【摘要】 电站锅炉燃烧系统是一个复杂的多输入多输出系统,为了在同一个模型中实现高效率、低污染物排放的优化目标,对标准最小二乘支持向量机回归方法进行了扩展.借助某电厂1 000MW超超临界锅炉的现场燃烧调整试验数据,建立了以锅炉热效率和NOx排放质量浓度为输出的共享最小二乘支持向量机(LSSVM)模型,采用一种改进的粒子群算法对共享模型中的锅炉运行工况进行了寻优.结果表明:在共享LSSVM模型中,锅炉热效率和NOx排放质量浓度的平均预测误差分别可达到0.028%和2.16%,搜索得到的高效率和低NOx排放的参数组合可为电站锅炉优化运行提供指导.更多还原

【Abstract】 The boiler combustion system is a complex multi-input & multi-output system.To achieve the objectives of high efficiency and low pollutant emission for the system in one model,some improvements were carried out on the standard regression algorithm of least squares support vector machine(LSSVM).Based on the field combustion adjustment data of a 1 000 MW ultra supercritical boiler,a sharing LSSVM model was established by taking both the boiler thermal efficiency and mass concentration of NOx emission as the output variables,with which an improved particle swarm algorithm was selected to optimize the operating conditions of the boiler.Results show that with the sharing LSSVM model,the mean prediction error for boiler thermal efficiency and mass concentration of NOx emission can reach 0.028% and 2.16% respectively,the searched parameter combination of high thermal efficiency and low NOx emission may serve as a reference for optimization of boiler operation.更多还原