Prediction of standard chemical exergy by a three descriptors QSPR model

Farhad Gharagheizi; Mehdi Mehrpooya

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Prediction of standard chemical exergy by a three descriptors QSPR model

机译：通过三个描述符QSPR模型预测标准化学能值

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摘要

A new quantitative structure-property relationship (QSPR) three parameter correlation (R~2 = 0.9977) of standard chemical exergy for a diverse set of 134 organic substances was developed by application of a genetic algorithm search. The descriptors are all calculated directly from the molecular structure, and the approach given is applicable, in principle, to all organic substances of regular structure. The application of the genetic algorithm in comparison with stepwise multi-variate linear regression (MLR) shows some advantages in required time for solving and in precision.

机译：应用遗传算法搜索，为134种有机物的标准化学能级建立了新的定量结构-性质关系（QSPR）三参数相关性（R〜2 = 0.9977）。所有的描述符都是直接从分子结构中计算出来的，给出的方法原则上适用于所有规则结构的有机物质。与逐步多元线性回归（MLR）相比，遗传算法的应用在求解所需的时间和精度方面显示出一些优势。

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来源
《Energy Conversion & Management》 |2007年第9期|2453-2460|共8页
作者
Farhad Gharagheizi; Mehdi Mehrpooya;
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作者单位

Department of Chemical Engineering, Faculty of Engineering, University of Tehran, P.O. Box 11365-4563, Tehran, Iran;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类能源与动力工程;
关键词
chemical exergy; QSPR; genetic algorithm;

机译：化学本能QSPR;遗传算法;

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5. Integrating Urban Recharge Uncertainty into Standard Groundwater Modeling Practice: A Case Study on Water Main Break Predictions for the Barton Springs Segment of the Edwards Aquifer, Austin, Texas. [D] . Sinner, Kate M.L. 2017

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6. Correction: Vatani A. et al. Prediction of Standard Enthalpy of Formation by a QSPR Model. Int. J. Mol. Sci. 2007 8 407–432. [O] . Ali Vatani, Mehdi Mehrpooya, Farhad Gharagheizi 2009

机译：校正：VataniA.等。用QSPR模型预测标准形成焓。诠释J.摩尔科学20078407–432。
7. Correction: Vatani, A., et al. Prediction of Standard Enthalpy of Formation by a QSPR Model. Int. J. Mol. Sci. 2007, 8, 407–432. [O] . Vatani, Ali, Mehrpooya, Mehdi, Gharagheizi, Farhad 2009

机译：校正：Vatani，A.等。用QSPR模型预测标准形成焓。诠释J.摩尔科学2007，8，407–432。
8. Tutorial on Neural Networks Using the Broyden-Fletcher-Goldfarb - Shanno (BFGS) Training Algorithm and Molecular Descriptors with Application to the Prediction of Dielectric Constants through the Development of Quantitative Structure Property Relationships (QSPRs) [R] . Schweitzer, R. C. , Morris, J. B. 2000

机译：使用Broyden-Fletcher-Goldfarb-shanno（BFGs）训练算法和分子描述符的神经网络教程应用于通过定量结构性质关系（QspR）的发展预测介电常数

Prediction of standard chemical exergy by a three descriptors QSPR model

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