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.com
Volume 9
Journal of Earth Science & Climatic Change
Natural Hazards Congress 2018
July 26-27, 2018
July 26-27, 2018 Melbourne, Australia
2
nd
International Conference on
Natural Hazards and Disaster Management
Optimization condition of solid waste vegetable oil industry modification in Arsenite and Rrsenate
removal and its prediction using artificial neural network
Afsoon Moatari-Kazerouni
Rhodes University, South Africa
A
rsenic usually is built up in the body through drinking water and food contaminated with arsenic and causes increased
risks of cancer in the skin, lungs, liver, kidney and bladder. This study is the laboratory scale that investigates the influence
of pH, contact time and Fe
2+
/H
2
O
2
on modification of solid waste vegetable oil industry in arsenic removal were investigated.
An artificial neural network model for arsenic removal during adsorption process under experimental conditions was derived
and validated. It was observed that the arsenic removal efficiency was influenced by two of these parameters. Fe
2+
/H
2
O
2
is an
important factor that affects both As(III) and As(V) removal (P<0.01). pH is another factor that affects significantly the As(V)
and As(III) removal (P<0.05). It was observed that the maximum As(III) removal by the modified solid waste vegetable oil
industry was obtained at pH 2, Fe
+2
/H
2
O
2
=0.04 and 30 minutes of contact time (81%) whereas, the maximum As(V) removal
was obtained under the conditions of pH 5, Fe
+2
/H
2
O
2
=0.04 and 30 minutes of contact time (75%). The efficiency of Arsenic
removal of the ANN model was compared with experimental value; error was small and within acceptable range. This study
shows that Fenton is an effective method for modification of solid waste vegetable oil industry in removal of As(III) and As(V)
from aqueous solution. The simulative results showed that the application of ANN to Arsenic removal is feasible and has the
high efficiency and precision.
a.kazerouni@ru.ac.zaJ Earth Sci Clim Change 2018, Volume 9
DOI: 10.4172/2157-7617-C2-043