naa a22 4500 510819923 CHVBK 20180411083526.0 cr unu---uuuuu 180411e20131201xx s 000 0 eng 10.1007/s11814-013-0170-0 doi (NATIONALLICENCE)springer-10.1007/s11814-013-0170-0 Khadom Anees Department of Chemical Engineering, College of Engineering, Diyala University, 32001, Baquba City, Diyala Governorate, Iraq aut Modeling of corrosion reaction data in inhibited acid environment using regressions and artificial neural networks [Elektronische Daten] [Anees Khadom] This paper reports the results of mass loss measurements in the corrosion inhibition of mild steel in different concentrations of H3PO4 in the temperature range 30-60 °C using potassium iodide as an inhibitor. The present work is focused on determining the optimum mathematical equation and the ANN architecture in order to gain good prediction properties. Three mathematical equations and three ANN architectures are suggested. Computer aided program was used for developing these models. The results show that the polynomial mathematical equation and multi-layer perception are able to accurately predict the measured data with high correlation coefficients. Korean Institute of Chemical Engineers, Seoul, Korea, 2013 Corrosion nationallicence Computer Program nationallicence Neural Network nationallicence Mild Steel nationallicence Acid nationallicence Regression nationallicence Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/12(2013-12-01), 2197-2204 0256-1115 30:12<2197 2013 30 11814 https://doi.org/10.1007/s11814-013-0170-0 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11814-013-0170-0 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Khadom Anees Department of Chemical Engineering, College of Engineering, Diyala University, 32001, Baquba City, Diyala Governorate, Iraq aut NATIONALLICENCE 773 0- Korean Journal of Chemical Engineering Springer US; http://www.springer-ny.com 30/12(2013-12-01), 2197-2204 0256-1115 30:12<2197 2013 30 11814 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer