In the present study, the waste part of the banana tree was used as a precursor, and copper chloride salt was used as an impregnating agent for the preparation of adsorbent to remove both cyanide and phenol from synthetic wastewater. Initially, thermogravimetric analysis was used to determine the rate of carbonization of the material with temperature, and thus, the optimum temperature (370 °C) and time of carbonization (35 min) were assessed. Different samples of adsorbents were prepared next by varying the weight ratio of pseudo-stem of waste banana tree to copper salt from 1:1 to 30:1. All the samples were then tested for removal of both the pollutants, and the ratio (20:1) corresponding to maximum removal of both the pollutants was considered as optimum. Therefore, further studies were conducted with the adsorbent prepared at optimum ratio, temperature and time and such adsorbent was termed as copper impregnated activated banana tree (CIABT). One variable at a time approach was followed to find out the most effective condition based on the maximum removal of pollutants. Maximum removal of 95.99 ± 1.03% and 97.33 ± 0.04% was achieved for cyanide (initial concentration: 100 ppm) and phenol (initial concentration: 450 ppm), respectively, at an optimum contact time of 150 min, the particle size of 90 μ, the adsorbent dosage of 10 g/L, pH 8.0 using CIABT at 25 °C. Hybrid artificial neural network–particle swarm optimization were employed for modelling-optimization of removal of both the pollutants while achieving 91.4–99.99% and 86.43–99.99% removal of cyanide and phenol, respectively, from simulated wastewater.

本研究以香蕉树的废弃部分为前驱体，以氯化铜盐为浸渍剂制备吸附剂，去除合成废水中的氰化物和苯酚。最初，热重分析用于确定材料随温度的碳化速率，因此评估了最佳温度（370°C）和碳化时间（35分钟）。接下来，通过改变废香蕉树假茎与铜盐的重量比从 1:1 到 30:1 来制备不同的吸附剂样品。然后测试所有样品的两种污染物的去除率，对应于两种污染物的最大去除率的比率（20：1）被认为是最佳的。因此，进一步研究了以最佳比例制备的吸附剂，温度和时间，这种吸附剂被称为铜浸渍活化香蕉树（CIABT）。遵循一次一个变量的方法，以根据污染物的最大去除量找出最有效的条件。氰化物（初始浓度：100 ppm）和苯酚（初始浓度：450 ppm）的最大去除率分别为 95.99 ± 1.03% 和 97.33 ± 0.04%，最佳接触时间为 150 分钟，粒径为 90 μ ，吸附剂用量为 10 g/L，pH 8.0，使用 CIABT，25 °C。混合人工神经网络-粒子群优化用于建模优化这两种污染物的去除，同时从模拟废水中分别实现 91.4-99.99% 和 86.43-99.99% 的氰化物和苯酚去除率。