# Salina Binti Ramli. I/C No: Dr. Norhazilan Bin Md. Noor

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1 Author: Salina Binti Ramli I/C No: Matric No: MA Year: 2008 Type of Degree: Master of Engineering (Civil-Structure) Supervisor: Dr. Norhazilan Bin Md. Noor Panel of Examiners: 1) Assoc. Prof. Dr. Mohamed Abdelkader El-Gelani Ismail 2) Prof. Ir. Dr. Mohd Warid Bin Hussin 3) Dr. Ahmad Kueh Beng Hong

2 CORROSION GROWTH PREDICTION IN SEAWATER BALLAST TANK OF BULK CARRIERS USING STATISTICAL MODEL SALINA BINTI RAMLI A project report submitted in partial fulfillment of the requirements for the award of degree of Master of Engineering (Civil-Structure) Faculty of Civil Engineering Universiti Teknologi Malaysia April 2008

3 I declare that this project report entitled Corrosion Growth Prediction In Seawater Ballast Tank Of Bulk Carriers Using Statistical Model is the result of my own research except as cited in references. This project has not been accepted for any degree and is not concurrently submitted in candidature of any other degree

4 Teristimewa untuk Ibu dan bapa tersayang; Pn. Aminah Binti Pandak Soud dan En. Ramli Bin Kassim, tunang yang banyak memberi sokongan; En. Nazrul Aizad Bin Harun serta adik-adik yang dikasihi; Mohd Shahril, Saliza, Syahirah dan Syafiqa.

6 ABSTRACT Corrosion is the major cause of deterioration in marine structures. For the past few years have seen an increase in the number of reported instances of accelerated corrosion in ship s ballast tank. This is why careful attention needs to be taken to prevent the deterioration due to corrosion of ballast tank. Therefore, corrosion growth prediction is important in failure analysis. This paper developed a statistical time dependent model for corrosion depth of seawater ballast tank in bulk carriers. The model is based on available group of statistical data for corrosion of existed bulk carriers. The proposed model is benefit for future prediction of corrosion data by eliminating the dependent factors such as environment factor, material properties and operational condition. In specific, a simple simulation procedure is implemented to predict the future distribution of corrosion depth. Based on the result in simulation stage, the result were synthesized and analyzed for validity of proposed model by comparing the actual data with predicted data. The result shows that the model is reliable and practical in predicting the distribution of corrosion depth. From the study, the proposed model seems to be more flexible comparing to the available analysis method and hopefully will facilitate the engineer in the future prediction of ship failure in marine structures.

8 TABLE OF CONTENT CHAPTER TITLE PAGE PROJECT STATUS APPROVAL SUPERVISOR APPROVAL TITLE DECLARATION DEDICATION APPRECIATION ABSTRACT ABSTRAK TABLE OF CONTENT LIST OF TABLE LIST OF FIGURE LIST OF SYMBOL LIST OF APPENDIX i ii iii iv v vi vii xi xiii xvi xvii 1 INTRODUCTION 1.1 Corrosion of Marine Structures Problem Statement Objectives and Aim of Research Scope of Study Expected Findings and Importance of Research Conclusion 5

9 2 LITERATURE REVIEW 2.1 Corrosion Definition Corrosion Mechanism Corrosion in Marine Structure Corrosion by sea water Marine Corrosion Mechanism Corrosion Failure Corrosion Failure Analysis Form of Corrosion Uniform Corrosion Localized corrosion Galvanic Corrosion Environmental Cracking Factor Cause Corrosion Metal Use in Marine Engineering Corrosion Prevention Painting Cathodic and Anodic Protection Metal Coating for Corrosion Protection Material and Design to Avoid Corrosion Seawater Ballast Tank Introduction Corrosion in Ballast Tank Coating Breakdown Mechanism in Ballast Tank Tank Inspection Marine Corrosion Model Literature Review of Previous Research Papers Empirical Model Kelly, Et. All. (2007) Mathematical Model Paik, Et. All (2003) Paik, Kim and Lee (1997) Shengping Q, Et. All (2002) 51

