Paper Title:
Back-Propagation Model for Nanofiltration Process Simulation in Pesticide Wastewater Treatment
  Abstract

This study simulated the nanofiltration (NF) process of contamination removing by back-propagation neural network (BPNN), according to the test values of DK membrane pre-treating Imidacloprid pesticide wastewater. The real time nanofiltration (NF) separation model was presented for effective controlling of DK NF separation. The research showed the simulation precision met the application demands, with the correlation coefficient between the simulation and test rejection of COD and salt over 0.99, and absoluteness error below ±4%. In order to test the prediction of this BPNN simulation model, further NF experiments were carried out. Under the same multifactor condition, the predictions for the NF process performances were found to be in good agreement with the experimental results. This BP simulation model for NF process could be used to test the stability and effectively of NF system, and support the membrane technology well.

  Info
Periodical
Advanced Materials Research (Volumes 168-170)
Edited by
Lijuan Li
Pages
404-407
DOI
10.4028/www.scientific.net/AMR.168-170.404
Citation
Q. Yang, Y. J. Hu, L. Xue, "Back-Propagation Model for Nanofiltration Process Simulation in Pesticide Wastewater Treatment", Advanced Materials Research, Vols. 168-170, pp. 404-407, 2011
Online since
December 2010
Export
Price
$32.00
Share

In order to see related information, you need to Login.

In order to see related information, you need to Login.

Authors: Hong Yan Duan, You Tang Li, Chun Li Lei, Gui Ping He
Abstract:Artificial neural network (ANN) back-propagation model was developed to predict the thermal expansion behavior and internal residual strains...
154
Authors: Xuan Luo, Shi Jie Wang, Xiao Ren Lv, Hao Sun
Abstract:ESPCP is a new way of well lifting. The speed optimization is the main way to improve economic index of ESPCP system. By analyzing the main...
3569
Authors: Bo Zhao
Chapter 1: Material Engineering and its Application
Abstract:The artificial neural network model is used to predict the breaking elongation of polyester/cotton ring spinning yarn in this paper. In order...
108
Authors: Bing Hua Mo, Zi Nan Pan
Chapter 15: Materials Processing Technology
Abstract:A neural network model is established to predict the joint quality in resistance microwelding (RMW) of fine Cu wire and stainless steel thin...
1709
Authors: Bo Zhao
Chapter 1: Energy Materials and Material Applications with Analysis of Material Properties
Abstract:The filtration properties of melt blowing nonwovens are affected by the pore structure of nonwovens which is strongly related to the...
47