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Modeling pseudo-second-order kinetics of orange peel-paracetamol adsorption process using artificial neural network
Chemometrics and Intelligent Laboratory Systems ( IF 3.9 ) Pub Date : 2020-08-01 , DOI: 10.1016/j.chemolab.2020.104053
Inioluwa Christianah Afolabi , Segun Isaiah Popoola , Olugbenga Solomon Bello

Abstract In this work, an artificial neural network (ANN) was developed to model the Pseudo- Second Order (PSO) kinetics of orange peel-paracetamol adsorption process. The orange peel used for the adsorption process was prepared, activated, and characterized using Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) techniques respectively. Batch adsorption experiment was carried out to obtain the concentrations of paracetamol (PCM) adsorbed on orange peel activated carbon (OPAC) at different operating conditions which include contact time (0–330 ​minutes), initial PCM concentration (10–50 ​mg/L), and temperature (30–50 ​°C). Then, the experimental data was used to compute PSO kinetics of the orange peel – paracetamol adsorption process. To predict the PSO kinetics, different ANN structures were investigated. The optimal ANN structure which uses 18 hidden neurons, hyperbolic tangent sigmoid transfer function (tansig) at the input layer, linear transfer function (purelin) at the output layer, and Levenberg Marquardt as its backpropagation algorithm demonstrated the optimal prediction ability. Specifically, the optimal ANN model gave Mean Average Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and R2 values of 0.0515, 0.0064, 0.0798 and 1.0000 respectively when compared with the experimental data. The results obtained showed that ANN can be used to effectively model PSO kinetics of orange peel-paracetamol adsorption process.

中文翻译:

使用人工神经网络模拟橙皮-扑热息痛吸附过程的准二级动力学

摘要 在这项工作中,开发了人工神经网络 (ANN) 来模拟橙皮-扑热息痛吸附过程的伪二阶 (PSO) 动力学。用于吸附过程的橘皮分别使用扫描电子显微镜 (SEM) 和傅立叶变换红外光谱 (FTIR) 技术进行制备、活化和表征。进行批量吸附实验以获得在不同操作条件下吸附在橘皮活性炭(OPAC)上的扑热息痛(PCM)浓度,包括接触时间(0-330分钟),初始PCM浓度(10-50mg/ L) 和温度 (30–50°C)。然后,实验数据用于计算橘皮 - 扑热息痛吸附过程的 PSO 动力学。为了预测 PSO 动力学,研究了不同的 ANN 结构。使用18个隐藏神经元的最优ANN结构,输入层的双曲正切sigmoid传递函数(tansig),输出层的线性传递函数(purelin),Levenberg Marquardt作为其反向传播算法证明了最优的预测能力。具体而言,与实验数据相比,最优 ANN 模型给出的平均平均误差 (MAE)、均方误差 (MSE)、均方根误差 (RMSE) 和 R2 值分别为 0.0515、0.0064、0.0798 和 1.0000。获得的结果表明,ANN 可用于有效模拟橘皮-扑热息痛吸附过程的 PSO 动力学。和 Levenberg Marquardt 作为其反向传播算法证明了最佳预测能力。具体而言,与实验数据相比,最优 ANN 模型给出的平均平均误差 (MAE)、均方误差 (MSE)、均方根误差 (RMSE) 和 R2 值分别为 0.0515、0.0064、0.0798 和 1.0000。获得的结果表明,ANN 可用于有效模拟橘皮-扑热息痛吸附过程的 PSO 动力学。和 Levenberg Marquardt 作为其反向传播算法证明了最佳预测能力。具体而言,与实验数据相比,最优 ANN 模型给出的平均平均误差 (MAE)、均方误差 (MSE)、均方根误差 (RMSE) 和 R2 值分别为 0.0515、0.0064、0.0798 和 1.0000。获得的结果表明,ANN 可用于有效模拟橘皮-扑热息痛吸附过程的 PSO 动力学。
更新日期:2020-08-01
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