• AWWA MTC57563
Provide PDF Format

Learn More

AWWA MTC57563

  • Predicting Contaminant Removal During Nanofiltration Using Artificial Neural Networks
  • Conference Proceeding by American Water Works Association, 03/05/2003
  • Publisher: AWWA

$12.00$24.00


An artificial neural network model is derived and validated for predicting contaminantremoval during nanofiltration of ground and surface waters under conditions typical of drinkingwater treatment. The network was trained using operating conditions such as permeate flux, feedwater recovery, and element recovery (crossflow velocity), and feed water quality parametersincluding pH, total dissolved solids concentration (surrogate for ionic strength), targetcontaminant concentration, and where possible the diffusion coefficient as inputs to predict thepermeate concentration. Deterministic and pseudo stochastic simulations showed that artificialneural networks closely predicted permeate concentrations of several organic and inorganiccontaminants in experiments using source waters from seven different locations by twocommercial thin film composite membranes operating in a wide range of permeate fluxes andfeed water recoveries. Hence, neural networks can predict transport of heterogeneous watertreatment contaminants such as natural organic matter and disinfection byproduct precursors,whose physicochemical properties are unknown. Includes 36 references, figure.

Related Products

AWWA WQTC65766

AWWA WQTC65766

Impact of MIEX Pretreatment on Downstream Conventionally Treated Water Quality Parameters..

$12.00 $24.00

AWWA WQTC57187

AWWA WQTC57187

Bromate Control During Ozonation of High-Bromide Drinking Water..

$12.00 $24.00

AWWA WQTC58965

AWWA WQTC58965

Assessment of Arsenic Treatment Alternatives for Source Waters in New Jersey, California, and Arizon..

$12.00 $24.00

AWWA ACE70521

AWWA ACE70521

Sinkzilla: Managing Media during an Interstate Disaster..

$12.00 $24.00