A Critical Overview of Adsorption Models Linearization: Methodological and Statistical Inconsistencies

M. E. González‐López, C. M. Laureano-Anzaldo, A. A. Pérez-Fonseca, M. Arellano, J. R. Robledo‐Ortíz
{"title":"A Critical Overview of Adsorption Models Linearization: Methodological and Statistical Inconsistencies","authors":"M. E. González‐López, C. M. Laureano-Anzaldo, A. A. Pérez-Fonseca, M. Arellano, J. R. Robledo‐Ortíz","doi":"10.1080/15422119.2021.1951757","DOIUrl":null,"url":null,"abstract":"ABSTRACT The linearization of adsorption equations is controversial. The estimation of fitting parameters strongly depends on the linearization method, magnitude of experimental error, and data range. Although many studies contrast linear versions of these equations with their non-linear counterparts, linearization is preferred due to its simplicity since a line could be represented with fewer experimental points than a curve. An in-depth analysis was carried out to compare the accuracy of linear and non-linear models. Although different transformations linearize Langmuir isotherms, only one form yields reliable fitting parameters. Linear transformations could also lead to a statistical bias, favoring a model that does not represent the experimental behavior. Similar observations are discussed regarding the pseudo-second-order kinetic model. Linearization of Freundlich isotherms, pseudo-first-order kinetic models, and fixed-bed adsorption models through logarithms implies that attention must be taken on the logarithm limits by properly selecting the data range. Linearization also promotes the incorrect interpretation of models due to oversimplification. The linearized van’t Hoff equation would yield a reasonable fit with fewer experimental points than the non-linear regression, which requires more data to assure convergence. In this sense, there is convincing evidence that non-linear regression is a more robust and reliable tool for adsorption modeling.","PeriodicalId":21744,"journal":{"name":"Separation & Purification Reviews","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"56","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Separation & Purification Reviews","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15422119.2021.1951757","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 56

Abstract

ABSTRACT The linearization of adsorption equations is controversial. The estimation of fitting parameters strongly depends on the linearization method, magnitude of experimental error, and data range. Although many studies contrast linear versions of these equations with their non-linear counterparts, linearization is preferred due to its simplicity since a line could be represented with fewer experimental points than a curve. An in-depth analysis was carried out to compare the accuracy of linear and non-linear models. Although different transformations linearize Langmuir isotherms, only one form yields reliable fitting parameters. Linear transformations could also lead to a statistical bias, favoring a model that does not represent the experimental behavior. Similar observations are discussed regarding the pseudo-second-order kinetic model. Linearization of Freundlich isotherms, pseudo-first-order kinetic models, and fixed-bed adsorption models through logarithms implies that attention must be taken on the logarithm limits by properly selecting the data range. Linearization also promotes the incorrect interpretation of models due to oversimplification. The linearized van’t Hoff equation would yield a reasonable fit with fewer experimental points than the non-linear regression, which requires more data to assure convergence. In this sense, there is convincing evidence that non-linear regression is a more robust and reliable tool for adsorption modeling.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
吸附模型线性化的关键概述:方法和统计上的不一致
吸附方程的线性化是有争议的。拟合参数的估计很大程度上取决于线性化方法、实验误差的大小和数据范围。尽管许多研究将这些方程的线性版本与非线性版本进行了对比,但线性化是首选的,因为它简单,因为一条直线可以用比曲线更少的实验点来表示。对线性模型和非线性模型的精度进行了深入的分析比较。虽然不同的变换使朗缪尔等温线线性化,但只有一种形式产生可靠的拟合参数。线性变换也可能导致统计偏差,倾向于不代表实验行为的模型。对拟二阶动力学模型也讨论了类似的观察结果。通过对数对Freundlich等温线、拟一级动力学模型和固定床吸附模型进行线性化,表明必须注意对数极限,适当选择数据范围。线性化还会由于过度简化而导致对模型的不正确解释。与非线性回归相比,线性化的范霍夫方程在实验点较少的情况下会产生合理的拟合,而非线性回归需要更多的数据来保证收敛。从这个意义上说,有令人信服的证据表明,非线性回归是一种更强大、更可靠的吸附建模工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Latest Development of Matrix Solid Phase Dispersion Extraction and Microextraction for Natural Products from 2015-2021 Recent Advances in the Chemistry of Hydrometallurgical Methods Separation of Plutonium from Other Actinides and Fission Products in Ionic Liquid Medium Fixed Bed Adsorption of Water Contaminants: A Cautionary Guide to Simple Analytical Models and Modeling Misconceptions Application of Aqueous Biphasic Systems Extraction in Various Biomolecules Separation and Purification: Advancements Brought by Quaternary Systems
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1