Abstract
In this research Artificial Neural Network (ANN) technique was applied to study the filtration
process in water treatment. Eight models have been developed and tested using data from a
pilot filtration plant, working under different process design criteria; influent turbidity, bed
depth, grain size, filtration rate and running time (length of the filtration run), recording
effluent turbidity and head losses. The ANN models were constructed for the prediction of
different performance criteria in the filtration process: effluent turbidity, head losses and
running time. The results indicate that it is quite possible to use artificial neural networks in
predicting effluent turbidity, head losses and running time in the filtration process, with a
good degree of accuracy reaching 97.26, 95.92 and 86.43% respectively. These ANN models
could be used as a support for workers in operating the filters in water treatment plants and to
improve water treatment process. With the use of ANN, water systems will get more
efficient, so reducing operation cost and improving the quality of the water produced.
process in water treatment. Eight models have been developed and tested using data from a
pilot filtration plant, working under different process design criteria; influent turbidity, bed
depth, grain size, filtration rate and running time (length of the filtration run), recording
effluent turbidity and head losses. The ANN models were constructed for the prediction of
different performance criteria in the filtration process: effluent turbidity, head losses and
running time. The results indicate that it is quite possible to use artificial neural networks in
predicting effluent turbidity, head losses and running time in the filtration process, with a
good degree of accuracy reaching 97.26, 95.92 and 86.43% respectively. These ANN models
could be used as a support for workers in operating the filters in water treatment plants and to
improve water treatment process. With the use of ANN, water systems will get more
efficient, so reducing operation cost and improving the quality of the water produced.
Keywords
Artificial Neural Network
filtration
head losses
modeling
running time.
turbidity
Water treatment