Stochastic modelling of perfect inspection and repair actions for leak-failure prone internal corroded pipelines

To enhance the performance of any facility, reduce cost and failure probability involves proper inspection and repair decisions. To be able to establish the cost of repair and inspection of corroded pipelines at different stages of the corrosion defect depth growth, Markov modelling technique was adopted. This model formulated an inspection and repair technique, which has the potentials of aiding policy makers in maintenance management of internally corroded pipelines. The transition states were determined using the Remaining Useful Life (RUL) of the pipelines whilst Weibull distribution was used for calculating the corrosion wastage rates at the lifecycle transition phases. Monte Carlo simulation and degradation models were applied for determining future corrosion defect depth growth, in a bid to establish periodic inspection and repair procedures and their costs. Data from an onshore pipeline inspected with Magnetic Flux Leakage (MFL) in-Line-Inspection (ILI) technique was used to test the validity of the model. The results obtained indicate that the model has practical applications for inspection and repairs of aged-internally corroded pipelines

On risk-based maintenance: A comprehensive review of three approaches to track the impact of consequence modelling for predicting maintenance actions

Since gas plants are progressively increasing near urban areas, a comprehensive tool to plan maintenance and reduce the risk arising from their operations is required. To this end, a comparison of three Risk-Based Maintenance methodologies able to point out maintenance priorities for the most critical components, is presented in this paper. Moreover, while the literature is mostly focused on probabilistic analysis, a particular attention is directed towards consequence analysis throughout this study. The first developed technique is characterized by a Hierarchical Bayesian Network to perform the occurrence analysis and a Failure Modes, Effects and Criticality Analysis to assess the magnitude of the adverse outcomes. The second approach is a Quantitative Risk Analysis carried out via a software named Safeti. Finally, another software called Synergi Plant is adopted for the third methodology, which provides a Risk-Based Inspection plan, through a semiquantitative risk analysis. The proposed study can assist asset manager in adopting the most appropriate methodology to their context, while highlighting priority components. To demonstrate the applicability of the approaches and compare their rankings, a Natural Gas Regulating and Measuring Station is considered as case study. The results showed that the most suited method strongly depends on the available data

Bayesian Analyses of Metal-Loss Corrosion on Energy Pipeline Based on Inspection Data

Bayesian models are developed to calibrate the accuracies of high-resolution in-line inspection (ILI) tools for sizing metal-loss corrosion defects and to characterize the growth of individual defects on energy pipelines. Moreover, a methodology is proposed to evaluate the time-dependent system reliability of a segment of a pressurized pipeline containing multiple active corrosion defects. The calibration of ILI tools is carried out by comparing the field-measured depths and ILI-reported depths for a set of static defects. The proposed methodology is able to quantify the measurement errors of multiple ILI tools simultaneously and evaluate the correlation between random errors associated with different ILI tools. The corrosion growth model is developed in a hierarchical Bayesian framework. The depth of the corrosion defects is assumed to be a power-law function of time characterized by two power-law coefficients and the corrosion initiation time, and the probabilistic characteristics of the parameters involved in the growth model are evaluated using Markov Chain Monte Carlo (MCMC) simulation technique based on ILI data collected at different times for a given pipeline. The model accounts for the constant and non-constant biases and random scattering errors of the ILI data, as well as the potential correlation between the random scattering errors associated with different ILI tools. Both the conventional Monte Carlo simulation and MCMC simulation techniques are employed in the methodology to evaluate the failure probability of the pipeline. The methodology considers three distinctive failure modes, namely small leak, large leak and rupture, and incorporates the hierarchical Bayesian power-law growth model for the depth of individual corrosion defect

