Uraz Ezel, Hayri-Senel Tugba, Erdol-Aydin Nalan, Nasun-Saygili Gulhayat
Istanbul Technical University, Chemical and Metallurgical Faculty, Chemical Engineering Department, 34469, Maslak, Istanbul, Turkey.
Heliyon. 2024 Oct 9;10(20):e39080. doi: 10.1016/j.heliyon.2024.e39080. eCollection 2024 Oct 30.
In this study, Ordu-Unye bentonite was used as an adsorbent in the removal of zinc from aqueous solutions. The aim of the experimental part of the study was to ascertain how zinc removal was affected by variables such as pH, adsorbent amount, contact time, and initial zinc concentration. In the second part of the experiments, bentonite was modified with two different acids and the adsorption performance of modified bentonite was also investigated. Characterization of raw and modified bentonites was also carried out using FTIR and XRD. It was observed that acid modification of bentonite negatively affected the zinc removal process from aqueous solutions. In this study, higher zinc removal (95 %) was obtained with raw bentonite compared to acid modified bentonites (58.4 % in HNO activated, 43.8 % for HSO activated). Equilibrium isotherms were obtained and modelled to explain the adsorption mechanism. Adsorption isotherm studies showed that zinc adsorption fits well with Langmuir (R: 0.99) and Temkin (R: 0.97) models. Besides from these experimental investigations, various artificial neural network (ANN) training techniques were used to optimize the zinc adsorption process. By trial and error, the optimal performance was obtained by changing the number of hidden neurons in each layer of the neural network architecture. These models under study were analyzed to determine their R and mean square error (MSE) values, and the optimal outcomes were identified. Among the various training models of ANN, it was determined that the Bayesian Regularization method exhibited the optimum network architecture with the highest R (R:0.995) and lowest MSE (MSE:0.0008) ratio.
在本研究中，奥尔杜-于内膨润土被用作从水溶液中去除锌的吸附剂。该研究实验部分的目的是确定诸如pH值、吸附剂用量、接触时间和初始锌浓度等变量如何影响锌的去除。在实验的第二部分，用两种不同的酸对膨润土进行改性，并研究了改性膨润土的吸附性能。还使用傅里叶变换红外光谱（FTIR）和X射线衍射（XRD）对原始膨润土和改性膨润土进行了表征。观察到膨润土的酸改性对从水溶液中去除锌的过程产生了负面影响。在本研究中，与酸改性膨润土（硝酸活化的为58.4%，硫酸活化的为43.8%）相比，原始膨润土获得了更高的锌去除率（95%）。获得了平衡等温线并进行建模以解释吸附机制。吸附等温线研究表明，锌的吸附与朗缪尔模型（R：0.99）和坦金模型（R：0.97）拟合良好。除了这些实验研究之外，还使用了各种人工神经网络（ANN）训练技术来优化锌的吸附过程。通过反复试验，通过改变神经网络架构各层中隐藏神经元的数量获得了最佳性能。对所研究的这些模型进行分析以确定它们的R值和均方误差（MSE）值，并确定了最佳结果。在人工神经网络的各种训练模型中，确定贝叶斯正则化方法表现出具有最高R值（R：0.995）和最低MSE值（MSE：0.0008）比率的最佳网络架构。
