Probabilistic Modeling and Bayesian Inference of Metal-Loss Corrosion with Application in Reliability Analysis for Energy Pipelines

The stochastic process-based models are developed to characterize the generation and growth of metal-loss corrosion defects on oil and gas steel pipelines. The generation of corrosion defects over time is characterized by the non-homogenous Poisson process, and the growth of depths of individual defects is modeled by the non-homogenous gamma process (NHGP). The defect generation and growth models are formulated in a hierarchical Bayesian framework, whereby the parameters of the models are evaluated from the in-line inspection (ILI) data through the Bayesian updating by accounting for the probability of detection (POD) and measurement errors associated with the ILI data. The Markov Chain Monte Carlo (MCMC) simulation in conjunction with the data augmentation (DA) technique is employed to carry out the Bayesian updating. Numerical examples that involve both the simulated and actual ILI data are used to validate the proposed Bayesian formulation and illustrate the application of the methodology. A simple Monte Carlo simulation-based methodology is further developed to evaluate the time-dependent system reliability of corroding pipelines in terms of three distinctive failure modes, namely small leak, large leak and rupture, by incorporating the corrosion models evaluated from the Bayesian updating methodology. An example that involves three sets of ILI data for a pipe joint in a natural gas pipeline located in Alberta is used to illustrate the proposed methodology. The results of the reliability analysis indicate that ignoring generation of new defects in the reliability analysis leads to underestimations of the probabilities of small leak, large leak and rupture. The generation of new defects has the largest impact on the probability of small leak

Development of Probabilistic Corrosion Growth Models with Applications in Integrity Management of Pipelines

Metal-loss corrosion is a major threat to the structural integrity and safe operation of underground oil and gas pipelines worldwide. The reliability-based corrosion management program has been increasingly used in the pipeline industry, which typically includes three tasks, namely periodic high-resolution inline inspections (ILIs) to detect and size corrosion defects on a given pipeline, engineering critical assessment of the corrosion defects reported by the inspection tool and mitigation of defects. This study addresses the core involved in the reliability-based corrosion management program. First, the stochastic process in conjunction with the hierarchical Bayesian methodology is used to characterize the growth of defect depth using imperfect ILI data. The biases, random scattering errors as well as the correlations between the random scattering errors associated with the ILI tools are accounted for in the Bayesian inference. The Markov Chain Monte Carlo (MCMC) simulation techniques are employed to carry out the Bayesian updating and numerically evaluate the posterior distributions of the parameters in the growth model. Second, a simulation-based methodology is presented to evaluate the time-dependent system reliability of pressurized energy pipelines containing multiple active metal-loss corrosion defects using the developed growth models. Lastly, a probabilistic investigation is carried out to determine the optimal inspection interval for the newly-built onshore underground natural gas pipelines with respect to external metal-loss corrosion by considering the generation of corrosion defects over time and time-dependent growth of individual defects. The proposed methodology will facilitate the reliability-based corrosion management for corroding pipelines

Corrosion Prediction Model Of Corroded Pipeline Using Gumbel

Corrosion is an important degradation mechanism that can affect the reliability and integrity of the pipeline. Offshore pipelines are usually inspected using MFL Intelligent Pigging method; this is how internal pipeline corrosion can be definitively measured. However, a huge amount of thickness profile data was not used optimally to predict the corrosion rate. A reliable corrosion rate model is paramount to determine the re-inspection time interval and corrosion mitigation for pipelines. The objective of this final year project is to predict and analyze the internal pipeline corrosion for the chosen case study and develop a corrosion model. The methodology used in this project includes data gathering, data review, classification into defect type, data analysis, corrosion modelling, validation and discussion. The IP data was modelled with Gumbel distribution and result show that the data fits the curve and predicted the time to failure was for another 60 years. The result from Gumbel was compared to the deterministic approach of average time to failure of 149 years. The percent error was 40%. The project met the objective and can be further developed