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

PREDICTING THE RISK OF PIPELINE FAILURES AFTER HURRICANES

Over 30,000 hazardous material pipeline failures in the United States have been documented by the Pipeline and Hazardous Material Safety Administration (PHMSA) since 1970. Hazardous pipeline failures, particularly failures associated with synoptic-scale extreme weather events like tropical cyclones, cause massive damage to the social, environmental, and economic landscapes. Yet even though tropical storms are broadly recognized as important drivers of pipeline failures, limited research has been conducted associating tropical storm characteristics with the likelihood of pipeline failure. This is largely due to limitations in historical records of pipeline failures, which are based on operator-generated incidence reports. As a substantial fraction of the hazardous material pipeline infrastructure is located in tropical storm/hurricane-prone regions, understanding how tropical cyclone (TC) characteristics impact pipelines is of critical importance, both now and as the intensity and frequency of tropical storms/hurricanes increase due to climate change. This analysis focuses on quantifying the relationship between tropical storm/hurricane characteristics and pipeline failure frequency. To accomplish this, PHMSA Failure Dataset and NOAA HURDAT2 Dataset are associated based on spatiotemporal concomitance to estimate the frequency of failure of pipelines in the aftermath of a tropical storm/hurricane. Over 70% of reported pipeline failures in TC active regions occur within the first year of TC exposure, and 17% occur within two months (60 days) of TC exposure. Since 1975, the annual frequency of pipeline failures within 60 days of TC exposure has more than doubled. The frequency of hazardous pipeline failures directly relates to the intensity (minimum pressure/maximum windspeed) of the tropical cyclone. Tropical storm/hurricane intensity explains 33% of inter-system variability in pipeline failure. Assuming linear continuations of strong increasing trends in mean tropical cyclone intensity, associations between storm strength and tropical cyclone intensity suggest that we may see an 5% increase in the frequency of annual hazardous pipeline failures in TC track regions per year by 2050. The results of this study can guide inspection and monitoring practices and create more responsive emergency response plans to reduce the potential contamination after a failure occurs. Limitations to the current PHMSA failure reporting data collection practices for pipeline failure cause attribution are discussed

Risk-based life cycle assessment methodology for green and safe product selection

Life-cycle assessment (LCA) is an effective technique widely used to estimate the emissions produced during the entire life-cycle of a fuel or a product. However, most of the conventional LCA methods consider the risk of voluntary releases such as emissions, discharges or energy use. In other words, involuntary risks such as accident risks associated with exploration, production, storage, process and transportation have been overlooked. For hazardous materials involuntary risks could be significant; thus, ignoring this may result in imprecise LCA. The present study aims to develop a methodology for risk-based life-cycle assessment (RBLCA) of fossil fuels by integrating both the voluntary and involuntary risks (risk associated with potential accidents) of hazardous materials. The risk associated with potential accidents is estimated using Bayesian network approach. This provides a robust probabilistic platform of LCA. The application of the developed methodology is demonstrated for liquefied natural gas (LNG) and heavy fuel oil (HFO) as fuels of a hypothetical power plant. The comparative analysis of two fuels based on RBLCA helps an analyst not only overcome data uncertainty but also identify holistically green and safer fuel option

Applications of Bayesian networks and Petri nets in safety, reliability, and risk assessments: A review

YesSystem safety, reliability and risk analysis are important tasks that are performed throughout the system lifecycle to ensure the dependability of safety-critical systems. Probabilistic risk assessment (PRA) approaches are comprehensive, structured and logical methods widely used for this purpose. PRA approaches include, but not limited to, Fault Tree Analysis (FTA), Failure Mode and Effects Analysis (FMEA), and Event Tree Analysis (ETA). Growing complexity of modern systems and their capability of behaving dynamically make it challenging for classical PRA techniques to analyse such systems accurately. For a comprehensive and accurate analysis of complex systems, different characteristics such as functional dependencies among components, temporal behaviour of systems, multiple failure modes/states for components/systems, and uncertainty in system behaviour and failure data are needed to be considered. Unfortunately, classical approaches are not capable of accounting for these aspects. Bayesian networks (BNs) have gained popularity in risk assessment applications due to their flexible structure and capability of incorporating most of the above mentioned aspects during analysis. Furthermore, BNs have the ability to perform diagnostic analysis. Petri Nets are another formal graphical and mathematical tool capable of modelling and analysing dynamic behaviour of systems. They are also increasingly used for system safety, reliability and risk evaluation. This paper presents a review of the applications of Bayesian networks and Petri nets in system safety, reliability and risk assessments. The review highlights the potential usefulness of the BN and PN based approaches over other classical approaches, and relative strengths and weaknesses in different practical application scenarios.This work was funded by the DEIS H2020 project (Grant Agreement 732242)

Cascading events triggering industrial accidents: quantitative assessment of NaTech and domino scenarios

