Faktor-faktor yang mempengaruhi prestasi penjerapan asid violet 7 (AV 7) telah dianalisiskan, termasuklah nisbah abu sekam padi (RHA)/abu terbang arang batu (CFA), jenis aditif yang digunakan, dan kepekatan aditif. Eksperimen telah dijalankan berdasarkan reka bentuk tiga-peringkat faktorial dalam kaedah permukaan sambutan (RSM). Hasil dari eksperimen telah digunakan untuk menganalisis pengaruh faktor input pada penjerapan pewarna dan untuk membina model yang dapat meramalkan prestasi sistem penjerapan. Plot permukaan tindak balas mencadangkan bahawa penjerapan pewarna yang cekap boleh dicapai pada nisbah abu dan kepekatan aditif yang tinggi. Model matematik telah dibina dengan menggunakan RSM dan prestasi model dianalisis melalui analisis varians (ANOVA). Model rangkaian neural juga dibina dengan menggunakan peralatan rangkaian neural dalam Matlab, dan juga operasi rangkaian (net operation) dan fungsi peramal (predictor function) dalam Mathematica. Model matematik dan rangkaian neural digunakan untuk meramalkan prestasi penjerapan AV 7. Disebabkan data eksperimen yang terhad untuk latihan rangkaian neural, model matematik yang dijanakan dalam RSM mempunyai ketepatan yang lebih tinggi dalam meramalkan kecekapan penjerapan pewarna AV 7 , dengan nilai R2 dari 0.9336 dan RMSE dari 3.3515. Pengoptimuman berangka untuk penjerapan AV 7 telah dilakukan oleh RSM untuk mendapatkan keadaan operasi optimum untuk proses penjerapan mencapai kecekapan maksimum. Hasil daripada pengoptimuman menunjukkan kecekapan penjerapan maksimum (45.14%) akan dicapai pada nisbah abu RHA/CFA sebanyak 3.00 dan 1 M NaOH.
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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.
Adsorption of acid violet 7 dye using rhacfa sorbent modelling, process analysis and optimization / Ng Wei Ling