Adsorption of acid violet 7 dye using rhacfa sorbent modelling, process analysis and optimization
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Date
2018-06
Authors
Ng, Wei Ling
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Abstract
The factors affecting the performance of acid violet 7 (AV 7) adsorption were analyzed,
which includes the rice husk ash (RHA)/coal fly ash (CFA) ash ratio, type of additives
used, and concentration of additives. The experiment was run based on the 3-level
factorial design in response surface methodology (RSM). The experimental results
were used to analyze the effect of input factors on dye adsorption and to build a model
to predict the performance of the system. Response surface plot suggested that higher
dye adsorption efficiency can be achieved at higher ash ratio and higher additive
concentration. Mathematical model was built using RSM and the performance of the
model was analyzed through analysis of variance (ANOVA). Another neural network
model were also built by using neural network toolbox in Matlab, and net operation
and predictor function in Mathematica. The mathematical and neural network model
were used to predict the performance of AV 7 adsorption. Due to the limited
experimental data available for neural network training, mathematical model
generated in RSM had better accuracy in predicting the output response. , with R2 of
0.9336 and RMSE of 3.3515. Numerical optimization for AV 7 adsorption was done
by RSM to obtain the optimum operating condition for adsorption to achieve
maximum dye removal efficiency. It was found out that the maximum adsorption
efficiency (45.14%) would be achieved at RHA/CFA ash ratio of 3.00 and 1 M of
NaOH.