低溶解氧下氨氧化过程神经网络预测控制模型

摘要

Under low dissolved oxygen (DO) concentration,the neural network prediction method was applied in SBR for treating domestic wastewater.The neural network control model was built to predict and control the ammonia oxidizing process.The model was divided into two parts.In the first part with the correlation coefficient (R value) of 0.9985,the end of ammonia oxidization was predicted based on the on-line pH variations.In the second part with R value of 0.9083,the ammonia concentration was real-time predicted based on the on-line pH variations.The results showed that the model with high prediction accuracy,good controllability,better adaptability and stability,can not only benefit for achieving and stabilizing short-cut,but also promote the application of anaerobic ammonium oxidation for treating domestic wastewater.%在低溶解氧(DO)状态下,以城市生活污水为研究对象,将神经网络预测的方法应用到污水处理过程中,建立了基于神经网络的氨氧化过程预测控制模型,预测并控制污水处理氨氧化过程.该模型分为两部分,一是根据在线pH值变化预测氨氧化结束时间,其相关系数R值为0.9985;二是根据在线pH值实时预测氨氮浓度,R值为0.9083.试验结果表明该模型预测精度高、可控性好,具有较好的适应性和稳定性,对实现并稳定短程硝化以及促进主流工艺(厌氧氨氧化)有重要的指导和借鉴意义.