基于GWO-BP神经网络算法的WFGD系统在线优化

Online Optimization of a WFGD System Based on GWO-BP Neural Network Algorithm

以浆液循环泵运行情况作为工况划分条件,通过提出的灰狼优化(GWO)-BP神经网络(GWO-BP神经网络)算法建立了针对湿法烟气脱硫(WFGD)系统多模态在线优化模型组,分析了机组负荷、入口SO_(2)质量浓度对出口SO2质量浓度变化量的影响,并利用某660 MW机组切换试验对该模型组性能进行验证。结果表明:随着机组负荷和入口SO_(2)质量浓度增大,切换后出口SO_(2)质量浓度变化量增大,该模型组具有较好的预测精度和泛化能力。

Based on operation conditions of slurry circulation pumps,a group of multi-modality models were established for online optimization of a wet flue gas desulfurization(WFGD)system by the grey wolf-optimized BP(GWO-BP)neural network algorithm proposed,and subsequently the effects of the following factors on the variation of outlet SO_(2) concentration were analyzed,such as the unit load and inlet SO_(2) concentration,etc.In addition,the performance of the group of models was verified through the switching test of a 660 MW unit.Results show that with the increase of unit load and inlet SO_(2) concentration,the change of outlet SO_(2) concentration increases after switching the pump,indicating high prediction accuracy and strong generalization ability of the group of models.