TY - JOUR AU - Al-Hazam, Hanan A. PY - 2009/12/29 Y2 - 2024/03/29 TI - Prediction of Corrosion Inhibitor Efficiency of Some Aromatic Hydrazides and Schiff Bases Compounds by Using Artificial Neural Network JF - Journal of Scientific Research JA - J. Sci. Res. VL - 2 IS - 1 SE - Section B: Chemical and Biological Sciences DO - 10.3329/jsr.v2i1.2757 UR - https://banglajol.info/index.php/JSR/article/view/2757 SP - 108-113 AB - <p class="MsoNormal" style="margin: 0cm 15.6pt 0pt 14.2pt; direction: ltr; unicode-bidi: embed; text-align: justify; mso-outline-level: 1;"><span style="font-size: 9pt;" lang="EN-US"><span style="font-family: Times New Roman;">Artificial neural networks are used for evaluating the corrosion inhibitor efficiency of some aromatic hydrazides and<span style="color: #000000;"> Schiff ba</span>ses compounds. The nodes of neural network input layer represent the quantum parameters, total negative charge (TNC) on molecule, energy of highest occupied molecular orbital (<em style="mso-bidi-font-style: normal;">E</em> Homo), energy of lowest unoccupied molecular orbital (<em style="mso-bidi-font-style: normal;">E</em> Lomo), dipole moment (<em style="mso-bidi-font-style: normal;">&mu;</em>), total energy (TE), molecular volume (<em style="mso-bidi-font-style: normal;">V</em>), dipolar-polarizability factor (<em style="mso-bidi-font-style: normal;">&Pi;</em>) and inhibitor<span style="mso-spacerun: yes;">&nbsp; </span>concentration (<em style="mso-bidi-font-style: normal;">C</em>). The neural network output is the corrosion inhibitor efficiency (<em style="mso-bidi-font-style: normal;">E</em>) for the mentioned compounds. The training and testing of the developed network are based on a database of 31 published experimental tests obtained by weight loss. The neural network predictions for corrosion inhibitor efficiency are more reliable than prediction using other conventional theoretical methods such as AM<sub>1</sub>, PM<sub>3</sub>, Mindo, and Mindo-3.</span></span></p><p class="MsoNormal" style="text-justify: kashida; margin: 0cm 15.6pt 0pt 14.2pt; direction: ltr; unicode-bidi: embed; text-align: justify; text-kashida: 10%;"><span style="font-size: 6pt; mso-bidi-font-size: 9.0pt;" lang="EN-US"><span style="font-family: Times New Roman;">&nbsp;</span></span></p><p class="MsoNormal" style="margin: 0cm 15.6pt 0pt 14.2pt; direction: ltr; unicode-bidi: embed; text-align: left;"><span style="font-family: Times New Roman;"><em style="mso-bidi-font-style: normal;"><span style="font-size: 9pt; mso-bidi-font-weight: bold;" lang="EN-US">Key words</span></em><span style="font-size: 9pt;" lang="EN-US">: Neural network; Corrosion inhibitor efficiency.</span></span></p><p class="MsoNormal" style="margin: 0cm 15.6pt 0pt 14.2pt; direction: ltr; unicode-bidi: embed; text-align: left;"><span style="font-size: 6pt; mso-bidi-font-size: 9.0pt;" lang="EN-US"><span style="font-family: Times New Roman;">&nbsp;</span></span></p><p class="MsoNormal" style="margin: 0cm 14.2pt 0pt; text-align: left; mso-layout-grid-align: none;" dir="rtl" align="right"><span style="font-size: 8pt; mso-ansi-language: EN-SG; mso-fareast-language: EN-SG;" dir="ltr"><span style="font-family: Times New Roman;">&copy; 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved</span></span></p><p class="MsoNormal" style="margin: 0cm 14.2pt 0pt; text-align: left; tab-stops: 14.2pt 6.0cm 177.2pt 184.3pt;" dir="rtl" align="right"><span style="font-family: Times New Roman;"><span style="font-size: 8pt; mso-ansi-language: EN-SG; mso-fareast-language: EN-SG;" dir="ltr">DOI: 10.3329/jsr.v2i1.2757<span style="mso-spacerun: yes;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; </span><span style="mso-spacerun: yes;">&nbsp;&nbsp;&nbsp;&nbsp;</span><span style="mso-spacerun: yes;">&nbsp;&nbsp;</span><span style="mso-spacerun: yes;">&nbsp;</span></span><span style="font-size: 8pt;" dir="ltr" lang="EN-US">J. Sci. Res. <strong style="mso-bidi-font-weight: normal;">2</strong> (1), 108-113 <span style="mso-spacerun: yes;">&nbsp;</span>(2010)</span></span></p><p>&nbsp;</p> ER -