Optimizations and artificial neural network validation studies for naphthalene and phenanthrene adsorption onto NH2-UiO-66(Zr) metalorganic framework

Abstract

. Adsorptive removal of naphthalene (NAP) and phenanthrene (PHE) was
reported using NH2-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