基于IQGA-GRNN模型的SCR脱硝出口NO_(x)质量浓度预测方法

Prediction method for NO_(x)discharged from SCR denitrification systems based on IQGA-GRNN model

针对选择性催化还原(SCR)烟气脱硝系统工艺复杂、非线性等特点,提出了一种基于改进量子遗传算法(IQGA)和广义回归神经网络(GRNN)的燃煤电站NO_(x)排放数学模型:先采用动态旋转门对量子遗传算法(QGA)进行改进,使其搜索更为精细,然后应用IQGA对GRNN中的光滑因子进行寻优,使该算法逼近能力更强。以某300 MW供热机组为例,针对现场实际运行数据,采用IQGA-GRNN进行训练建模,并将该模型与GRNN模型、QGAGRNN模型的预测结果进行对比,结果表明,IQGA-GRNN模型的预测值与实测值最大误差在8.0%以内,平均误差在0.2%以内,可为后续喷氨量的精准控制提供有力的支撑。

According to the complex craftwork and non-linear characteristic of SCR denitrification systems,a mathematical model for NO_(x)discharged from coal-fired power plants was created based on Improved Quantum Genetic Algorithm(IQGA)and General Regression Neural Network(GRNN).Firstly,QGA was modified by revolving door to get the search results more accurate.Secondly,the smoothness factor in GRNN was optimized to improve the approximation ability of the algorithm.Taking a 300 MW heat-supply unit as an example,an IQGA-GRNN model was created and trained by the training data of the unit.The maximum error between the predicted value made by the model and the measured value is within 8.0%,and the average error is within 0.2%.The IQGA-GRNN model is supportive for precise control on NH_(3)spray.