10 3 METHODOLOGY 3.1 Introduction Research Activities Literature Review Data Analysis Data Collection Analysis and Interpretation of Data Histogram Development Model Verification using Chi Square Test Statistical modeling of corrosion models Simulation and Prediction Corrosion Depth Prediction Using Inverse 66 Transformation Method Verifying the Validity of Proposed Model 67 Based on Simulation Results Analysis of Model Using Root Mean Square Error 67 Method (RMSE) 4 ANALYSIS AND RESULT 4.1 Introduction Linear Regression Model Probability Time-Dependent Model Chi-Square Test Simulation Inverse Transformation Method Result Analysis Graphical Comparison Root Mean Square Error Method (RMSE) 92 5 DISCUSSION AND RECOMMENDATION 5.1 Overview Data Collection Data Analysis 99

11 5.4 Simulation and Prediction Recommendation CONCLUSION 107 REFERENCES 109 APPENDIX 112

12 LIST OF TABLES TABLE NO. TITLE PAGE 2.1 Galvanic series of in flowing seawater Gathered number of measured data set of thickness loss 48 due to corrosion in seawater ballast tank of bulk carriers (Paik and Thayambali, 2003). 2.3 Summary of computed results for mean and the COV of 49 Annualized corrosion rate of bulk carrier s seawater ballast Tank (Paik and Thayambali, 2003) 3.1 Gathered number of measured data set of thickness loss due 57 to corrosion in seawater ballast tank of bulk carriers. 4.1 Corrosion distribution data in seawater ballast tank of bulk 69 Carriers 4.2 Chi square test (average depth = ) Chi square test (average depth = ) Chi square test (average depth = ) Comparison for group of vessel s age (1 st trial) Comparison for group of vessel s age (2 nd trial) Comparison for group of vessel s age (3 rd trial) Comparison for group of vessel s age (1 st trial) Comparison for group of vessel s age (2 nd trial) Comparison for group of vessel s age (3 rd trial) Comparison for group of vessel s age (1 st trial) Comparison for group of vessel s age (2 nd trial) Comparison for group of vessel s age (3 rd trial) 88

13 4.14 Comparison for group of vessel s age (1 st trial) Comparison for group of vessel s age (2 nd trial) Comparison for group of vessel s age (3 rd trial) Comparison for group of vessel s age (1 st trial) Comparison for group of vessel s age (2 nd trial) Comparison for group of vessel s age (3 rd trial) RMSE value for each class of vessel s age The best simulation result based on RMSE Collected data Mean depth and standard deviation for each vessel s age 100

14 LIST OF FIGURES FIGURE NO. TITLE PAGE 1.1 Organization of study Schematic representation of current flow in simple 9 Corrosion cell 2.2 Regions where the metal will freely corrode Crevice corrosion mechanism Corrosion of metal in marine environment Electrochemical cell Uniform corrosion Pitting corrosion Crevice corrosion Filiform corrosion on welded tank Worm-like filiform corrosion Galvanic corrosion Effect of cathode-to-anode (C/A) ratio on galvanic 25 Corrosion 2.13 Stress corrosion cracking Rusting due to stress corrosion cracking Cathodic protection electrochemical technique Cross section of vessel with single ballast tank at the bottom Electrolyte process of corrosion Deep pitting in ballast tank Unprotected ballast tank Protected ballast tank 39