수소 생산 및 수송시스템의 안전한 디자인 및 관리 설계

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 화학생물공학부(에너지환경 화학융합기술전공), 2020. 8. 이원보.International demand for hydrogen is increasing. In particular, after the spread of electric vehicles, hydrogen has been connected not only with chemical plants, but also with peoples living life. In this paper, the safe design of a hydrogen refueling station for electronic vehicle and the prediction of the corrosion damage of a pipe defect for the safe management of a hydrogen underground buried pipe is studied. First, the safe design of a hydrogen refueling station targets a process that produces hydrogen from natural gas-derived material, which is known to be the most economical. This is a comparison of three processes: the first is to load hydrogen produced from the outside of the station carried by a high-pressure trailer, and then transform its pressure to meet the demand. The second is to produce hydrogen from gaseous NG(natural gas) through steam reforming reaction, and the last is the process of producing hydrogen by steam reforming reaction through LPG. All three processes is found to exceed tolerable risk levels in areas with some population density under currently known process conditions. Therefore, it is possible to safely design the process by changing the conditions of the process units that most affect the risk to mitigate the risk, or lower the frequency of failure event occurring by constructing additional firewalls. On the other hand, off-site pipelines placed to transport the produced hydrogen going out of the hydrogen station or the incoming hydrogen from the outside are mainly installed in a buried form. Buried piping is an inevitable structure for the utilization of the ground area, but it is difficult to check the condition frequently due to the limitations of drilling costs and human resources to directly check the condition of the piping. Therefore, more attention should be paid to safety management. In particular, buried piping accidents in areas close to the population, such as Kaohsiung in Taiwan or San Bruno in the United States, can cause personal injury, so evaluate and predict whether the risk or reliability of piping is safe and secure in the future. It is important to do. There have been many studies predicting the defect depth distribution of pipes due to external corrosion. Predictive modeling of the previous papers were well predicted defect depths measured in the soil environments. However, the external corrosion of piping is affected by various environmental factors, so a well-made model may be inaccurate in other environments. This is because a large amount of data is required and it is generally difficult to apply to changing soils. To overcome this, the Adaptive Bayesian methodology is needed. Predicting Defect Depth well can be said to have established a model for how quickly the defect depth is growing. Defect Depth Growth rate model, that is, prediction model for External Corrosion rate, has also been studied. Like Defect Depth, since it is affected by various environmental variables, the Adaptive model is effective for general prediction. Therefore, through this, it was possible to study a more accurate prediction model of the defect depth for the safe design of the hydrogen filling station and the reliability measurement of the pipe that transports hydrogen to the outside of the filling station. It is a demand for a more careful and safe design for the hydrogen charging station in the vicinity of a person, and it is expected that through the above study, a safe hydrogen storage will be installed and managed.