水葫芦类吸附剂对水溶液中双氯芬酸钠的去除:基于RSM-ANN/ANFIS混合模型的建模和优化

Djemima Bulanga , Bonface Orero , Sibusiso Sibiya , Thulane Paepae , Tebogo Mashifana
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摘要

非处方药,主要是双氯芬酸钠(DCFS),已经成为制药行业的一项巨大业务。因此,这导致DCFS在水体中广泛存在,对整个生物群造成不利影响。此外,入侵水葫芦(WH)的快速生长也不断阻碍着水生生理过程甚至捕捞活动。因此,本研究考察了WH作为活性炭(WH- ac)的前驱体对水溶液中DCFS的吸附。表征分析证实了易溶WH-AC的成功合成。结果表明,RSM的最佳条件为:投加量为1.191 g/L,初始浓度为21.064 mg/L,接触时间为107 min,效率为 %。在相同条件下,实验效率为97.9% %,验证了RSM二次模型的正确性。此外,确定DCFS在WH-AC上的吸附遵循D-R>;Langmuir>Temkin>;Freundlich等温线。D-R (R²= 0.9916)和E(2.5 ×10⁻26 kJ/mol)小于10 kJ/mol说明吸附机制是物理的,在非均质表面上具有高斯能量分布。正Temkin吸附能(BT = 14.5 J/mol)和负ΔH°(-21841 Jmol−1)表明吸附是放热的。此外,与基于速度和统计指标的ANFIS相比,采用log-s型的trainLM模型对吸附效率的预测效果最好。总之,人工神经网络可以给出一个启发式预测,这对于制定吸附过程的关键决策是必要的。此外,利用WH- ac可以为WH管理和水净化提供绿色解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Water hyacinth-derived adsorbent for removal of diclofenac sodium from aqueous solution: Modeling and optimization via RSM-ANN/ANFIS hybrid
Over-the-counter medications, mainly diclofenac sodium (DCFS), have emerged as a huge business in the pharmaceutical industry. Consequently, this has led to the widespread presence of DCFS in water bodies, posing adverse effects on the entire biota. Also, the rapid growth of invasive water hyacinth (WH) has continuously impeded aquatic physiological processes and even fishing activities. Thus, this study investigated the WH as the precursor of activated carbon (WH-AC) for the adsorption of DCFS from an aqueous solution. The characterization analysis confirmed the successful synthesis of facile WH-AC. The RSM optimum condition for 100 % efficiency was determined to be 1.191 g/L dosage, 21.064 mg/L initial concentration, and 107 min contact time. Under the same conditions, the experimental efficiency was determined to be 97.9 %, which validates the RSM quadratic model. In addition, the adsorption of DCFS onto the WH-AC was determined to follow D-R>Langmuir>Temkin>Freundlich isotherms. The D-R (R² = 0.9916) and E (2.5 ×10⁻⁶ kJ/mol) less than 10 kJ/mol imply that the adsorption mechanism is physical with Gaussian energy distribution over heterogeneous surfaces. Furthermore, a positive Temkin adsorption energy (BT = 14.5 J/mol) and negative ΔH° (-21841 Jmol−1) indicate that the adsorption is exothermic. In addition, the ANN model using trainLM with log-sigmoid showed the best adsorption efficiency prediction compared to ANFIS based on speed and statistic indicators. In conclusion, ANN can give a heuristic prediction necessary for making critical decisions about the adsorption process. Also, the utilization of WH-AC can guarantee a green solution for WH management and water purification.
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