15 2.21 Sketch of coating barrier degradation during the vessel s ballast 41 tank life 2.22 Type of marine corrosion model The corrosion depth versus the ship age from thickness 49 measurements of seawater ballast tank structures (Paik and Thayambali, 2003) 2.24 The 95 percentile and above band for developing the severe 50 (upper bound) corrosion wastage model (Paik and Thayambali, 2003) 2.25 Comparison of annualized corrosion rate formulations, 50 together with the measured corrosion data for seawater ballast tank (Paik and Thayambali, 2003) 2.26 Melchers conceptual model for marine corrosion Flow chart of study Plot of Exponential probability density function Cumulative distribution function of Exponential distribution Plot of Weibull probability density function Weibull cumulative distribution function Linear regression analysis of mean depth and vessel s age Linear regression analysis of standard deviation and 71 vessel s age 4.3 Specific histogram of ship s age years Specific histogram of ship s age years Specific histogram of ship s age years Specific histogram of ship s age years Specific histogram of ship s age years Histogram of all data Exponential probability plot Histogram of comparison for simulated and actual data 83 for group age years (1 st trial) 4.11 Histogram of comparison for simulated and actual data 83 for group age years (2 nd trial) 4.12 Histogram of comparison for simulated and actual data 84 for group age years (3 rd trial)

16 4.13 Histogram of comparison for simulated and actual data 85 for group age years (1 st trial) 4.14 Histogram of comparison for simulated and actual data 85 for group age years (2 nd trial) 4.15 Histogram of comparison for simulated and actual data 86 for group age years (3 rd trial) 4.16 Histogram of comparison for simulated and actual data 87 for group age years (1 st trial) 4.17 Histogram of comparison for simulated and actual data 87 for group age years (2 nd trial) 4.18 Histogram of comparison for simulated and actual data 88 for group age years (3 rd trial) 4.19 Histogram of comparison for simulated and actual data 89 for group age years (1 st trial) 4.20 Histogram of comparison for simulated and actual data 89 for group age years (2 nd trial) 4.21 Histogram of comparison for simulated and actual data 90 for group age years (3 rd trial) 4.22 Histogram of comparison for simulated and actual data 91 for group age years (1 st trial) 4.23 Histogram of comparison for simulated and actual data 91 for group age years (2 nd trial) 4.24 Histogram of comparison for simulated and actual data 92 for group age years (3 rd trial) 4.25 Graph RMSE vs Vessel s Age 94

17 LIST OF SYMBOLS λ = Exponential parameter also known as failure rate µ = mean β = shape parameter δ = location parameter d ave = linear regression model of defect depth average d g = degree of freedom. E = expected frequency φ = standard normal probability f = probability density function F = cumulative distribution function k n = number of classes. μ x = mean value N = class size O = observed frequency θ = scale parameter R 2 = coefficient of determination σ 2 = variance std d = linear regression model of defect depth standard deviation σ x = standard deviation t v = age of vessel u = random variables generated x o = an offset, which is assumed to be known a priori (the smallest value) Y u = upper limit of selected class

18 LIST OF APPENDIX APPENDIX TITLE PAGE NO A Chi-Square Statistical Table 112 B Simulation Data Example 117 C Graphical Comparison Between Actual 155 D Exponential Model of Corrosion Growth 175 Data and Simulated Data

19 1 CHAPTER 1 INTRODUCTION 1.1 Corrosion of Marine Structures Corrosion is the major cause of deterioration in marine structures. The past few years have been seen an increase in number of reported instances of accelerated corrosion in ship s ballast tank (Cleland, 1994). Vessels are often made of steel and therefore these structures are too exposed to suffer various types of damage as they get older. In many forms of corrosion, pitting or grooving is forms of corrosion major involved to marine corrosion especially for ballast tank of oil tanker and bulk carrier. This localized corrosion may have serious consequences in ship s structures. It can cause severe cracking or extend wider to produce general structure deterioration. Such failure may associate with higher cost of maintenance including lost lives in some cases.

