Ng, Wei Ling (2018) Adsorption Of Acid Violet 7 Dye Using RHA/CFA Sorbent : Modelling, Process Analysis And Optimization. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Kimia. (Submitted)
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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.
| Item Type: | Monograph (Project Report) |
|---|---|
| Subjects: | T Technology T Technology > TP Chemical Technology |
| Divisions: | Kampus Kejuruteraan (Engineering Campus) > Pusat Pengajian Kejuruteraan Kimia (School of Chemical Engineering) > Monograph |
| Depositing User: | Mr Engku Shahidil Engku Ab Rahman |
| Date Deposited: | 27 Jul 2022 07:01 |
| Last Modified: | 27 Jul 2022 07:01 |
| URI: | http://eprints.usm.my/id/eprint/53629 |
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