Isiyaka Hamza Ahmad, Jumbri Khairulazhar, Sambudi Nonni Soraya, Zango Zakariyya Uba, Fathihah Abdullah Nor Ain, Saad Bahruddin, Mustapha Adamu
Department of Fundamental and Applied Sciences, Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Malaysia
Chemical Engineering Department, Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Malaysia.
RSC Adv. 2020 Nov 27;10(70):43213-43224. doi: 10.1039/d0ra07969c. eCollection 2020 Nov 23.
An aluminium-based metal-organic framework ((MOF), MIL-53(Al)), was hydrothermally synthesized, characterized and applied for the remediation of the herbicides dicamba (3,6-dichloro-2-methoxy benzoic acid) and 4-chloro-2-methylphenoxyacetic acid (MCPA) in aqueous medium. Response surface methodology (RSM) and artificial neural network (ANN) were used to design, optimize and predict the non-linear relationships between the independent and dependent variables. The shared interaction of the effects of key response parameters on the adsorption capacity were assessed using the central composite design-RSM and ANN optimization models. The optimum adsorption capacities for dicamba and MCPA are 228.5 and 231.9 mg g, respectively. The RSM ANOVA results showed significant -values, with coefficients of determination ( ) = 0.988 and 0.987 and adjusted = 0.974 and 0.976 for dicamba and MCPA, respectively. The ANN prediction model gave = 0.999 and 0.999, adjusted = 0.997 and 0.995 and root mean square errors (RMSEs) of 0.001 and 0.004 for dicamba and MCPA, respectively. In each set of experimental conditions used for the study, the ANN gave better prediction than the RSM, with high accuracy and minimal error. The rapid removal (∼25 min), reusability (5 times) and good agreement between the experimental findings and simulation results suggest the great potential of MIL-53(Al) for the remediation of dicamba and MCPA from water matrices.
水热合成了一种铝基金属有机框架（（MOF），MIL-53（Al）），对其进行了表征，并将其应用于水介质中除草剂麦草畏（3,6-二氯-2-甲氧基苯甲酸）和4-氯-2-甲基苯氧基乙酸（MCPA）的修复。采用响应面法（RSM）和人工神经网络（ANN）设计、优化和预测自变量与因变量之间的非线性关系。使用中心复合设计-RSM和ANN优化模型评估关键响应参数对吸附容量影响的共同作用。麦草畏和MCPA的最佳吸附容量分别为228.5和231.9 mg/g。RSM方差分析结果显示显著的P值，麦草畏和MCPA的决定系数（R²）分别为0.988和0.987，调整后的R²分别为0.974和0.976。ANN预测模型的R²分别为0.999和0.999，调整后的R²分别为0.997和0.995，麦草畏和MCPA的均方根误差（RMSE）分别为0.001和0.004。在用于该研究的每组实验条件下，ANN的预测比RSM更好，具有高精度和最小误差。快速去除（约25分钟）、可重复使用性（5次）以及实验结果与模拟结果之间的良好一致性表明MIL-53（Al）在从水基质中修复麦草畏和MCPA方面具有巨大潜力。