20 2 The area of ballast tank that most exposed to corrosion are wing part of ballast tank which situated between the holds and the shipsides. Ballast tank is the outer hull, which create the external shape of ships. Usually, the inner water ballast tank surface area is very large. To make sure the quality of ballast tank, it needs a careful attention. (Singh, 1990). Corrosion has to be avoided. It is due to the complex structure of it with frequent wetting and drying in highly corrosive salt water environment. 1.2 Problem Statement Ballast tank is one of vessel s parts that highly subjected to corrosion. Maintenance of ballast tank to prevent corrosion involves very high cost, therefore only relevant types and level of maintenances should be done to avoid excessive cost. To predict corrosion growth in ballast tank in the future, it is necessary to have a relevant estimate of the corrosion rate. An inspection to measure corrosion depths in a number of vessels ballast tank of various ages has been made. Data gathered from the inspection will be used to develop a statistical time-dependent corrosion model. This model will provide the statistical characteristic such as mean, variance, distribution of corrosion rate as a function of time ship age, making it possible to predict the corrosion rate of ballast tank of any age. Statistical and probability analysis have been suggested by previous researchers to achieve better understanding and predict the exact depth of corrosion growth. Even though this method can gives better interpretation of corrosion growth, wide application is still uncommon. The statistical model is the best method

21 3 to improve the complex parameter. Thus, this study is motivated by several problems existed in the corrosion failure analysis as listed in the following: a) Difficulty for plant engineer to understanding the available analysis method because of the complexity of corrosion empirical models and statistic technique. b) Errorneous data of corrosion for seawater ballast tank c) Lack of inspection data d) Corrosion models are generally developed based on experimental work at laboratory and will not gives a practical and exact simulation to the real event on site. 1.3 Objectives and Aim The aim of this study is to develop a probability time-dependent model of corrosion depth in bulk carrier s ballast tank. To achieve the aim, the following objectives have been listed which are: a) To identify the distribution of real inspection data using statistical approach. b) To develop a model for representing the growth of corrosion depth using probability method. c) To predict the future corrosion distribution using Inverse Transformation Simulation procedure. d) To compare the actual data with the predicted data generated using the proposed model.

22 4 1.4 Scope of Study This study will be focusing on the corrosion depth of bulk carrier s seawater ballast tank. The data used to develop a statistical model are the real data that gathered during the site inspection by ship s owner. The types of corrosion that have been considered are marine corrosion. Method that are going to be use in development of statistical model is linear/theoretical method which not consider the environment parameter. The development of statistical model in this research will enable the prediction of corrosion depths at any point of time (ship s age). Then Inverse Transformation Method which is one of element in Monte Carlo simulation will be used to assess and predict the future corrosion depth at any age of vessels ballast tank. The prediction of future distribution of corrosion depth is based on the proposed time-dependent growth model. 1.5 Expected Finding and Importance of Research The expected finding from this study is a time-dependent growth model of marine corrosion in vessels sea water ballast tank. This corrosion model will be reliable in predicting the corrosion rate of seawater ballast tank of bulk carriers at any point of time. The model is specifically tailored to be simple yet practical for on site assessment of structure remaining life-time upon corrosion attack. The proposed method is a provisional tool in predicting the future growth of corrosion defect until more data can become available. Hence, enable the use of more advance and intricate model in corrosion modeling.

23 5 1.6 Conclusion Figure 1.1 shows the work flow of the study. This study begins with an introduction to corrosion of marine structures, followed by a discussion on problem statement, aims, objectives and scope of study. The introduction is followed by Chapter 2, covering reviews of all topics related to corrosion and previous studies. Then, the research methodology is presented in Chapter 3. Chapters 4 covers the statistical and probability analysis of corrosion data from seawater ballast tank of bulk carriers. The steps in development of proposed model were discussed in detail in Chapter 4 together with the result of simulation then, followed by Chapter 5 with discussion and recommendation to improve the proposed model. The study ends in Chapter 6 conclusion to the study. Introduction Corrosion of Problem Aim and Scope of Expected marine structure statement objectives study findings Literature Review Corrosion background Corrosion Model Previous research work Ballast tank Research Methodology Continue to the next page

24 6 Analysis and Result Data collection Statistical and probability analysis Results Discussion and Recommendation Data Statistical analysis Results Conclusion Appendix Chi-Square Statistical Table Simulation Data Example Figure 1.1: Organization of study

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