VOL. 11, NO. 20, OCTOBER 2016 ISSN 1819-6608 ARPN Journal of Engineering and Applied Sciences © 2006-2016 Asian Research Publishing Network (ARPN). All rights reserved. www.arpnjournals.com 11966 AN EXPERIMENTAL ANALYSIS USING TAGUCHI METHOD IN RESOLVING THE SIGNIFICANT FACTORS SUBJECT TO CORROSION UNDER INSULATION Nurul Rawaida Ain Burhani, Masdi Muhammad, Mokhtar Che Ismail and Masri Asmi Mahed Department of Mechanical Engineering, Universiti Teknologi Petronas, Bandar Sri Iskandar, Perak, Malaysia E-Mail: nurulrawaidaain@gmail.com ABSTRACT Corrosion under insulation (CUI) is a gradually vital issue for piping in industries especially petrochemical and chemical plants due to its astonishing catastrophic disaster and automatic impact on the environmental problem. To ensure this CUI problem does not spark sudden surprise in plants, indeterminate factors that contribute to the deterioration of CUI should be recognized and taken care seriously. Thus, this research will unearth the most influential factors for the CUI deterioration using Taguchi method for design of the experiment. Result analysed using a signal to noise ratio revealed that most significant factor for CUI occurrence is insulation type followed by service temperature. However, this method also exposes that interactions between factors for CUI are less significant. Meanwhile, the most influential factor for service temperature is 120oC, type of insulation are perlite and calcium silicate while cycle type is isothermal wet/dry. This will help as an acceptable guideline for inspection planning purpose and priority in the maintenance schedule. Keywords: taguchi method, corrosion under insulation, ASTM G189-07, signal to noise ratio. INTRODUCTION Corrosion under insulation (CUI) is localized corrosion attacking the interface of metal between the metal surface and its insulation. Insulation is frequently applied to maintain process temperatures that reduce energy loss and associated costs including precaution for safety issues. CUI is typically difficult to perceive until it becomes a serious problem, especially in petrochemical or chemical plants that have been operating for decades [1]. These failures can be catastrophic in the environment or, at least, have undesirable economic effect during downtime and restoration. In 2003, Exxon Mobile Chemical indicated the highest incidence of leaks in the refining and chemical industries is due to CUI [1]. The piping maintenance costs are concerning 40% to 60% for CUI detection and cure for CUI occurrence. Afterward in 2008, National Association of Corrosion Engineers (NACE) fulfils a survey, out of 30 facilities, 17 experiences CUI as the main challenge they have to encounter. Furthermore, NACE Corrosion Costs Study in 2011 states that corrosion costs in the US are approaching $1 trillion annually, and expected to exceed that unfortunate milestone in future [2-5]. To ensure this CUI problem does not spark sudden surprise in plants, factors that contribute to the deterioration of CUI should be recognized and taken care of seriously. APPLICATION OF TAGUCHI METHOD Taguchi method Taguchi method is robust engineering. This method aims to develop outcomes that worked distinctly in spite of natural variation. Traditionally, Taguchi method proposes two-level, three-level, and mixed-level fractional factorial designs in orthogonal arrays. However, the approach of signal and noise factors, inner and outer arrays, and signal to noise ratios make this method unique [6, 7]. This study will only focus on signal to noise ratio. Signal to noise ratio (S/N ratio) Signal factors are system control inputs known as an inner array, while outer array, which is made up of noise factors are variables that are habitually difficult or expensive to manipulate. A signal-to-noise ratio is a statistic function calculated over an entire outer array. Its formula contingent on whether the investigational goal is to minimize, maximize or match a target assessment of the quality characteristic of interest [8, 9]. This study will emphasize on maximizing the CUI rate using Equation. (1) where n is some observations, and y is the observed data as used in [9, 10]. S/N = - 10 log 1/n (Σy 2 ) (1) This S/N ratio for CUI rate formula is the performance statistic computed to maximize the resulted value, which is –10 times the common logarithm of the average squared reciprocal. The mathematical expression of this methodology can be retrieved in [8]. ALTERNATIVE ANALYSIS To calculate either CUI parameters are significant or not to CUI rate, and the value lay in the acceptable range, Pareto statistical analysis of variance (Pareto ANOVA) analysis is used. This method is commonly used to analyse data for process optimization [9,10]. Pareto ANOVA is an abridged ANOVA technique which uses Pareto principles and requires neither ANOVA table nor the F-tests. It is an easy way to analyse results of parameter design and reliable application for industrial practitioners and engineers. Particular in this Pareto ANOVA analysis, Sum of Squared Differences (SSD) formula is used as in Equation. (2) [9].