摘要
为了实现“双碳”目标,对电站锅炉燃烧系统进行改造升级势在必行。首先利用精英反向学习策略、动态惯性权重和自适应t分布变异对麻雀搜索算法(SSA)的种群初始化和位置更新进行改进,获得一种新的改进麻雀搜索算法(ISSA)。然后通过ISSA优化核极限学习机(KELM)的正则化系数和核函数参数,建立ISAA-KELM锅炉燃烧特性预测模型。采用该预测模型对某超超临界660 MW机组实际运行数据进行预测,预测结果得到锅炉NO_(x)排放质量浓度和锅炉热效率的平均绝对误差率分别为1.441 7%和0.023 9%,预测效果较好。最后,根据该模型预测结果,利用ISSA对2种典型工况锅炉运行可调参数进行寻优,优化后低负荷工况锅炉NO_(x)排放质量浓度降低约91.73 mg/m~3,热效率提高0.54%,高负荷工况锅炉NO_(x)排放质量浓度降低约45.96 mg/m~3,热效率提高0.50%。
In order to achieve the carbon emission peak and carbon neutrality goals,it is imperative to reform and upgrade the combustion system of utility boilers.Firstly,a new improved sparrow search algorithm(ISSA) is developed by using elite opposition-based learning strategy,adaptive dynamic inertia weight and adaptive t-distribution mutation,to improve the population initialization and position update of sparrow search algorithm(SSA).Then,by optimizing the regularization coefficient and kernel function parameters of kernel extreme learning machine(KELM) by ISSA,the prediction model of boiler combustion characteristics is established.The model is used to predict acutral operation data of an ultra supercritical 660 MW unit,and the average absolute error rate of the predicted NO_(x) emission mass concentration and boiler thermal efficiency is 1.7191% and 0.0239%,respectively,indicating the prediction effect is good.Finally,based on the prediction result,the ISSA is used again to optimize the adjustable operating parameters of the utility boiler.After optimizaiton,the NO_(x) emission mass concentration reduces by about 91.73 mg/m~3 at low load and 45.96 mg/m~3 at high load,and the boiler thermal efficiency increases by 0.54% at low load and 0.50% at high load.
作者
冯磊华
张杰
詹毅
FENG Leihua;ZHANG Jie;ZHAN Yi(School of Energy and Power Engineering,Changsha University of Science and Technology,Changsha 410114,China)
出处
《热力发电》
CAS
CSCD
北大核心
2022年第9期96-102,共7页
Thermal Power Generation
基金
湖南省自然科学基金项目(2018JJ3552)。
