This research aims to carry out the column study to remove toxic phenol by natural adsorbent neem leaves (Azadirachta indica). The adsorbent was characterized through SEM, XRD, FTIR, and BET. The phenol removal was performed in the fixed-bed column at optimum pH 3 and room temperature with the change of process variables, i.e., bed height (8.5–13.5 cm), flow rate (10–30 ml/min), and phenol content (100–300 mg/L). These experiments revealed that the breakthroughs of phenol occurred faster for lesser bed height, higher flow rate, and higher phenol content. The Yen et al. model is the best fitted kinetic model and is applicable for scale-up design. The DFT shows that the interaction between different components of the adsorbent and phenol. The artificial neural networks (ANN) modeling using a single hidden layered neural network is successful with the Levenberg-Marquardt algorithm. Desorption of phenol and safe disposal from the used adsorbents were reported.

本研究旨在进行柱研究，以通过天然吸附剂印楝叶（Azadirachta indica）去除有毒酚。通过SEM、XRD、FTIR和BET对吸附剂进行表征。随着工艺变量的变化，即床高（8.5-13.5 cm）、流速（10-30 ml/min）和苯酚含量的变化，在最佳pH 3和室温下在固定床柱中进行苯酚去除(100–300 毫克/升)。这些实验表明，对于较小的床高、较高的流速和较高的苯酚含量，苯酚的突破发生得更快。日元等人。模型是拟合最好的动力学模型，适用于放大设计。DFT 表明吸附剂的不同组分与苯酚之间存在相互作用。Levenberg-Marquardt 算法使用单个隐藏层神经网络进行人工神经网络 (ANN) 建模是成功的。报告了苯酚从用过的吸附剂中的解吸和安全处置。