Nano Zero-Valent Aluminum (nZVAl) Preparation, Characterization, and Application for the Removal of Soluble Organic Matter with Artificial Intelligence, Isotherm Study, and Kinetic Analysis

Abstract

Zero-valent metals proved high reactivity to adsorb and degrade various contaminants removal. The chemically prepared nZVAl was characterized using UV-Vis spectrum, X-ray diffraction (XRD), and scanning electron microscope (SEM). This investigation explores the adsorption effect of nZVAl powder toward soluble organic compounds exemplified by chemical oxygen demand (COD) standard solution. The effect of different operating parameters was studied to identify the best removal conditions. All variable and covariable data were introduced to build statistical models. The effect of the operating parameter was studied at different pH (3-10), nZVAl dosages (0.1-0.8 g), at different times (5-120 minutes), stirring rate (50-400 RPM), and initial COD concentration (100-800 mg/L). The obtained results displayed that nZVAl is effective in the removal of standard COD solutions, where the removal percentages were 56% and 96% for 800 ± 18.0 and 100 ± 11.8 mg/L COD, respectively, at 10 minutes after using nZVAl dry dosage 0.6 g/L, pH 8, and rate 100 rpm. Also, the effect of nZVAl on other wastewater contaminants removal was studied and compared with Egyptian law for draining wastewater into nonfresh water (drainage-lakes-ponds) No. 48 of 1982 limits. The results of adsorption isotherm and kinetic model of COD fitted well to Freundlich isotherm and pseudo second order, respectively. Nonlinear artificial intelligence neural network (ANN) importance data agree with linear response surface methodologies (RSM) in simulating the adsorption of COD onto nZVAl indicating that the most significant coverable is adsorbent dose. Finally, this study appropriates using nZVAl in highly contaminated wastewater rather than other chemical and biological processes.

Comparative Studies of Using Nano Zerovalent Iron, Activated Carbon, and Green Synthesized Nano Zerovalent Iron for Textile Wastewater Color Removal Using Artificial Intelligence, Regression Analysis, Adsorption Isotherm, and Kinetic Studies

“…The neural network model adapted operating coverable (pH, dose, time, stirring rate, and concentration) by connecting weight and bias through a continuous progression to build the artificial neural network architectures for color removal (target) as shown in Figure 7. 44,74 Each color contaminant removal was calculated using training and testing techniques without any excluded as explained in Table 8.…”

Effective Chromium Adsorption From Aqueous Solutions and Tannery Wastewater Using Bimetallic Fe/Cu Nanoparticles: Response Surface Methodology and Artificial Neural Network

“…Linear regression statistical equations (General Linear Model [GLM]) were estimated for chrome removal as a dependent parameter with pH, dose, stirring rate, contact time, and concentrations as Predictors by using Enter methods (Mahmoud, Farag, et al, 2019;Edwards, 2002;Edwards & Parry, 1993;Larson-Hall, 2015) with R 2 98.6% and Std. The error of Estimate 1.4.…”

“…The x-ray diffraction pattern (XRD) was used for investigating the material structure of the prepared bimetallic Fe/Cu nanoparticles (Mahmoud et al, 2018). A Philips XRG 3100 diffractometer (Netherlands) was used for performing the XRD analysis, where a graphite monochromator and a copper K-alpha radiation were utilized to generate x-rays with a wavelength of 1.5418 A⁰ (Mahmoud, Farag, et al, 2019). The X-Ray was operated at a voltage of 40 kV and a current of 40 mA.…”

The Influences of Global Geographical Climate towards COVID-19 Spread and Death

“…The ANN was performed using SPSS 23. The similar approach can be seen in [20] - [23]. Two-layer neural network is adapted, with hyperbolic tangent transfer function in the first layer andpurelin transfer function at the second layer.…”