[关键词]
[摘要]
考虑到基于智能计算的燃烧优化算法难以应用于工程领域的问题,设计了一种基于数据挖掘案例推理的电站锅炉燃烧优化系统。根据海量的分散控制系统(Distributed Control System,DCS)历史数据,采用改进的模糊减法聚类算法确定分类数,以模糊C均值算法建立初始案例库,通过目标寻优约简案例库。在线应用时,基于非可控因子的案例推理方法计算当前工况最佳燃烧参数,根据可控因子对输出参数进行修正,保证系统的实时最优性。对比某机组的优化效果表明,优化后选择性催化还原(Selective Catalytic Reduction,SCR)装置入口NO〖HT5”〗x浓度平均降低26.2 mg/m3,锅炉效率平均提升了0.21%。
[Key word]
[Abstract]
Since the intelligent optimizationbased combustion optimization algorithm is difficult to be applied in engineering field,this paper designs a boiler combustion optimization system based on data mining casebased reasoning.Based on the massive DCS historical data,this system uses the improved fuzzy subtraction clustering algorithm to determine the classification number,and establishes the initial case library by the fuzzy Cmeans algorithm.Finally,it seeks to optimize the case base through the target.Then,the casebased reasoning method based on noncontrollable factors is used to calculate the optimal combustion parameters of the current working conditions,and the outputs are corrected according to the controllable factors to ensure the realtime optimality of the system.Compared with the optimization effect of a certain unit,NO〖HT5〗x concentration at SCR inlet is reduced by 26.2 mg/nm3,and boiler efficiency is increased by 021%.
[中图分类号]
TK221
[基金项目]
内蒙古电力(集团)有限责任公司科技项目(2018-73)