堆最优功率点优化方法[J]. 化工学报, 2023, 74(8): 3533-3542.
[9]	刘文竹, 云和明, 王宝雪, 胡明哲, 仲崇龙. 基于场协同和耗散的微通道拓扑优化研究[J]. 化工学报, 2023, 74(8): 3329-3341.
[10]	张曼铮, 肖猛, 闫沛伟, 苗政, 徐进良, 纪献兵. 危废焚烧处理耦合有机朗肯循环系统工质筛选与热力学优化[J]. 化工学报, 2023, 74(8): 3502-3512.
[11]	诸程瑛, 王振雷. 基于改进深度强化学习的乙烯裂解炉操作优化[J]. 化工学报, 2023, 74(8): 3429-3437.
[12]	吴文涛, 褚良永, 张玲洁, 谭伟民, 沈丽明, 暴宁钟. 腰果酚生物基自愈合微胶囊的高效制备工艺研究[J]. 化工学报, 2023, 74(7): 3103-3115.
[13]	汤晓玲, 王嘉瑞, 朱玄烨, 郑仁朝. 基于Pickering乳液的卤醇脱卤酶催化合成手性环氧氯丙烷[J]. 化工学报, 2023, 74(7): 2926-2934.
[14]	文兆伦, 李沛睿, 张忠林, 杜晓, 侯起旺, 刘叶刚, 郝晓刚, 官国清. 基于自热再生的隔壁塔深冷空分工艺设计及优化[J]. 化工学报, 2023, 74(7): 2988-2998.
[15]	江锦波, 彭新, 许文烜, 门日秀, 刘畅, 彭旭东. 泵出型螺旋槽油气密封泄漏特性及参数影响研究[J]. 化工学报, 2023, 74(6): 2538-2554.