The so called cascading events, which lead to high-impact low-frequency scenarios are rising concern worldwide. A chain of events result in a major industrial accident with dreadful (and often unpredicted) consequences. Cascading events can be the result of the realization of an external threat, like a terrorist attack a natural disaster or of “domino effect”. During domino events the escalation of a primary accident is driven by the propagation of the primary event to nearby units, causing an overall increment of the accident severity and an increment of the risk associated to an industrial installation. Also natural disasters, like intense flooding, hurricanes, earthquake and lightning are found capable to enhance the risk of an industrial area, triggering loss of containment of hazardous materials and in major accidents. The scientific community usually refers to those accidents as “NaTechs”: natural events triggering industrial accidents. In this document, a state of the art of available approaches to the modelling, assessment, prevention and management of domino and NaTech events is described. On the other hand, the relevant work carried out during past studies still needs to be consolidated and completed, in order to be applicable in a real industrial framework. New methodologies, developed during my research activity, aimed at the quantitative assessment of domino and NaTech accidents are presented. The tools and methods provided within this very study had the aim to assist the progress toward a consolidated and universal methodology for the assessment and prevention of cascading events, contributing to enhance safety and sustainability of the chemical and process industry

Risk based integrity modeling for the optimal maintenance strategies of offshore process components

Ageing of components is a major threat to asset integrity in offshore process facilities. A robust maintenance strategy mitigates the effects of age-based structural degradations and reduces the threat of failure. Failure caused by structural degradations is a stochastic process. For maintenance strategies to be effective, the stochastic nature of failure has to be taken into consideration. Risk based integrity modeling (RBIM) is a newly-developed approach that aims at the protection of human life, financial investment, and the environment against the consequences of failure. RBIM quantifies the risk to which individual components are subjected and uses this as a basis for the design of a maintenance strategy. Risk is a combination of the probability and the consequence of failure. The major age-based structural degradations to be addressed include corrosion; such as uniform, pitting and erosion mechanisms; and cracking; such as stress corrosion, corrosion fatigue, and hydrogen induced cracking. In this study, component degradation processes are modeled stochastically to estimate the probability of failure using Bayesian analysis methods. Bayesian analysis improves the fidelity on the likelihood of future events by relating with the prior and posterior probabilities. Prior modeling is performed using judgmental studies and analyzing historic databases from similar installations. For the assessment of ageing assets and degradation mechanisms, field non-destructive test (NDT) data is used to establish the likelihood function. The posterior modeling is performed using a simulation-based Metropolis-Hastings algorithm and Laplace approximation since the prior-likelihood combinations are non-conjugate pairs. In this study, the consequences of failure are modeled using economic analysis to estimate the costs of failure, inspection and maintenance. The cost of failure includes lost production, loss of shutdown, cost of spill cleanup, loss caused by environmental damage and liability. The inspection and maintenance costs are estimated using the inspection and maintenance tasks, access, surface preparation, gauging defects, coating and restoration costs. Maintenance may be either minimal repair or replacement of components. The annual equivalent cost (AEC) of operation and maintaining a facility is the summation of the annual equivalent costs of failure, inspection, and maintenance. The cumulative posterior failure probability is combined with AEC to produce the operational life risk curve for a component. Since the risk curve is a convex function of the maintenance interval, then the optimum interval is the global minimum point. The operational risk is thus reduced to as low as reasonably practicable level by optimal maintenance

Fire Risk Assessment: A Systematic Review of the Methodology and Functional Areas

Fire is a physical and social phenomenon that affects both individuals and the environment. Fire risk assessment is a critical part of a fire prevention program. In this process, the fire risk associated with the possibility of occurrence and severity of damage resulting from the fire is estimated and calculated. In this paper, a classification scheme and a systematic literature review are presented in order to classify and interpret the current researches on fire risk assessment methodologies and applications. Based on the scheme, 93 scholarly papers from 13 journals are categorized into application areas and other categories. The application areas include the papers on the topics of environmental impact, production and industry, transportation, buildings, power industry, oil and gas industry, urban fires and other topics. Scholarly papers are also classified by (1) year of publication, (2) journal of publication, (3) year of publication and application areas and (4) authors’ nationality. The survey results show that the largest number of papers was published during the period 2010-2012 with 31 (33.33%), the most of the studies have been carried out on environmental impact (47.31%), the journal of Forest Ecology and Management had the highest percentage of articles with 26.88%. It is hoped that the paper can meet the needs of researchers for easy references of fire risk assessment methodologies and applications. Therefore, this work would be able to provide useful insights into the anatomy of the fire-risk assessment methods, and suggest academic researchers and experts a framework for future attempts and researches