수소에 대한 국제 수요가 점차 증가하고 있다. 특히 전기자동차의 보급 이후, 수소는 화학플랜트에서 뿐만 아니라, 도시에서 있는 사람들의 생활권과도 맞닿아 있다. 이 논문에서는 전기자동차에 수소를 공급받기 위한 수소충전소의 안전한 설계와 해당 수소충전소의 외부로, 혹은 외부에서 수소가 이송될 경우 이용하게 될 수소 지하매설배관의 안전한 관리를 위한 배관결함의 손상도 예측을 연구하였다. 먼저 수소충전소에 대한 안전한 설계는 수소를 가장 경제적으로 생산할 수 있다고 알려진 천연가스로부터 생산하는 공정을 대상으로 한다. 이는 3가지 공정을 비교할 수 있는데, 첫번째는 외부에서 생산된 수소를 고압 트레일러로 싣고 온 후, 수요에 맞게 변압하는 공정이고, 두번째는 기체상태의 NG에서 수소를 Steam Reforming Reaction으로 생산하는 공정, 마지막으로 LPG에서 수소를 Steam Reforming하여 생산하는 공정이다. 세 공정 모두 현재 알려진 공정 조건에서는 인구밀도가 어느 정도 있는 지역에서 모두 Tolerable한 위험도 수준을 넘어서는 것으로 나타났다. 따라서 위험도에 가장 많이 영향을 주는 공정 유닛들의 조건들을 조금씩 바꿔가며 위험도를 낮추는 공정 수정을 하여 안전한 공정설계를 할 수 있다. 한편, 수소충전소 외부로 나가는 생산된 수소, 혹은 외부에서 들어오는 수소를 이송하기 위해 놓여지는Off-site 파이프라인들은 주로 매설된 형태로 설치가 된다. 매설배관은 지상면적의 활용을 위해 필연적인 구조물이지만, 배관 상태를 직접 확인하기 위한 굴착비용 및 인적 자원의 한계 등으로 자주 상태를 확인하기 힘들다. 따라서 안전관리에 더욱 유의하여야 한다. 특히 대만의 가오슝(Kaohsiung)이나 미국의 산 브루노(San Bruno) 사고처럼 인구 밀접 지역에서의 매설배관사고는 인명피해를 유발할 수 있어, 현재 및 향후에 배관의 위험도나 신뢰도가 안전한 수준인지 평가하고 예측하는 것이 중요하다. 외부부식에 따른 배관의 Defect Depth 분포를 예측하는 연구들이 많이 있어왔다. 선행 논문들의 예측 모델링들은 해당 토양환경들에서 직접 측정한 Defect Depth들을 잘 예측한 모델들이었다. 하지만 배관의 외부부식은 여러가지의 환경요소에 영향을 받고, 따라서 잘 만들어진 모델도 다른 환경에서는 부정확할 수 있다. 대량의 데이터가 필요하고, 변화하는 토양에 일반적으로 적용하기 힘들기 때문이다. 이를 극복하기 위해 Adaptive Bayesian 방법론이 필요하다. Defect Depth를 잘 예측한다는 것은 defect depth가 얼마나 빨리 성장하고 있는지에 대한 모델을 잘 세웠다고도 할 수 있다. Defect Depth Growth rate 모델, 즉 External Corrosion rate에 대한 예측모델 역시 많은 연구가 있어왔다. Defect Depth와 마찬가지로 여러 환경변수의 영향을 받으므로, 이 역시 일반적인 예측을 위해 Adaptive 모델이 효과적이었다. 따라서 이를 통해 수소 충전소의 안전한 설계 및 충전소 외부로 수소를 이송하는 배관의 신뢰도 측정을 위한 Defect Depth의 보다 정확한 예측모델을 연구할 수 있었다. 사람이 인접한 곳의 수소 충전소를 대상으로 하여, 더욱 신중하고, 안전한 설계가 요구되는 수요처이며, 위 연구를 통해 안전한 수소 저장소 설치 및 관리가 될 것을 기대한다.Chapter1. Introduction 1 1.1. Research motivation 1 Chapter2. Safe design for onsite hydrogen refueling station 5 2.1. Background 5 2.2. Process description 9 2.2.1. Hydrogen production process modeling 9 2.3. Quantitative risk assessment procedure 47 2.4. Layout of the hydrogen refueling station 50 2.5. Result and discussion 52 2.5.1. Risk assessment result before process modification 52 2.5.1. Proposed process modification for risk mitigation 70 2.6. Conclusion 74 Chapter3. Adaptive approach for estimation of pipeline corrosion defects via Bayesian inference 75 3.1. Introduction 75 3.2. Adaptive estimation of corrosion defect depth 81 3.2.1. Time-dependent GEV distribution for corrosion defect depth distribution 81 3.2.2. Adaptive estimation framework using Bayesian inference 84 3.3. Implementation 89 3.4. Visualization and discussion 93 3.4.1. Case 1 Direct inspection 93 3.4.1. Case 2 indirect inspection 96 3.4.1. Case 3 sudden changes in hidden depth distribution 100 3.5. Conclusion 108 Chapter4. Concluding remarks 110 Reference 112Docto

