Open AccessOptimizations and artificial neural network validation studies for naphthalene and phenanthrene adsorption onto NH2-UiO-66(Zr) metal-organic framework
Author(s) -
Zakariyya Uba Zango,
Khairulazhar Jumbri,
Hayyiratul Fatimah Mohd Zaid,
ni Soraya Sambudi,
Juan Matmin
Publication year - 2021
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/842/1/012015
Subject(s) - adsorption , naphthalene , phenanthrene , metal organic framework , central composite design , nap , materials science , response surface methodology , artificial neural network , chemistry , chromatography , computer science , organic chemistry , artificial intelligence , neuroscience , biology
Adsorptive removal of naphthalene (NAP) and phenanthrene (PHE) was reported using NH 2 -UiO-66(Zr) metal-organic frameworks. The process was optimized by response surface methodology (RSM) using central composite design (CCD). The fitting of the model was described by the analysis of variance (ANOVA) with significant Fischer test (F-value) of 85.46 and 30.56 for NAP and PHE, respectively. Validation of the adsorption process was performed by artificial neural network (ANN), achieving good prediction performance at node 6 for both NAP and PHE with good agreement between the actual and predicted ANN adsorption efficiencies. The good reusability of the MOF was discovered for 7 consecutive cycles and achieving adsorption efficiency of 89.1 and 87.2% for the NAP and PHE, respectively. The performance of the MOF in a binary adsorption system was also analyzed and the adsorption efficiency achieved was 97.7 and 96.9% for the NAP and PHE, respectively.