首页|基于初始氨覆盖率寻优的燃煤电站SCR脱硝系统机理建模

基于初始氨覆盖率寻优的燃煤电站SCR脱硝系统机理建模

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
随着新能源发电大规模并网,燃煤机组工况快速变化,选择性催化还原(SCR)反应器入口氮氧化物(NOx)浓度剧烈波动.传统PID控制已无法满足 NOx 的超低排放标准,需要建立准确的模型对出口 NOx 浓度进行预测,以实现快速准确的喷氨量控制.然而,在机理建模的过程中,氨覆盖率这一关键数据因无法测量而缺失,直接影响模型精度.为了解决氨覆盖率缺失的问题,提出了一种名为初始氨覆盖率寻优的方法.采用改进的粒子群优化(IPSO)方法,通过燃煤电厂运行数据对机理模型反应常数和初始氨覆盖率进行校准.结果表明:经过初始氨覆盖率寻优,机理模型预测的出口 NOx 浓度精度得到了显著提升,典型工况下,测试集平均绝对百分比误差下降了 17.1%.
Mechanism Modeling of SCR DeNOx System in Coal-Fired Units Based on Initial Ammonia Coverage Fraction Optimization
With large-scale integration of renewable energy generation into grid,the operating con-ditions of coal-fired units change rapidly,selective catalytic reduction(SCR)reactor inlet nitrogen oxides(NOx)concentration fluctuates dramatically.Traditional PID control can no longer meet the ultra-low emission standards for NOx,and it is necessary to establish an accurate model to predict the outlet NOx concentration in order to achieve fast and accurate ammonia injection control.How-ever,in the process of mechanism modeling,ammonia coverage fraction,a key data,is missing be-cause it cannot be measured,which affects model accuracy.To solve the problem of absence of NH3 coverage fraction,a method called initial ammonia coverage fraction optimization was proposed.Then the reaction constants and initial ammonia coverage fractions of the mechanism model were cal-ibrated by the coal-fired plant data with Improved Particle Swarm Optimization(IPSO)method.Results show that after initial ammonia coverage fraction optimization the performance of the mecha-nistic SCR model is obviously improved.Under typical conditions the average absolute percentage error of the test set is decreased by 17.1%.

coal-fired power plantNOxSCRIPSOinitial ammonia coverage frac-tions optimization

陈达、李德波、李峥辉、危由兴、李龙千、陈姜宏、卢志民、姚顺春

展开 >

华南理工大学 电力学院,广东 广州 510640

广东省能源高效清洁利用重点实验室,广东 广州 510640

南方电网电力科技股份有限公司,广东 广州 510080

华南理工大学 自动化科学与工程学院,广东 广州 510640

展开 >

燃煤电站 氮氧化物 选择性催化还原 改进的粒子群算法 初始氨覆盖率寻优

国家重点研发计划政府间国际科技创新合作项目广东省自然科学基金-杰出青年项目广东省省级科技计划项目佛山市科技创新项目

2019YFE01097002021B15150200712020A05051400011920001000052

2024

锅炉技术
上海锅炉厂有限公司

锅炉技术

北大核心
影响因子：0.409
ISSN：1672-4763
年,卷(期)：2024.55(4)