This research reports the results of literature data of mass loss tests of high temperature corrosion inhibition of steel in different concentration ratios of MgO, Al2O3 and SiO2 to corrosive fuel ash of V2O5 in the temperature range of 550-590°C and time range of 8-100 h. Analysis focused on determining optimum mathematical equation and artificial neural network (ANN) architecture in order to gain good prediction properties. Three mathematical equations and five ANN architectures were suggested. A computer aided program was used for developing these models. Results show that polynomial mathematical equation and multilayer perceptron are able to accurately predict selected data with high correlation coefficients.
LaiG. Y.: ‘High-temperature corrosion and materials applications’2007Materials Park, OHASM International.
2.
GaoW. and LiZ.: ‘Nano-structured alloy and composite coatings for high temperature applications’Mater. Res., 20047175–182. doi: 10.1590/S1516-14392004000100023
3.
KarlssonS.ÅmandL. -E. and LiskeJ.: ‘Reducing high-temperature corrosion on high-alloyed stainless steel superheaters by co-combustion of municipal sewage sludge in a fluidised bed boiler’Fuel2015139482–493. doi: 10.1016/j.fuel.2014.09.007
4.
KhadomA. A.YaroA. S.AltaieA. S. and KadhumA. H.: ‘Mathematical modeling of corrosion inhibition behavior of low carbon steel in HCl acid’J. Appl. Sci., 200992457–2462. doi: 10.3923/jas.2009.2457.2462
5.
KhadomA. A.: ‘Modeling of corrosion reaction data in inhibited acid environment, using regressions and artificial neural networks, Korean’J. Chem. Eng., 2013302197–2204.
6.
DuaV.: ‘An artificial neural network approximation based decomposition approach for parameter estimation of system of ordinary differential equations’Comput. Chem. Eng., 201135545–553. doi: 10.1016/j.compchemeng.2010.06.005
7.
RashidiA. M.: ‘A galvanostatic modeling for preparation of electrodeposited nanocrystalline coatings by control of current density’J. Mater. Sci. Technol., 2012281071–1076. doi: 10.1016/S1005-0302(12)60175-3
8.
MoralH.AksoyA. and GokcayC. F.: ‘Modeling of the activated sludge process by using artificial neural networks with automated architecture screening’Comput. Chem. Eng., 2008322471–2478. doi: 10.1016/j.compchemeng.2008.01.008
9.
FahmiI. and CremaschiS.: ‘Process synthesis of biodiesel production plant using artificial neural networks as the surrogate models’Comput. Chem. Eng., 201246105–123. doi: 10.1016/j.compchemeng.2012.06.006
10.
AhmadA. L.AzidI. A.YusofA. R. and SeetharamuK. N.: ‘Emission control in palm oil mills using artificial neural network and genetic algorithm’Comput. Chem. Eng., 2004282709–2715. doi: 10.1016/j.compchemeng.2004.07.034
11.
EisaM. Y.: ‘The inhibition of synthetic fuel oil ash under high temperature corrosion’. MSc thesis, Department of Chemical Engineering, College of Engineering, University of Baghdad, Iraq, 2000.
12.
KhadomA. A.YaroA. S.KadumA. A. H.AlTaieA. S. and MusaA. Y.: ‘The effect of temperature and acid concentration on corrosion of low carbon steel in hydrochloric acid media’Am. J. Appl. Sci., 200961403–1409. doi: 10.3844/ajassp.2009.1403.1409
13.
MathurP. B. and VasudevanT.: ‘Reaction Rate Studies for the Corrosion of Metals in Acids-I, Iron in Mineral Acids’Corrosion1982383. doi: 10.5006/1.3579270
14.
BirbilisN.CavanaughM. K.SudholzA. D.ZhuS. M.EastonM. A. and GibsonM. A.: ‘A combined neural network and mechanistic approach for the prediction of corrosion rate and yield strength of magnesium-rare earth alloys’Corros. Sci., 201153168. doi: 10.1016/j.corsci.2010.09.013
15.
PlattJ. A.: ‘A resource-allocating network for function interpolation’Neural Comput., 19913213–218. doi: 10.1162/neco.1991.3.2.213
16.
HaykinS.: ‘Neural networks: a comprehensive foundation’1994New YorkMacmillan Publishing.
17.
LazićZ. R.: ‘Design of experiments in chemical engineering’2004WeinheimWiley-VCH Verlag GmbH & Co. KGaA.
