Optimization Study Based on Genetic Algorithms and Neural Networks Applied on an Ammidation Reaction

被引：0

作者：

Mocanu, Anca Mihaela ^{[1
]}

Cernatescu, Corina ^{[1
]}

Diaconescu, Rodica ^{[2
]}

机构：

[1] Gheorghe Asachi Tech Univ Iasi, Fac Chem Engn & Environm Protect, Dept Organ Biochem & Food Engn, Iasi 700050, Romania

[2] Gheorghe Asachi Tech Univ Iasi, Fac Chem Engn & Environm Protect, Dept Chem Engn, Iasi 700050, Romania

来源：

REVISTA DE CHIMIE | 2012年 / 63卷 / 11期

关键词：

neural network; genetic algorithms; direct modeling; inverse modeling; optimization; ammidation reaction; HETEROCYCLIC-COMPOUNDS; 1,2,4-TRIAZOLE; DERIVATIVES; 1,3,4-THIADIAZOLE; SIMULATION;

D O I：

暂无

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

In order to cumulate the pharmacological effects of the phenoxyacetic derivatives with the antifungal activity of the 1,2,4-triazoles, we thought that an ammidation reaction will be required so that the original compounds will include both moieties, fact that will probably increase the global biological effect of these new synthesized products. The aim of this paper is to present a methodology to predict the optimum for the ammidation reaction and the reaction parameters that are required on this purpose. The obtained results confirm that for the studied ammidation reaction the maximum yield is 90.9% and it is attained for a molar ratio sulfochloridel triazole =2 and reaction time 1.5 h.

引用

页码：1138 / 1142

页数：5

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