AIRCC's International Journal of Computer Science and Information Technology

Stephen Dapiap ¹, Gregory Wajiga ², Michael Egwurube ², Musa Kadzai ², Nathaniel Oye ², ThankGod Anazodo ¹

Affiliations
1 Department of Strategic Information, Institute of Human Virology, NG
2 Federal University of Technology, Yola, NG

Abstract

In this work we have developed a decision support system that can determine corrosion and project the time for corrosion growth maintenance using probabilistic modelling approach. Historical data of the atmospheric industrial environment conditions for five years on three metals-zinc, iron and steel with known thicknesses were used. The environmental conditions included precipitation, wind speed, sulphur dioxide, relative humidity and temperature. For the five-year period, the percentiles of corrosion in the environment were determined to be from 63.1% to 69%. The corrosion rates of Zinc, iron and steel were 0.92μm/year,0.9μm/year and 0.51μm/year respectively. At the end of the first year the expected time to initiate corrosion growth maintenance actions for zinc, iron and steel were 9 years, 10 years and 14 years in that order. The probable contributions of each of the environmental factors to the corrosiveness of the environment were determined.

Keywords

Data Mining, Corrosion, Corrosion Control, Decision Support System, Probabilistic Modelling, Environmental Factors.

Full Text