钢铁表面常见药物缓蚀效率的预测模型:各种方法的合理比较

IF 7.5 Q1 CHEMISTRY, PHYSICAL
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引用次数: 0

摘要

随着时间的推移,人们提出了几种防腐处理方法,以形成保护层来阻止腐蚀现象。近年来,从植物提取物和过期药物中提取的有机分子因其潜在的缓蚀特性而受到测试。然而,直接评估缓蚀效率(IE%)需要昂贵的反应物和特定的实验装置。定量-结构-活性关系(QSAR)建议根据以往实验中测量的变量或理论方法确定的变量来建立 IE% 模型。计算出的描述符(如电离能 (I)、电子亲和力 (A) 或整体硬度)被添加到理化性质数据库中。这项工作对几种方法进行了比较,以获得预测缓蚀效率的精确而便携的数学模型。作为该研究小组的独创方法,使用正交最小二乘法前向回归(FROLS)的外生输入非线性自回归移动平均(NARMAX)模型被作为一种稳健的方法来获得非线性可移植模型,并确定影响 IE% 的最重要变量。与此形成鲜明对比的是,普通最小二乘法(OLS)采用了一种新方法,即在仅有一个自变量的情况下,将正交最小二乘法(FROLS)变量的幂级数展开应用于线性回归和多项式回归,从而使趋势图更加清晰可视,并便于根据原始信息提出经验法则。最后,IBM Watson 作为传统数学方法的一种稳健但非便携且高度参数化的替代方法,基于额外树回归器(ETR)进行了比较。使用平均绝对百分比误差 (MAPE)、均方误差 (MSE) 和均方根误差 (RMSE) 对模型进行了比较。总体而言,变量较少且最多为二阶项的模型性能有所改善。通过二阶 NARX 对 630 种物质的推断,分析了 IE% 的主要趋势。此外,还报告了最高占据分子轨道能量的决定性作用。实验人员可以利用一种 "无成本 "的通用方法,尤其是二阶 NARX 模型,获得误差约为 6% 的 IE% 估计值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Models for predicting corrosion inhibition efficiency of common drugs on steel surfaces: A rationalized comparison among methodologies

Several anticorrosive treatments have been proposed over time to create protective layers to hinder the corrosion phenomenon. In recent years, organic molecules from plant extracts and expired drugs have been tested due to their potential corrosion inhibition properties. However, direct corrosion inhibition efficiency (IE%) evaluation requires costly reactants and a specific experimental setup. Quantitative-structure activity relationship (QSAR) proposes modeling IE% in terms of variables measured in previous experiments or determined by theoretical approaches. Computed descriptors, such as ionization energy (I), electronic affinity (A), or global hardness, were added to a database of physicochemical properties. This work compares several methodologies to obtain precise yet portable mathematical models for predicting corrosion inhibition efficiency. As an original approach from this research group, nonlinear autoregressive moving average with exogenous inputs (NARMAX), using forward regression with orthogonal least squares (FROLS), models were implemented as a robust method to get nonlinear portable models and to determine the most important variables impacting IE%. Contrastingly, ordinary least squares (OLS) methodology was employed with the novelty of applying power series expansions from the promoted FROLS variables for linear and polynomial regression with only one independent variable, which resulted in clearer graph visualization of trends and the ease of proposing thumb rules based on raw information. Finally, IBM Watson was also compared as a robust yet non-portable and highly parametrized alternative to conventional mathematical approaches, based on extra trees regressor (ETR). The models were compared using mean absolute percentage error (MAPE), mean-squared error (MSE), and root-mean-squared error (RMSE). Overall, models with fewer variables and up to second-order terms show improved performance. The main tendencies of IE%, drawn by inferences for 630 substances by second-order NARX, are analyzed. Also, the determinant role of the highest occupied molecular orbital energy was reported. Experimentalists can take advantage of a “cost-free” general approach that can obtain estimations for IE% values with errors of about 6 %, in particular the second-order NARX model.

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1.60%
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128
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