In this work we reported a novel methodology in prediction of molecular separation using adsorption process, and understanding the effect of underlying adsorption process on removal of pollutants from water. The data for adsorption of thallium (I) from water was collected and the model was developed based on machine learning (ML) approach. The type of adsorbent studied in this work is Ni₅₀Co₅₀-Layered double hydroxide/UiO-66-NH₂ which is a metal organic framework-based nanocomposite material. The adsorbent was selected due to its high capacity in separation and removal of thallium (I) from aqueous solutions with surface area of around 900 m²/g and pore volume of 0.9 cc/g. The modeling and computations were performed using artificial neural network which is a machine learning technique considering the equilibrium concentration of ion in the liquid solution at equilibrium as the main output. Two inputs were considered including temperature and the initial concentration of the adsorbate. The training and validation of the model indicated very high accuracy of the model compared to other modeling approaches with high determination coefficient (R²) more than 0.99 for both training and testing the model stages.

在这项工作中，我们报道了一种使用吸附过程预测分子分离的新方法，并了解潜在吸附过程对从水中去除污染物的影响。收集了从水中吸附铊 (I) 的数据，并基于机器学习 (ML) 方法开发了模型。在这项工作中研究的吸附剂类型是 Ni ₅₀ Co ₅₀ -Layered 双氢氧化物/UiO-66-NH ₂，它是一种基于金属有机骨架的纳米复合材料。选择吸附剂是因为它具有从表面积约为 900 m ^{2 的}水溶液中分离和去除铊 (I) 的高容量/g 和 0.9 cc/g 的孔体积。建模和计算是使用人工神经网络进行的，人工神经网络是一种机器学习技术，考虑到平衡时液体溶液中离子的平衡浓度作为主要输出。考虑了两个输入，包括温度和吸附物的初始浓度。模型的训练和验证表明，与其他建模方法相比，该模型具有非常高的准确性，在训练和测试模型阶段，具有超过 0.99 的高决定系数 (R ² )。