Paper Type |
Contributed Paper |
Title |
Utilization of (Modified-) Ground Hazelnut Shells for Adsorption of Azo-metal Toxic Dyes: Empirical and ANFIS Modeling and Optimization |
Author |
Musa Buyukada, Sibel Uzuner and Fatih Evrendilek |
Email |
musabuyukada@ibu.edu.tr |
Abstract: Identification of both significantly influential predictors by Plackett-Burman and their interaction terms by Box-Behnken was carried out to quantify color removal efficiency of chemically modified ground hazelnut shells as a novel adsorbent for two common
textile dyes of Procion blue H-EGN 125 (PB) and Sandolan brilliant red N-BG 125 (SBR). Color removal efficiency mechanism was characterized using a Fourier transform infra-red spectrometer, an X-ray diffractometer, a scanning electron microscopy-energy dispersive spectroscopy. Multiple (non-)linear regression (M(N)LR) models and adaptive neuro fuzzy inference systems (ANFIS) were comparatively used to model color removal efficiency.
The dilute acid treatment led to the maximum biosorption capacities (qmax) of 59.67 mgืg-1 for PB and 63.62 mgืg-1 for SBR. Additionally, the maximum color removal efficiency of 98.6% for PB and 99.7% for SBR was achieved under 50 ฐC, 10 mgืL-1 IDC, 5 g BA, and
90 min RT. Futhermore, the minimum color removal efficiency of 13.5% for PB and 15.2% for SBR was obtained under 25 ฐC, 50 mgืL-1 IDC, 1 g BA, and 30 min RT. Independent validation results showed that the best-fit MNLR and ANFIS models performed similarly. Adsorption mechanism of both dyes were described by Freundlich isotherm better than the other models. |
|
Start & End Page |
342 - 354 |
Received Date |
2016-12-27 |
Revised Date |
|
Accepted Date |
2017-02-21 |
Full Text |
Download |
Keyword |
Hazelnut shell, ANFIS, Box-Behnken design, Data-driven modeling, Plackett-Burman design |
Volume |
Vol.45 No.1 (January 2018) |
DOI |
|
Citation |
Buyukada M., Uzuner S. and Evrendilek F., Utilization of (Modified-) Ground Hazelnut Shells for Adsorption of Azo-metal Toxic Dyes: Empirical and ANFIS Modeling and Optimization, Chiang Mai J. Sci., 2018; 45(1): 342-354. |
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