Effect of the oxidation process on the molecular interaction of polyaromatic hydrocarbons (PAH) with carbon nanotubes: Adsorption kinetic and isotherm study

Agglomeration is a major setback in the use of pristine multiwall carbon nanotube for many applications. Therefore, application of hydroxyl functionalized multiwall carbon nanotubes (MWCNT-OH) for the removal of naphthalene and fluorine was investigated and compared with pristine MWCNT for the first time. FTIR, BET, TGA, SEM, and TEM were used to investigate the functional group, microstructure and morphology of MWCNT and MWCNT-OH. The BET results showed more mesoporosity for MWCNT-OH than MWCNT. Batch equilibrium adsorption data were fitted to the Langmuir, Freundlich and Temkin models, of which the Langmuir model exhibited the best fit. The correlation of Langmuir model for naphthalene adsorption on MWCNT-OH was observed to be higher (i.e. R2 = 0.9703 for MWCNT-OH and R2 = 0.5082 for MWCNT) while the correlation for adsorption of flourene is higher for MWCNT (i.e. R2 = 0.998 for MWCNT and R2 = 0.9517 for MWCNT-OH). The maximum adsorption capacity (qe) for MWCNT-OH (57.401 ng/g-356.121 ng/g for naph and 61.235 ng/g - 367.361 ng/g for fluorene) was generally higher and more stable for lower concentration ranges while it is comparable with MWCNT (48.177 ng/g - 354.00 ng/g for naph and 59.635 ng/g- 366.709 ng/g for fluorene) at higher concentration. The qe calculated (344.828 ng/g for naph and 357.143 ng/g for fluorene) using PSO was observed to be much closer to experimental qe (356.121 ng/g for naph and 367.361 ng/g for fluorine) for MWCNT-OH adsorbent than MWCNT (cal qe are 384.615 ng/g for naph and 400.000 ng/g for fluorene; exp qe 354.00 ng/g for naph and 366.709 ng/g for fluorene). The value of boundary layer thickness, is >0, this indicates the reaction process is quite complex and is not solely controlled by the intra-particle diffusion. The results of this study indicate the high potential of the developed MWCNT-OH adsorbent as an efficient and successful material for the removal of carcinogenic PAHs from the water body. © 2019 Elsevier B.V

A Multivariate Machine Learning Model of Adsorptive Lindane Removal from Contaminated Water

It is challenging to use conventional one-variable-at-time (OVAT) batch experiments to evaluate multivariate/inter-parametric interactions between physico-chemical variables that contribute to the adsorptive removal of contaminants. Thus, chemometric prediction approaches for multivariate calibration and analysis reveal the impact of multi-parametric variation on the process of concern. Hence, we aim to develop an artificial neural network (ANN), and stepwise regression (SR) models for multivariate calibration and analysis utilizing OVAT data prepared through experimentation. After comparing the models’ performance, ANN was the superior model for this application in our work. The standard deviations (SD) between the observed and ANN-predicted values were very close. The average correlation coefficient (R2) between observed and ANN-predicted values for the training dataset was 96.9%. This confirms the ability of our developed ANN model to forecast lindane removal accurately. The testing dataset correlation coefficients (89.9% for ANN and 67.75% for SR) demonstrated a better correlation between observed and predicted ANN values. The ANN model training and testing dataset RMSE values were 1.482 and 2.402, lower than the SR values of 4.035 and 3.890. The MAPE values for the ANN model’s training and testing datasets, 0.018 and 0.031, were lower than those for the SR model. The training and testing datasets have low RSR and PBIAS values, implying model strength. The R2 and WIA values are above 0.90 for both datasets, proving the ANN model’s accuracy. Applying our developed ANN model will reduce the cost of removing inorganic and organic impurities, including lindane, and optimize chemical utilization