Arsenic in groundwater
is a harmful and hazardous substance that
must be removed to protect human health and safety. Adsorption, particularly
using metal oxides, is a cost-effective way to treat contaminated
water. These metal oxides must be selected systematically to identify
the best material and optimal operating conditions for the removal
of arsenic from water. Experimental research has been the primary
emphasis of prior work, which is time-consuming and costly. The previous
simulation studies have been limited to specific adsorbents such as
iron oxides. It is necessary to study other metal oxides to determine
which ones are the most effective at removing arsenic from water.
In this work, a molecular simulation computational framework using
molecular dynamics and Monte Carlo simulations was developed to investigate
the adsorption of arsenic using various potential metal oxides. The
molecular structures have been optimized and proceeded with sorption
calculations to observe the adsorption capabilities of metal oxides.
In this study, 15 selected metal oxides were screened at a pressure
of 100 kPa and a temperature of 298 K for As(V) in the form of HAsO
4
at pH 7. Based on adsorption capacity calculations for selected
metal oxides/hydroxides, aluminum hydroxide (Al(OH)
3
),
ferric hydroxide (FeOOH), lanthanum hydroxide La(OH)
3
,
and stannic oxide (SnO
2
) were the most effective adsorbents
with adsorption capacities of 197, 73.6, 151, and 42.7 mg/g, respectively,
suggesting that metal hydroxides are more effective in treating arsenic-contaminated
water than metal oxides. The computational results were comparable
with previously published literature with a percentage error of 1%.
Additionally, SnO
2
, which is rather unconventional to be
used in this application, demonstrates potential for arsenic removal
and could be further explored. The effects of pH from 1 to 13, temperature
from 281.15 to 331.15 K, and pressure from 100 to 350 kPa were studied.
Results revealed that adsorption capacity decreased for the high-temperature
applications while experiencing an increase in pressure-promoted adsorption.
Furthermore, response surface methodology (RSM) has been employed
to develop a regression model to describe the effect of operating
variables on the adsorption capacity of screened adsorbents for arsenic
removal. The RSM models utilizing CCD (central composite design) were
developed for Al(OH)
3
, La(OH)
3
, and FeOOH, having
R
2
values 0.92, 0.67, and 0.95, respectively,
suggesting that the models developed were correct.