The use of artificial neural network (ANN) for modeling adsorption of sunset yellow onto neodymium modified ordered mesoporous carbon. (October 2020)

• Record Type: Journal Article
• Authors: Ahmad, Zaki Uddin; Yao, Lunguang; Lian, Qiyu; Islam, Fahrin; Zappi, Mark E.; Gang, Daniel Dianchen
• Is Part Of: Chemosphere. Volume 256(2020)
• Publication Date: 2020-10
• Subjects: Neodymium modified OMC -- Anionic dye -- ANN modeling -- XPS spectra -- Adsorption kinetics; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2020.127081
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Discharging coloring products in water bodies has degraded water quality irreversibly over the past several decades. Order mesoporous carbon (OMC) was modified by embedding neodymium(III) chloride on the surface of OMC to enhance the adsorptive removal towards these contaminants. This paper represents an artificial neural network (ANN) based approach for modeling the adsorption process of sunset yellow onto neodymium modified OMC (OMC-Nd) in batch adsorption experiments. Neodymium modified OMC was characterized using N2 adsorption-desorption isotherm, TEM micrographs, FT-IR and XPS spectra analysis techniques. 2.5 wt% Nd loaded OMC was selected as the final adsorbent for further experiments because OMC-2.5Nd showed highest removal efficiency of 93%. The ANN model was trained and validated with the adsorption experiments data where initial concentration, reaction time, and adsorbent dosage were selected as the variables for the batch study, whereas the removal efficiency was considered as the output. The ANN model was first developed using a three-layer back propagation network with the optimum structure of 3-6-1. The model employed tangent sigmoid transfer function as input in the hidden layer whereas a linear transfer function was used in the output layer. The comparison between modeled data and experimental data provided high degree of correlation (R 2 = 0.9832) which indicated the applicability of ANN model for describing the adsorption process with reasonable accuracy. Graphical abstract: Image 1 Highlights: Neodymium embedded ordered mesoporous carbon was used for sunset yellow adsorption. Artificial neural network was trained with batch adsorption experiments dataset. Trained ANN model performed reasonably well for batch adsorption data. Optimal topology of developed ANN was found to be 3-6-1. ANN model can be improved with more training data or addition of more factors.