Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studies.

IF 4.3 2区 化学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Simon Bbumba, John Ssekatawa, Ibrahim Karume, Emmanuel Tebandeke, Moses Kigozi, Solomon Yiga, Robert Setekera, Joseph Ssebuliba, Steven Sekitto, Ruth Mbabazi, Ivan Kiganda, Maximillian Kato, Patrick Taremwa, Moses Murungi, Chinaecherem Tochukwu Arum, Collins Yiiki Letibo, Geofrey Kaddu, Margret Namugwanya, John Kusasira, Peace Mwesigwa, Muhammad Ntale
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引用次数: 0

Abstract

This study involved the chemical synthesis of Metal-organic Frameworks (MOFs). The synthesized MOFs were characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), and Powder X-ray diffraction (PXRD). Artificial intelligence models such as Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to predict and optimize the adsorptive removal of Rhodamine B (RhB) from water. The adsorption process was optimized using RSM with a Central Composite Design (CCD), which predicted a maximum removal efficiency of 95.91% under the following conditions: initial dye concentration (10 mg/L), adsorbent dosage (15 mg), pH (6), and temperature (25 °C). ANN was also optimized using similar conditions and the resulting predictive removal efficiency of 97.18% was obtained. Non-linear isotherm studies strongly correlated with the Freundlich (R² = 0.9987) and Sips (R² = 0.9928) models, indicating multilayer and monolayer adsorption. Non-linear Pseudo-first-order, Pseudo-second-order, and Elovich model correlation coefficients of 0.9644, 0.9998, and 0.952 suggested that the mechanisms were by chemisorption and physisorption on energetically stable heterogeneous surfaces. The findings of this study show a dual approach based on metal-organic framework and machine learning models as efficient alternatives to understanding the removal of RhB from water.

利用金属有机框架预测和优化罗丹明B从水中去除:RSM-CCD, ANN,非线性动力学和等温线研究。
本研究涉及金属有机骨架(MOFs)的化学合成。利用扫描电子显微镜(SEM)、傅里叶变换红外(FTIR)和粉末x射线衍射(PXRD)对合成的mof进行了表征。采用响应面法(RSM)和人工神经网络(ANN)等人工智能模型对水中罗丹明B (RhB)的吸附去除效果进行了预测和优化。采用中心复合设计(CCD)优化RSM吸附工艺,在初始染料浓度(10 mg/L)、吸附剂用量(15 mg)、pH(6)、温度(25℃)条件下,最大去除率为95.91%。在相似条件下对人工神经网络进行优化,预测去除率为97.18%。非线性等温线研究与Freundlich (R²= 0.9987)和Sips (R²= 0.9928)模型密切相关,表明多层吸附和单层吸附。非线性伪一阶、伪二阶和Elovich模型相关系数分别为0.9644、0.9998和0.952,表明吸附机理为在能量稳定的非均质表面上的化学吸附和物理吸附。这项研究的结果表明,基于金属有机框架和机器学习模型的双重方法是理解从水中去除RhB的有效替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
BMC Chemistry
BMC Chemistry Chemistry-General Chemistry
CiteScore
5.30
自引率
2.20%
发文量
92
审稿时长
27 weeks
期刊介绍: BMC Chemistry, formerly known as Chemistry Central Journal, is now part of the BMC series journals family. Chemistry Central Journal has served the chemistry community as a trusted open access resource for more than 10 years – and we are delighted to announce the next step on its journey. In January 2019 the journal has been renamed BMC Chemistry and now strengthens the BMC series footprint in the physical sciences by publishing quality articles and by pushing the boundaries of open chemistry.
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