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

• Published in: IOP conference series. Earth and environmental science Vol. 842; no. 1; pp. 12015 - 12022
• Main Authors: Zango, Z U, Jumbri, K, Zaid, H F M, Sambudi, N S, Matmin, J
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.08.2021
• Subjects: Adsorption; Adsorptivity; Artificial neural networks; Metal-organic frameworks; Naphthalene; Neural networks; Phenanthrene; Response surface methodology; Variance analysis; Zirconium
• ISSN: 1755-1307; 1755-1315
• DOI: 10.1088/1755-1315/842/1/012015

Abstract 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.