Pipeline Risk Assessment Using Dynamic Bayesian Network (DBN) for Internal Corrosion

Pipelines are the most efficient mode of transportation for various chemicals and are considered as safe, yet pipeline incidents remain occurring. Corrosion is one of the main reasons for incidents especially in subsea pipelines due to the harsh corrosive environment that prevails. Corrosion can be attributed to 36% amongst all the causes of subsea pipeline failure. Internal corrosion being an incoherent process, one can never forecast exact occurrences inside a pipeline resulting in highly unpredictable risk. Therefore, this paper focuses on risk assessment of internal corrosion in subsea pipelines. Corrosion is time-dependent phenomena, and conventional risk assessment tools have limited capabilities of quantifying risk in terms of time dependency. Hence, this paper presents a Dynamic Bayesian Network (DBN) model to assess and manage the risk of internal corrosion in subsea. DBN possesses certain advantages such as representation of temporal dependence between variable, ability to handle missing data, ability to deal with continuous data, time- based risk update, observation of the change of variables with time and better representation of cause and effect relationship. This model aims to find the cause of internal corrosion and predict the consequence in case of pipeline failure given the reliability of safety barrier in place at each time step. It also demonstrates the variation of corrosion promoting agents, corrosion rate and safety barriers with time

DEVELOPING HYBRID PHM MODELS FOR PIPELINE PITTING CORROSION, CONSIDERING DIFFERENT TYPES OF UNCERTAINTY AND CHANGES IN OPERATIONAL CONDITIONS

Pipelines are the most efficient and reliable way to transfer oil and gas in large quantities. Pipeline infrastructures represent a high capital investment and, if they fail, a source of environmental hazards and a potential threat to life. Among different pipeline failure mechanisms, pitting corrosion is of most concern because of the high growth rate of pits. In this dissertation two hybrid prognostics and health management (PHM) models are developed to evaluate degradation level of piggable pipelines, due to internal pitting corrosion. These models are able to incorporate multiple sensors data and physics of failure (POF) knowledge of internal pitting corrosion process. This dissertation covers both cases when in some pipeline's segments the pit density is low and in some segments it is high. In addition, it takes into account four types of uncertainty, including epistemic uncertainty, variability in the temporal aspects, spatial heterogeneity, and inspection errors. For a pipeline segment with a low pit density, a hybrid defect-based algorithm is developed to estimate probability distribution of maximum depth of each individual pit on that segment. This algorithm considers change in operational condition in internal pitting corrosion degradation modeling for the first time. In this way a two-phase similarity-based data fusion algorithm is developed to fuse POF knowledge, in-line inspection (ILI) and online inspection (OLI) data. In the first phase, a hierarchical Bayesian method based on a non-homogeneous gamma process is used to fuse POF knowledge and in-line inspection (ILI) data on multiple pits, and augmented particle filtering is used to fuse POF knowledge and online inspection (OLI) data of an active reference pit. The results are used to define a similarity index between each ILI pit and the OLI pit. In the second phase, this similarity index is used to generate dummy observations of depth for each ILI pit, based on the inspection data of the OLI pit. Those dummy observations are used in augmented particle filtering to estimate the remaining useful life (RUL) of that segment after the change in operational conditions when there is no new ILI data. For a pipeline segment with a high pit density, a hybrid population-based algorithm is developed to estimate the probability density function of maximum depth of the pit population on that segment. This algorithm eliminates the need of matching procedure that is computationally expensive and prone to error when the pit density is high. In this algorithm three types of measurement uncertainty including sizing error, probability of detection (POD), and probability of false call (POFC) are taken into account. In addition, initiation of new pits between the last ILI and a prediction time is modeled by using a homogeneous Poisson process. The non-linearity of the pitting corrosion process and the POF knowledge of this process is modeled by using a non-homogeneous gamma process. The estimation of these two algorithms are used in a series system to estimate the reliability of a long pipeline with multiple segments, when in some segments the pit density is low and in some segments it is high. The output of this research can be used to find the optimal maintenance action and time for each segment and the optimal next ILI time for the whole pipeline that eventually decreases the cost of unpredicted failures and unnecessary maintenance activities

