首页 | 本学科首页   官方微博 | 高级检索  
     

ABR处理硫酸盐有机废水的BP神经网络建模
引用本文:韦添尹,蒋永荣,刘可慧,刘成良,张威. ABR处理硫酸盐有机废水的BP神经网络建模[J]. 环境工程学报, 2013, 7(8): 2997-3000
作者姓名:韦添尹  蒋永荣  刘可慧  刘成良  张威
作者单位:桂林电子科技大学生命与环境科学学院,桂林,541004
基金项目:广西自然科学基金资助项目,广西科学研究与技术开发计划课题,广西教育厅项目(LD10071Y
摘    要:通过厌氧折流板反应器(ABR)处理硫酸盐有机废水的实验数据对BP神经网络进行训练,建立了ABR处理硫酸盐有机废水的BPNN模型,通过测试对比,找出了较优训练函数为traingda,较优训练次数为1 900.利用分割连接权值法(PCW)对影响出水SO42-和COD的主要因素进行分析,结果显示进水COD、SO42-、pH、COD/SO42-和HRT对出水SO42-和COD均产生一定影响,其中进水pH对出水SO42-和COD的影响最大,相对重要性(RI)指数分别为30.79%和23.44%;并通过样本试验数据分别建立了对SO42-和COD去除率的限制因子仿真模型,为预测硫酸盐有机废水的厌氧处理过程提供指导.

关 键 词:BP神经网络  硫酸盐有机废水处理  厌氧折流板反应器(ABR)  建模  仿真

Model and simulink of anaerobic baffled reactor treating sulfate organic wastewater based on back-propagation neural network
Wei Tianyin,Jiang Yongrong,Liu Kehui,Liu Chengliang and Zhang Wei. Model and simulink of anaerobic baffled reactor treating sulfate organic wastewater based on back-propagation neural network[J]. Techniques and Equipment for Environmental Pollution Control, 2013, 7(8): 2997-3000
Authors:Wei Tianyin  Jiang Yongrong  Liu Kehui  Liu Chengliang  Zhang Wei
Affiliation:College of Life and Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China;College of Life and Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China;College of Life and Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China;College of Life and Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China;College of Life and Environmental Science, Guilin University of Electronic Technology, Guilin 541004, China
Abstract:The back-propagation neural network(BPNN) trained with the data from the sulfate organic wastewater treatment of anaerobic baffled reactor(ABR) and a network model was built. The better training function and times were 'traingda' and 1 900, respectively. Partition connection weights (PCW) was adopted to analyze the dominant factors of effluent COD and SO42-. The results showed that all of the factors (feed COD, SO42-, pH, COD/SO42- and HRT) had an influence on effluent COD and SO42-. Nevertheless, the feed pH was the dominant factor, which relative importance (RI) were 30.79% and 23.44%, respectively. The model and simulink on restrictive factors for COD and SO42- removal were built respectively, which can be used for prediction on sulfate organic wastewater treatment.
Keywords:back propagation neural network  containing sulfate organic wastewater treatment  anaerobic baffled reactor (ABR)  model identification  simulink
本文献已被 万方数据 等数据库收录!
点击此处可从《环境工程学报》浏览原始摘要信息
点击此处可从《环境工程学报》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号