基于混合PSO优化的LSSVM锅炉烟气含氧量预测控制

O_2 content in flue gas of boilers predictive control based on hybrid PSO and LSSVM

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而复杂,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对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。

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.