低溶解氧下氨氧化过程神经网络预测控制模型
发布时间：2018-11-21 08:43
【摘要】：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.
[Abstract]:Under the condition of low dissolved oxygen (DO), the neural network prediction method was applied to the process of wastewater treatment, and the predictive control model of ammonia oxidation process based on neural network was established. Predict and control the ammonia oxidation process of wastewater treatment. The model is divided into two parts: one is to predict the end time of ammonia oxidation according to the change of on-line pH value, the correlation coefficient R is 0.9985; the other is real-time prediction of ammonia nitrogen concentration based on on-line pH value, R value is 0.9083. The experimental results show that the model has high prediction accuracy, good controllability, good adaptability and stability, and has important guidance and reference significance for realizing and stabilizing short-cut nitrification and promoting mainstream process (anaerobic ammonia oxidation).
【作者单位】： 北京工业大学北京市水质科学与水环境恢复工程重点实验室;中国人民大学环境学院;
【基金】：国家自然科学基金(51508561) 北京市委组织部青年拔尖团队 北京市优秀人才培养资助计划
【分类号】：X703
[Abstract]:Under the condition of low dissolved oxygen (DO), the neural network prediction method was applied to the process of wastewater treatment, and the predictive control model of ammonia oxidation process based on neural network was established. Predict and control the ammonia oxidation process of wastewater treatment. The model is divided into two parts: one is to predict the end time of ammonia oxidation according to the change of on-line pH value, the correlation coefficient R is 0.9985; the other is real-time prediction of ammonia nitrogen concentration based on on-line pH value, R value is 0.9083. The experimental results show that the model has high prediction accuracy, good controllability, good adaptability and stability, and has important guidance and reference significance for realizing and stabilizing short-cut nitrification and promoting mainstream process (anaerobic ammonia oxidation).
【作者单位】： 北京工业大学北京市水质科学与水环境恢复工程重点实验室;中国人民大学环境学院;
【基金】：国家自然科学基金(51508561) 北京市委组织部青年拔尖团队 北京市优秀人才培养资助计划
【分类号】：X703
【参考文献】
相关期刊论文 前8条
1 顾升波;王淑莹;彭永臻;;短程深度脱氮中试工艺的低温启动和维持[J];环境科学;2013年08期
2 李泽兵;李军;李妍;马家轩;王晓毅;;短程硝化反硝化技术研究进展[J];给水排水;2011年09期
3 田建平;曹东卫;李海楠;;LM-BP神经网络在于桥水库水质预测中的应用[J];水利信息化;2010年04期
4 王淑莹;黄惠s，
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