生物脱氮除磷活性污泥系统复合模拟方法  

Modeling biological phosphorus and nitrogen removal in activated sludge systems:A hybrid approach

作　　者：韦安磊[1,2] 曾光明[1,2] 黄国和[1,2] 梁婕[1,2] 李晓东[1] 

机构地区：[1]湖南大学环境科学与工程学院,长沙410082 [2]环境生物与控制教育部重点实验室(湖南大学),长沙410082

出　　处：《环境工程学报》2010年第11期2590-2594,共5页Chinese Journal of Environmental Engineering

基　　金：国家"863"高技术研究发展计划项目(2004AA649370);长江学者和创新团队发展计划项目(IRT0719);国家自然科学基金资助项目(50808071;51039001)

摘　　要：为避免繁琐的参数校核工作,提出了活性污泥2 d号模型(ASM2d)和人工神经网络(ANNs)相结合的复合模拟方法。考察了复合方法在某污水处理厂生物脱氮除磷工艺中的应用情况。研究表明,ANNs能够准确地模拟出水实测值与未经校核的ASM2d机理模型的估计值之间的差值。利用Levenberg-Marquardt算法,对出水氨氮、总氮和总磷分别建立网络结构为5-12-1、5-8-1和5-8-1的ANNs子模型,将这些子模型输出同ASM2d机理模型输出相加便得到复合模型输出。复合模型估计值对前10.4 d(ANNs子模型训练数据时段)出水氨氮、总氮和总磷浓度的拟合平均绝对百分比误差分别为0.267、0.055和0.048;其对后2.6 d(ANNs子模型测试数据时段)出水氨氮、总氮和总磷浓度的预测平均绝对百分比误差分别为0.332、0.083和0.069。均方根误差、平均绝对误差等评价指标也表明复合模型能够给出合理的模拟结果。This paper studied a hybrid approach combining the activated sludge model No.2d（ASM2d） and an artificial neural network（ANN）.The hybrid approach was evaluated using 13-day measurements of a full scale activated sludge process.Results demonstrated that ANNs were able to simulate the difference between the measurements and estimates of an uncalibrated ASM2d.Based on Levenberg-Marquardt algorithm,three back-propagation ANN sub-models were established by trial and error with the structures of 5-12-1,5-8-1 and 5-8-1 for effluent ammonia nitrogen（NH＋4-N）,total nitrogen（TN） and total phosphorus（TP）,respectively.Then,outputs of the ANN sub-models were added to estimates of the ASM2d,which led to estimates of a hybrid model.The hybrid model fitted the measurements of NH＋4-N,TN and TP with mean absolute percentage errors（MAPEs） of 0.267,0.055 and 0.048,respectively,for the data of the first 10.4 days,which acted as training data for ANN sub-models.For the remained data,which acted as testing data for ANN sub-models,the hybrid model estimated the effluent concentrations of NH＋4-N,TN and TP with mean absolute percentage errors（MAPEs） of 0.332,0.083 and 0.069.Moreover,the hybrid model offered satisfactory performance by evaluation of root mean square errors and mean absolute errors.

关 键 词：污水处理 活性污泥 人工神经网络 数学模型 

分 类 号：X505[环境科学与工程—环境工程]