불확실성 하에서 시스템의 유지 보수 최적화 및 수명 주기 예측

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 화학생물공학부, 2019. 2. 이원보.The equipment and energy systems of most chemical plants have undergone repetitive physical and chemical changes and lead to equipment failure through aging process. Replacement and maintenance management at an appropriate point in time is an important issue in terms of safety, reliability and performance. However, it is difficult to find an optimal solution because there is a trade-off between maintenance cost and system performance. In many cases, operation companies follow expert opinions based on long-term industry experience or forced government policy. For cost-effective management, a quantitative state estimation method and management methodology of the target system is needed. Various monitoring technologies have been introduced from the field, and quantifiable methodologies have been introduced. This can be used to diagnose the current state and to predict the life span. It is useful for decision making of system management. This thesis propose a methodology for lifetime prediction and management optimization in energy storage system and underground piping environment. First part is about online state of health estimation algorithm for energy storage system. Lithium-ion batteries are widely used from portable electronics to auxiliary power supplies for vehicle and renewable power generation. In order for the battery to play a key role as an energy storage device, the state estimation, represented by state of charge and state of health, must be well established. Accurate rigorous dynamic models are essential for predicting the state-of health. There are various models from the first principle partial differential model to the equivalent circuit model for electrochemical phenomena of battery charge / discharge. It is important to simulate the battery dynamic behavior to estimate system state. However, there is a limitation on the calculation load, therefore an equivalent circuit model is widely used for state estimation. Author presents a state of health estimation algorithm for energy storage system. The proposed methodology is intended for state of health estimation under various operating conditions including changes in temperature, current and voltage. Using a recursive estimator, this method estimate the current battery state variable related to battery cell life. State of health estimation algorithm uses estimated capacity as a cell life-time indicator. Adaptive parameters are calibrated by a least sum square error estimation method based on nonlinear programming. The proposed state-of health estimation methodology is validated with cell experimental lithium ion battery pack data under typical operation schedules and demonstration site operating data. The presented results show that the proposed method is appropriate for state of health estimation under various conditions. The suitability of algorithm is demonstrated with on and off line monitoring of new and aged cells using cyclic degradation experiments. The results from diverse experimental data and data of demonstration sites show the appropriateness of the accuracy, robustness. Second part is structural reliability model for quantification about underground pipeline risk. Since the long term usage and irregular inspection activities about detection of corrosion defect, catastrophic accidents have been increasing in underground pipelines. Underground pipeline network is a complex infrastructure system that has significant impact on the economic, environmental and social aspects of modern societies. Reliability based quantitative risk assessment model is useful for underground pipeline involving uncertainties. Firstly, main pipeline failure threats and failure modes are defined. External corrosion is time-dependent factor and equipment impact is time-independent factor. The limit state function for each failure cause is defined and the accident probability is calculated by Monte Carlo simulation. Simplified consequence model is used for quantification about expected failure cost. It is applied to an existing underground pipeline for several fluids in Ulsan industrial complex. This study would contribute to introduce quantitative results to prioritize pipeline management with relative risk comparisons Third part is maintenance optimization about aged underground pipeline system. In order to detect and respond to faults causing major accidents, high resolution devices such as ILI(Inline inspection), Hydrostatic Testing, and External Corrosion Direct Assessment(ECDA) can be used. The proposed method demonstrates the structural adequacy of a pipeline by making an explicit estimate of its reliability and comparing it to a specified reliability