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| 基于混合PSO优化的LSSVM锅炉烟气含氧量预测控制 | |
| 引用本文： | 龙文,梁昔明,龙祖强.基于混合PSO优化的LSSVM锅炉烟气含氧量预测控制[J].中南大学学报(自然科学版),2012,43(3):980-985. |
| 作者姓名： | 龙文 梁昔明 龙祖强 |
| 作者单位： | 1. 贵州财经学院贵州省经济系统仿真重点实验室,贵州贵阳,550004;中南大学信息科学与工程学院,湖南长沙,410083 2. 中南大学信息科学与工程学院,湖南长沙,410083 3. 衡阳师范学院物理与电子信息科学系,湖南衡阳,421008 |
| 基金项目： | 国家自然科学基金资助项目(60874070,61074069);湖南省研究生科研创新项目(CX2009B038) |
| 摘 要： | 烟气含氧量是影响火电厂锅炉运行安全性和经济性的一个重要因素,影响锅炉烟气含氧量的因素多面复杂,对烟气含氧量特性进行建模与控制是实现锅炉正常运行的基础.借助现场运行数据,根据锅炉烟气含氧量的特性,建立基于最小二乘支持向量机(LSSVM)的锅炉烟气含氧量预测模型.在此基础上结合全局寻优的混合粒子群算法(PSO),对锅炉烟气含氧量进行控制.仿真结果表明:该方法能够比较准确地列火电厂锅炉烟气含氧量进行测量和控制,为锅炉燃烧系统的闭环控制与优化运行提供了新的手段. |
| 关 键 词： | 最小二乘支持向量机 粒子群算法 烟气含氧量 预测控制 |
O2 content in flue gas of boilers predictive control based on hybrid PSO and LSSVM |
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| LONG Wen , LIANG Xi-ming , LONG Zu-qiang.O2 content in flue gas of boilers predictive control based on hybrid PSO and LSSVM[J].Journal of Central South University:Science and Technology,2012,43(3):980-985. | |
| Authors: | LONG Wen LIANG Xi-ming LONG Zu-qiang |
| Institution: | 1.Key Laboratory of Economics System Simulation,Guizhou College of Finance and Economics,Guiyang 550004,China;2.School of Information Science and Engineering,Central South University,Changsha 410083,China;3.Department of Physics and Electronics Information Science,Hengyang Normal College,Hengyang 421008,China) |
| Abstract: | O2 content in flue gas is a main factor that has great impacts on the safety and economical efficiency of boiler operation.Together with many other complicated factors.Building a model to predict O2 content in flue gas is a good way to realize the normal operation of boiler.Using the data of boiler operation,a least square support vector machine(LSSVM) model of the boiler oxygen content property was developed based on gas oxygen characteristic.After that,combined with the particle swarm optimization algorithm(PSO),the O2 content in flue gas of boiler was controlled.Simulation results show that the proposed method can more accurately measure and control the O2 content in flue gas of boiler,and provide a new way to optimize and control process of boiler combustion in close-loop. |
| Keywords: | least square support vector machine(LSSVM) particle swarm optimization(PSO) O2 content in flue gas predictive control |
| 本文献已被 CNKI 万方数据 等数据库收录！ | |