基于初始氨覆盖率寻优的燃煤电站SCR脱硝系统机理建模

Mechanism Modeling of SCR DeNO_(x) System in Coal-Fired Units Based on Initial Ammonia Coverage Fraction Optimization

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(NO_(x))浓度剧烈波动。传统PID控制已无法满足NO_(x)的超低排放标准,需要建立准确的模型对出口NO_(x)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NO_(x)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%。

With large-scale integration of renewable energy generation into grid,the operating conditions of coal-fired units change rapidly,selective catalytic reduction(SCR) reactor inlet nitrogen oxides(NO_x) concentration fluctuates dramatically.Traditional PID control can no longer meet the ultra-low emission standards for NO_x,and it is necessary to establish an accurate model to predict the outlet NO_(x) concentration in order to achieve fast and accurate ammonia injection control.However,in the process of mechanism modeling,ammonia coverage fraction,a key data,is missing because it cannot be measured,which affects model accuracy.To solve the problem of absence of NH_(3) 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 calibrated 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 mechanistic SCR model is obviously improved.Under typical conditions the average absolute percentage error of the test set is decreased by 17.1%.