target. Structural reliability analysis is obtaining wider acceptance as a basis for evaluating pipeline integrity and these methods are ideally suited to managing metal corrosion damage as identified risk reduction strategies. The essence of this approach is to combine deterministic failure models with maintenance data and the pipeline attributes, experimental corrosion growth rate database, and the uncertainties inherent in this information. The calculated failure probability suggests the basis for informed decisions on which defects to repair, when to repair them and when to re-inspect or replace them. This work could contribute to state estimation and control of the lithium ion battery for the energy storage system. Also, maintenance optimization model helps pipeline decision-maker determine which integrity action is better option based on total cost and risk.화학공장 내 장치 및 에너지 시스템은 반복적인 사용으로 물리화학적 변화를 겪으며 노후화되고 설계 수명에 가까워지게 된다. 적절한 시점에 장비 교체와 보수 관리는 안전과 신뢰도, 전체 시스템 성능을 좌우하는 중요한 문제이다. 그러나, 보수 비용과 시스템 성능을 유지하는 것 사이에는 트레이드 오프가 존재하기 때문에 이에 대한 최적점을 찾는 것은 어려운 문제이다. 많은 경우에 운영회사에서는 경험에 기반한 전문가 의견을 따르거나, 정부차원의 안전관리 정책 최소 기준에 맞추어 진행한다. 비용효율적 관리를 위하여 정량적인 상태 추정 기법이나 유지보수 관리 방법론은 필요하다. 많은 모니터링 기술이 개발되어지고 있고 점차 실시간 측정 방법이나 센서 기술이 발달 하고 있다. 그러나, 여전히 직접 측정 및 검사 이전 장비의 수명 예측과 시스템 관리에 대한 의사결정을 도울 방법론은 부족한 실정이다. 본 논문에서는 리튬 이온 배터리의 수명예측 방법론과 지하매설배관의 관리 최적화 문제를 다룬다. 첫 장에서는 에너지 저장시스템 운전패턴에 적합한 배터리 SOH 추정 방법론에 대한 것이다. 리튬 이온 배터리는 이동가능 전자장치에서부터 자동차 및 신재생발전 등의 보조 전력 저장장치로서 활용이 이루어지고 있다. 배터리가 정상적인 역할을 하기 위하여 SOC와 SOH의 정확한 추정이 중요하다. 정확한 동적 모델은 SOH 예측을 위하여 필수적이다. BMS에는 계산 로드에 한계가 있기 때문에 상태 추정을 위하여 계산 부하가 비교적 적은 등가회로 모델이 사용된다. 본 논문에서는 SOH 예측 알고리즘을 제시하고, 셀 및 실증 사이트 데이터로 검증한다. 반복 예측기와 관측기 기법을 활용하여 SOH를 추정을 통하여 현재의 배터리 상태를 제시한다. SOH 예측 알고리즘은 용량을 중요 상태변수로 하여 예측된다. 제안 알고리즘에서는 SOH를 정확히 추정하기 위하여 확장칼만필터를 도입하여 배터리 상태변수들을 정확히 예측하고 이를 기반으로 SOH를 추정하는 알고리즘을 제안한다. 두번째 장은 구조 신뢰도 분석을 통하여 지하배관의 정량적 위험성 모델을 수립한다. 배관의 장기 사용과 불규칙한 검사/보수 활동에 대한 불확실성은 지하배관 안전 사고의 위험성을 증대시키는 요인이다. 산업단지 내의 지하배관 네트워크는 복잡한 인프라를 갖추고 있기 때문에 사고 발생시 경제적, 환경적, 사회적으로 큰 위협요소가 된다. 신뢰도 기반 정량적 위험도 모델은 지하배관의 큰 불확실성 요소를 고려하는데 유용한 방법론이다. 배관 사고 위협요인과 사고 모드를 정의하고, 부식과 타공사에 이르는 시간 의존적, 비의존적 요소를 고려하여 한계상태함수를 결정한다. 몬테카를로 시뮬레이션을 통하여 연간 사고확률이 유추되며 사고 영향거리 및 누출량 계산 모델과 합하여 정량적 위험성 분석을 할 수 있다. 배관에 존재하는 다양한 물질들에 대하여 케이스 스터디를 진행하여 정량화된 위험도에 근거하여 배관관리 우선순위를 정하는 의사결정에 반영할 수 있다. 세번째 장은 노후화된 배관 시스템의 관리 최적화에 대한 내용이다. 사고의 위험성을 미연에 방지하기 위하여 다양한 검사, 보수 방법론이 사용된다. 그러나, 이에 대한 효과가 위험성과 어떻게 관련되어서 나타나는지 알기 어렵다. 대부분 경험적으로 혹은 제도적인 방안을 통하여 보수적인 안전관리를 진행하는 한계성이 있다. 제안된 방법론을 토대로 하여 안전관리 방법에 대한 실제적인 부식 관리에 영향 정도를 정량화 한다. 신뢰도 목표와 제안 되어진 예산 등을 제한조건으로 하는 최적화를 실시하여 최적의 검사 주기, 최적의 검사 방법론을 확인한다. 위 연구를 토대로 개선된 리튬이온 배터리의 온라인 상태추정 알고리즘 제시하고 위험도 환산 비용을 결합한 구조 신뢰도 모델로 지하배관 관리 최적화 방법론을 제시한다.Abstract i Contents vi List of Figures ix List of Tables xii CHAPTER 1. Introduction 14 1.1. Research motivation 14 1.2. Research objectives 19 1.3. Outline of the thesis 20 CHAPTER 2. Lithium ion battery modeling and state of health Estimation 21 2.1. Background 21 2.2. Literature Review 22 2.2.1. Battery model 23 2.2.2. Qualitative comparative review of state of health estimation algorithm 29 2.3. Previous estimation algorithm 32 2.3.1. Nonlinear State estimation method 32 2.3.2. Sliding mode observer 35 2.3.3. Proposed Algorithm 37 2.3.4. Uncertainty Factors for SOH estimation in ESS 42 2.4. Data acquisition 44 2.4.1. Lithium ion battery specification 45 2.4.2. ESS Experimental setup 47 2.4.3. Sensitivity Analysis for Model Parameter 54 2.5. Result and Discussion 59 2.5.1. Estimation results of battery model 59 2.5.2. Estimation results of proposed method 63 2.6. Conclusion 68 CHAPTER 3. Reliability estimation modeling for quantitative risk assessment about underground pipeline 69 3.1. Introduction 69 3.2. Uncertainties in underground pipeline system 72 3.3. Probabilistic based Quantitative Risk Assessment Model 73 3.3.1. Structural Reliability Assessment 73 3.3.2. Failure mode 75 3.3.3. Limit state function and variables 79 3.3.4. Reliability Target 86 3.3.5. Failure frequency modeling 90 3.3.6. Consequence modeling 95 3.3.7. Simulation method 101 3.4. Case study 103 3.4.1. Statistical review of Industrial complex underground pipeline 103 3.5. Result and discussion 107 3.5.1. Estimation result of failure probability 107 3.5.1. Estimation result validation 118 CHAPTER 4. Maintenance optimization methodology for cost effective underground pipeline management 120 4.1. Introduction 120 4.2. Problem Definition 124 4.3. Maintenance scenario analysis modeling 126 4.3.1. Methodology description 128 4.3.2. Cost modeling 129 4.3.3. Maintenance mitigation model 132 4.4. Case study 136 4.5. Results 138 4.5.1. Result of optimal re-inspection period 138 4.5.2. Result of optimal maintenance actions 144 CHAPTER 5. Concluding Remarks 145 References 147Docto

