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J Chin Soc Corr Pro  1999, Vol. 19 Issue (1): 33-38     DOI:
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STUDIES ON CORROSION INHIBITION OF ANILINE AND ITS DIRIVATIVES BY QUANTUM CHEMICAL CALCULATION AND ARTIFICIAL NEURAL NETWORKS
Fang Hu;;;
湖南大学化学化工学院
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Abstract  The approach of neural networks was made on its applications to the correlation between molecular electronic structrue and corrosion inhibition properties of aniline and its halogenated and methylated derivatives with substituents at different positions.It was found by reference [2] that there exists the relation between the inhibitive efficiency of the above compounds in acid media for mild steel and their electronic structures;and a tentative interpretation was given for the phenomenon that when the inhibitiors are substituted with the same kind of substituents at different positions in the molecule,or with different substituents at same position,they exhibit different inhibitive effciency.The mechanism of corrosion inhibition was also wxplained from the point of view of electron transfer and the information provided in that paper could be probably useful for developing new more effective inhibitors.The present authors believe that the molecules of the inhibitors were possibly adsorbed on the surface of iron electrode in horizontal state.The inputs of neural networks included the molecular structure parameters,such as hydrophobic index (π,π-),molar refraction (MR),steric parameter (Es),electronic index (σ,σ*),as well as net charge and πcharge of N atom (qN),and low-unoccupied molecular orbital(LUMO),which could be calculated by means of semi-empirical methods of quantum chemisty,including HMO and CNDO/2 methods.The neural network's outputs were the corrosion-inhibiting efficiencies of aniline and its halogenated and methylated derivatives for mild steel in acid media.The corresponding relationship and modelling prediction were established by using neural networks with 8-15-1 topological structures trained by a modified backpropagation (MBP) algorithm,respectively.The learning precision was high and the predicting performance was excellent.The corrosion inhibiting properties or electrode parameters of some new aniline's derivatives were predicted quantitatively and the results were quite good.
Key words:  neural networks      molecular electronic structure      aniline and its derivatives      lnhibition efficiency      
Received:  15 July 2005     
Corresponding Authors:  Fang Hu   

Cite this article: 

Fang Hu. STUDIES ON CORROSION INHIBITION OF ANILINE AND ITS DIRIVATIVES BY QUANTUM CHEMICAL CALCULATION AND ARTIFICIAL NEURAL NETWORKS. J Chin Soc Corr Pro, 1999, 19(1): 33-38 .

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https://www.jcscp.org/EN/     OR     https://www.jcscp.org/EN/Y1999/V19/I1/33

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[2] HU Fang QU Ding-rong SHI Xian-ming LI Zhi-liang(College of Chemical Engineering; Institute of Molecular Science; Hunan University;Changsha 410082)(Institute of Chemistry; Chinese Academy of Sciences; Beijing 100080) (College of Chemical Engineering; Chong. NEURAL NETWORK STUDY ON CORRELATION BETWEENELECTRONIC STRUCTURE AND CORROSION INHIBITIONPROPERTIES OF ISOQUINOLINE AND ITS DERIVATIVES[J]. 中国腐蚀与防护学报, 1998, 18(4): 283-288.
[3] CAI Jianping KE Wei (Institute of Corrosion and Protection of Metals; Chinese Academy of Sciences). APPLICATION OF NEURAL NETWORKS TO ATMOSPHERIC CORROSION OF CARBON STEEL AND LOW ALLOY STEELS[J]. 中国腐蚀与防护学报, 1997, 17(4): 303-306.
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