Hazra Mohamed, Addar Fatima Zahra, Tahaikt Mustapha, Elmidaoui Azzedine, Taky Mohamed, Belhamidi Sakina
Chemistry Laboratory of Advanced Materials and Processes Engineering, Faculty of Sciences, Ibn Tofail University, P.O. Box 1246, Kenitra, Morocco.
Chemistry Superior School of Technology, Ibn Tofail University, P.O. Box 1246, Kenitra, Morocco.
ChemistryOpen. 2024 Dec;13(12):e202400163. doi: 10.1002/open.202400163. Epub 2024 Oct 23.
This study aims to optimize ammonium removal from NHCl-enriched groundwater at different concentrations using an electrodialysis (ED) process. A customized design (CD) based on response surface methodology (RSM) was employed to develop predictive models and improve the performance of the demineralization system. Ion removal efficiency was evaluated in 32 unique experimental configurations, taking into account variations in three input parameters: voltage (A), initial ammonium concentration (B) and demineralization rate (C). These parameters were selected for their impact on two response variables: electric conductivity (Y) and final ammonium concentration (Y). An in-depth analysis of variance (ANOVA) was performed to examine the variables and their interactions. The results indicated that Y was significantly influenced by C, while Y was influenced by B. In addition, the predictive models demonstrated strong correlations, with a coefficient of determination (R) greater than 0.88 for both response variables. The RSM approach applied to optimize the parameters studied identified the following optimum values: 14.17 V for A, 1 mg/L for B and 70 % for C, giving Y of 215.377 μS/cm and Y of 0.279 mg/L.
本研究旨在利用电渗析（ED）工艺优化不同浓度富含氯化铵的地下水中铵的去除。采用基于响应面法（RSM）的定制设计（CD）来开发预测模型并提高脱盐系统的性能。在32种独特的实验配置中评估了离子去除效率，同时考虑了三个输入参数的变化：电压（A）、初始铵浓度（B）和脱盐率（C）。选择这些参数是因为它们对两个响应变量有影响：电导率（Y）和最终铵浓度（Y）。进行了深入的方差分析（ANOVA）以检验变量及其相互作用。结果表明，Y受C的显著影响，而Y受B的影响。此外，预测模型显示出很强的相关性，两个响应变量的决定系数（R）均大于0.88。应用RSM方法优化所研究的参数，确定了以下最佳值：A为14.17 V，B为1 mg/L，C为70%，此时Y为215.377 μS/cm，Y为0.279 mg/L。
