Abstract
ABSTRACT In this study, the factors affecting the performance of rice husk ash (RHA)-coal fly ash (CFA) adsorbent in removing acid violet 7 (AV7) dye were analyzed using response surface methodology (RSM). The experiment was run based on the 3-level factorial design in RSM. Modeling and optimization analyses were carried out using RSM and artificial neural network (ANN). Response surface plot suggested that higher adsorption efficiency can be achieved at higher ash ratio and additive concentration. RSM had the highest accuracy (R2 = 0.934) in predicting dye adsorption efficiency, while ANN modeling by Mathematical calculations (i.e., using predictor function) showed the lowest accuracy (R2 = 0.733). The optimum RHA-CFA adsorbent preparation condition with the highest AV7 dye adsorption efficiency was obtained through the numerical optimization of RSM model. Through RSM optimization study, maximum adsorption efficiency obtained was 45.1% at RHA/CFA ratio of 3.00 and 1.00 M of NaOH. This study demonstrated that the second-order response surface model (RSM) together with ANN models was used successfully to predict and optimize the AV7 dye adsorption efficiency of RHA-CFA adsorbent.
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