| 基于共享最小二乘支持向量机模型的电站锅炉燃烧系统的优化 |
| |
| 引用本文： | 高芳,翟永杰,卓越,韩璞,陆原.基于共享最小二乘支持向量机模型的电站锅炉燃烧系统的优化[J].动力工程,2012(12):928-933,940. |
| |
| 作者姓名： | 高芳 翟永杰 卓越 韩璞 陆原 |
| |
| 作者单位： | [1]华北电力大学河北省发电过程仿真与优化控制重点实验室,保定071003 [2]河北大学电子信息工程学院,保定071002 [3]天津国投津能发电有限公司,天津300480 |
| |
| 基金项目： | 基金项目：中央高校基金科研业务费专项资金资助项目（12MS118）;河北省教育厅2009年科学研究计划资助项目（2009412） |
| |
| 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1000MW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．
|
| 关 键 词： | 共享模型 多输出系统 最小二乘支持向量机 粒子群优化算法 锅炉热效率 NOx排放 质量浓度 |
Combustion Optimization for Utility Boilers Based on Sharing LSSVM Model |
| |
| GAO Fang,ZHAI gong-jie,ZHUO Yue,HAN Pu,LU Yuan.Combustion Optimization for Utility Boilers Based on Sharing LSSVM Model[J].Power Engineering,2012(12):928-933,940. |
| |
| Authors: | GAO Fang ZHAI gong-jie ZHUO Yue HAN Pu LU Yuan |
| |
| Affiliation: | Hebei Engineering Research Center of Simulation ＆ Optimized Control for Power Generation, North China Electric Power University, Baoding 071003, China; School of Electronic and Information Engineering, Hebei University, Baoding 071002, China; 3. Tianjin SDIC Jinneng Electric Power Co. , Ltd. , Tianjin 300480, China) |
| |
| 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 op- erating 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. |
| |
| Keywords: | sharing model multi-output system least squares support vector machine particle swarm op-timization algorithm boiler thermal effieiency mass concentration of NOx emission |
| 本文献已被 维普 等数据库收录！ |