Application of Bayesian Networks to Integrity Management of Energy Pipelines

Metal-loss corrosion and third-party damage (TPD) are the leading threats to the integrity of buried oil and natural gas pipelines. This thesis employs Bayesian networks (BNs) and non-parametric Bayesian networks (NPBNs) to deal with four issues with regard to the reliability-based management program of corrosion and TPD. The first study integrates the quantification of measurement errors of the ILI tools, corrosion growth modeling and reliability analysis in a single dynamic Bayesian network (DBN) model, and employs the parameter learning technique to learn the parameters of the DBN model from the ILI-reported and filed-measured corrosion depths. The second study develops the BN model to estimate the probability of a given pipeline being hit by third-party excavations by taking into account common preventative and protective measures. The parameter learning technique is employed to learn the parameters of the BN model from datasets that consist of individual cases of third-party activities. The ILIs are infeasible for a portion of buried pipelines due to various reasons, which are known as unpiggable pipelines. To assist with the corrosion assessment for the unpiggable pipelines, the third study develops a non-parametric Bayesian network (NPBN) model to predict the corrosion depth on buried pipelines using the pipeline age and local soil properties as the predictors. The last study develops an optimal sample size determination method for collecting samples to reduce the epistemic uncertainties in the probabilistic distributions of basic random variables in the reliability analysis of corroded pipelines

