An artificial neural network model is derived and validated for predicting contaminant
removal during nanofiltration of ground and surface waters under conditions typical of drinking
water treatment. The network was trained using operating conditions such as permeate flux, feed
water recovery, and element recovery (crossflow velocity), and feed water quality parameters
including pH, total dissolved solids concentration (surrogate for ionic strength), target
contaminant concentration, and where possible the diffusion coefficient as inputs to predict the
permeate concentration. Deterministic and pseudo stochastic simulations showed that artificial
neural networks closely predicted permeate concentrations of several organic and inorganic
contaminants in experiments using source waters from seven different locations by two
commercial thin film composite membranes operating in a wide range of permeate fluxes and
feed water recoveries. Hence, neural networks can predict transport of heterogeneous water
treatment contaminants such as natural organic matter and disinfection byproduct precursors,
whose physicochemical properties are unknown. Includes 36 references, figure.
| Edition : | Vol. - No. |
| File Size : | 1
file
, 220 KB |
| Note : | This product is unavailable in Ukraine, Russia, Belarus |
| Number of Pages : | 8 |
| Published : | 03/05/2003 |
