Abstract

A novel adsorbent namely, Azadirachta indica twig ash, was identified and evaluated for adsorption of Cu²⁺, Ni²⁺ and Zn²⁺ ions. The properties of novel adsorbent were studied by various characterization techniques such as Scanning Electron Microscopy, Fourier Transformation Infrared analysis, X-ray diffraction, proximate and ultimate analysis. Furthermore, an optimization study was conducted to determine the optimal value of process parameters. Various dimensionless numbers have been derived and used to elucidate the adsorption dynamics in the present study. Application of Artificial Neural Network in data modeling together with comparative analysis of novel adsorbents with other adsorbents has been done. Elemental analysis revealed 38.47% Carbon, 1.89% Hydrogen, 49.21% Oxygen and 10.43% Nitrogen in the A. indica twig ash. Outcome of X-ray diffraction showed amorphous characteristic (58.4%) of an adsorbent. BET surface area and bulk density was found to be 71.35 m²/g and 0.47 g/cm³ for A. indica twig ash. Furthermore, the correlation plot showed a deviation of 0.16% for Cu²⁺, 0.09% for Ni²⁺ and 0.08% for Zn²⁺ between experimental and Artificial Neural Network predicted values which implied that the chosen algorithm and methodology were appropriate for prediction. The obtained values of film and pore diffusivity were 2.13 × 10⁻¹², 5.17 × 10⁻¹², 3.43 × 10⁻¹¹ cm² sec⁻¹ and 3.70 × 10⁻¹³, 4.44 × 10⁻¹³, 5.55 × 10⁻¹³ cm² sec⁻¹ for Cu²⁺, Ni²⁺ and Zn²⁺ ions, respectively. These values revealed that the adsorption mechanism depends on the pore diffusion. The values of dimensionless numbers $(\phi ,$ N_k and $\lambda )\mathrm{}$ indicated that the adsorption of Cu²⁺, Ni²⁺ and Zn²⁺ ions on A. indica twig ash surface was predominantly controlled by diffusion. Moreover, isotherm and kinetic modeling demonstrated superior values of the regression coefficient for the Langmuir isotherm and pseudo-second order kinetic model. This reflected that the adsorption occurred in monolayer fashion. Simultaneous removal of metal ions followed an order based on electronegativity: Cu²⁺ > Ni²⁺ > Zn²⁺. Positive ΔH (126,579.20 kJ/mole, 96,527.95 kJ/mole and 73,076.65 kJ/mole) and ΔS (437.65 J/mol K, 331.15, J/mol K and 248.71 J/mol K) for Cu²⁺, Ni²⁺ and Zn²⁺ ions, respectively showed that binding of metal ions on the surface of A. indica twig ash was endothermic with increased turbulence at the solid-aqueous interface.

摘要

鉴定了一种新型吸附剂印za（Azadirachta indica twig ash），并评估了其对Cu 2 ⁺，Ni ²⁺和Zn ^2+的吸附。离子。通过各种表征技术研究了新型吸附剂的性能，例如扫描电子显微镜，傅立叶变换红外分析，X射线衍射，近似分析和最终分析。此外，进行了优化研究以确定工艺参数的最佳值。各种无因次数已被推导并用于阐明本研究中的吸附动力学。人工神经网络在数据建模中的应用以及新型吸附剂与其他吸附剂的比较分析已经完成。元素分析显示，印度A草中的碳含量为38.47％，氢气为1.89％，氧气为49.21％，氮气为10.43％树枝灰。X射线衍射的结果显示出吸附剂的无定形特征（58.4％）。BET比表面积和堆密度，发现是71.35米² /克和0.47克/厘米³为印楝枝杈灰。此外，相关性图显示出0.16％对Cu的偏差²⁺，0.09％的Ni为²⁺和0.08％的Zn为²⁺试验和人工神经网络预测之间这意味着所选择的算法和方法是适合于预测值。所得到的膜和孔扩散的值分别为2.13×10 ^-12，5.17×10 ^-12，3.43×10 ^-11厘米²秒^-1和3.70×10 ^-13，4.44×10 ^-13，5.55×10 ^-13厘米²秒^-1对Cu ²⁺，镍²⁺和Zn ²⁺分别离子。这些值表明吸附机理取决于孔的扩散。无量纲数的值$（\phi ，$ N _k 和$\lambda ）\mathrm{}$结果表明，A枝木灰表面Cu 2 ⁺，Ni ²⁺和Zn ²⁺离子的吸附主要受扩散控制。此外，等温线和动力学模型证明了Langmuir等温线和伪二级动力学模型的回归系数具有较高的价值。这反映出吸附以单层方式发生。金属离子的同时去除遵循基于电负性的顺序：Cu ²⁺ > Ni ²⁺ > Zn ²⁺。Cu 2 ⁺，Ni ^2+的正ΔH（126,579.20 kJ / mol，96,527.95 kJ / mol和73,076.65 kJ / mol）和ΔS（437.65 J / mol K，331.15，J / mol K和248.71 J / mol K）Zn ²⁺和Zn ²⁺离子分别表明A枝粉煤灰表面金属离子的吸附是吸热的，在固-水界面处湍流增加。