Risk-based evaluation of pitting corrosion in process facilities

Pitting is one of the most challenging forms of corrosion to study and model due to complex pit behavior. Pitting can occur in different engineering alloys and can lead to catastrophic consequences. Pits are usually latent or difficult-to-detect and resulting degradation often causes in-service failure of process equipment. Therefore, the ability to predict pit behavior is key to design and maintenance of assets. In particular, pitting corrosion is a significant challenge in marine environments and offshore operations due to remoteness of operations and hidden damage under insulations. Thus, the ability to assess risk and estimate remaining life of assets affected by pitting corrosion is necessary for timely maintenance and safe operation of assets. This thesis proposes a methodology to assess and dynamically update the risk of pressurized components affected by pitting corrosion. To take into consideration the time-dependent growth of pits, the application of non-homogenous Markov process is proposed to model the maximum pit depth. The integration of the developed maximum pit model into a pressureresistance model is proposed to predict the failure probability of affected components. An economic consequence analysis model is developed to estimate both business and accidental losses due to failure of the affected component. Then, risk is estimated by integrating models developed for probability of failure and associated consequences. The application of Bayesian analysis is proposed to update estimated risk as new inspection data gets available and also as economic condition of the process evolves. This work also proposes a risk management strategy including corrosion prevention, control and monitoring measures to make effective decision related to pitting corrosion. The application of the proposed methods is demonstrated using different case studies

Dynamic corrosion risk-based integrity assessment of marine and offshore systems

Corrosion poses a serious integrity threat to marine and offshore systems. This critical issue leads to high rate of offshore systems degradation, failure, and associated risks. The microbiologically influenced corrosion (microbial corrosion), which is a type of corrosion mechanism, presents inherent complexity due to interactions among influential factors and the bacteria. The stochastic nature of the vital operating parameters and the unstable microbial metabolism affect the prediction of microbial corrosion induced failure and the systems’ integrity management strategy. The unstable and dynamic characteristics of the corrosion induced risk factors need to be captured for a robust integrity management strategy for corroding marine and offshore systems. This thesis proposes dynamic methodology for risk-based integrity assessment of microbially influenced corroding marine and offshore systems. Firstly, a novel probabilistic network based structure is presented to capture the non-linear interactions among the monitoring operating parameters and the bacteria (e.g., sulfate-reducing bacteria) for the microbial corrosion rate predictions. A Markovian stochastic formulation is developed for the corroding offshore system failure probability prediction using the degradation rate as the transition intensity. The analysis results show that the non-linear interactions among the microbial corrosion influential parameters increase the corrosion rate and decrease the corroding system's failure time. Secondly, a dynamic model is introduced to evaluate the offshore system's operational safety under microbial corrosion induced multiple defect interactions. An effective Bayesian network - Markovian mixture structure is integrated with the Monte Carlo algorithm to forecast the effects of defects interactions and the corrosion response parameters’ variability on offshore system survivability under multispecies biofilm architecture. The results reveal the impact of defects interaction on the system's survivability profile under different operational scenarios and suggest the critical intervention time based on the corrosivity index to prevent total failure of the offshore system. Finally, a probabilistic investigation is carried out to determine the parametric interdependencies' effects on the corroding system reliability using a Copula-based Monte Carlo algorithm. The model simultaneously captures the failure modes and the non-linear correlation effects on the offshore system reliability under multispecies biofilm structure. The research outputs suggest a realistic reliability-based integrity management strategy that is consistent with industry best practices. Furthermore, a dynamic risk-based assessment framework is developed considering the evolving characteristics of the influential microbial corrosion factors. A novel dynamic Bayesian network structure is developed to capture the corrosion's evolving stochastic process and the importance of input parameters based on their temporal interrelationship. The associated loss scenarios due to microbial corrosion induced failures are modeled using a loss aggregation technique. A subsea pipeline is used to demonstrate the model performance. The proposed integrated model provides a risk-based prognostic tool to aid engineers and integrity managers for making effective safety and risk strategies. This work explores the microbial corrosion induced failure mechanisms and develops dynamic risk-based tools under different operational scenarios for systems’ integrity management in the marine and offshore oil and gas industries