Liquid Transport Pipeline Monitoring Architecture Based on State Estimators for Leak Detection and Location

This research presents the implementation of optimization algorithms to build auxiliary signals that can be injected as inputs into a pipeline in order to estimate —by using state observers—physical parameters such as the friction or the velocity of sound in the fluid. For the state estimator design, the parameters to be estimated are incorporated into the state vector of a Liénard-type model of a pipeline such that the observer is constructed from the augmented model. A prescribed observability degree of the augmented model is guaranteed by optimization algorithms by building an optimal input for the identification. The minimization of the input energy is used to define the optimality of the input, whereas the observability Gramian is used to verify the observability. Besides optimization algorithms, a novel method, based on a Liénard-type model, to diagnose single and sequential leaks in pipelines is proposed. In this case, the Liénard-type model that describes the fluid behavior in a pipeline is given only in terms of the flow rate. This method was conceived to be applied in pipelines solely instrumented with flowmeters or in conjunction with pressure sensors that are temporarily out of service. The design approach starts with the discretization of the Liénard-type model spatial domain into a prescribed number of sections. Such discretization is performed to obtain a lumped model capable of providing a solution (an internal flow rate) for every section. From this lumped model, a set of algebraic equations (known as residuals) are deduced as the difference between the internal discrete flows and the nominal flow (the mean of the flow rate calculated prior to the leak). The residual closest to zero will indicate the section where a leak is occurring. The main contribution of our method is that it only requires flow measurements at the pipeline ends, which leads to cost reductions. Some simulation-based tes

High-viscosity biphasic flow characterization in a pipeline: application to flow pattern classification and leak detection

Pipeline systems play an essential role in the oil industry. These systems connect ports, oil fields, refineries, and consumer markets[104]. Pipelines covering long distances require pumping stations, where products are propelled to the next pumping station, refinery, or deposit terminal, thus traveling through most of the country. The product considered in this research work is crude oil. It is usually transported with a combination of crude oil with viscosity reducers (DRA, drag reducer agent) and oil with gas in onshore/offshore pipelines. This mode of transport is efficient for large quantities and large product shipment distances. Problems may arrive when a leak occurs. In major incidents, large scale damage to humans and the environment is possible. Then, this research addresses the problem of how to detect the leak earlier to reduce the impact in the surrounding areas and economic losses, considering five research topics taking into account that the products inside the pipeline are water-glycerol and gas-glycerol mixtures (simulating oil-DRA and oil-gas in the laboratory test apparatus). The first research topic presents a mathematical model to describe the flow of a mixture of water and glycerol in pressurized horizontal pipelines, which emulates the mixture of heavy oil and a viscosity reducer. The model is based on the mass and momentum conservation principles and empirical correlations for the mixture’s density and viscosity. The set of partial differential equations is solved using finite differences. These equations were implemented in a computer platform to be able to simulate a system. This simulation platform is a tool to simulate leak cases for different fractions of water and glycerol to evaluate algorithms for leak detection and localization before their implementation in a laboratory setting.DoctoradoDoctor en Ingeniería Mecánic

Enabling Resilience in Cyber-Physical-Human Water Infrastructures

Rapid urbanization and growth in urban populations have forced community-scale infrastructures (e.g., water, power and natural gas distribution systems, and transportation networks) to operate at their limits. Aging (and failing) infrastructures around the world are becoming increasingly vulnerable to operational degradation, extreme weather, natural disasters and cyber attacks/failures. These trends have wide-ranging socioeconomic consequences and raise public safety concerns. In this thesis, we introduce the notion of cyber-physical-human infrastructures (CPHIs) - smart community-scale infrastructures that bridge technologies with physical infrastructures and people. CPHIs are highly dynamic stochastic systems characterized by complex physical models that exhibit regionwide variability and uncertainty under disruptions. Failures in these distributed settings tend to be difficult to predict and estimate, and expensive to repair. Real-time fault identification is crucial to ensure continuity of lifeline services to customers at adequate levels of quality. Emerging smart community technologies have the potential to transform our failing infrastructures into robust and resilient future CPHIs.In this thesis, we explore one such CPHI - community water infrastructures. Current urban water infrastructures, that are decades (sometimes over a 100 years) old, encompass diverse geophysical regimes. Water stress concerns include the scarcity of supply and an increase in demand due to urbanization. Deterioration and damage to the infrastructure can disrupt water service; contamination events can result in economic and public health consequences. Unfortunately, little investment has gone into modernizing this key lifeline.To enhance the resilience of water systems, we propose an integrated middleware framework for quick and accurate identification of failures in complex water networks that exhibit uncertain behavior. Our proposed approach integrates IoT-based sensing, domain-specific models and simulations with machine learning methods to identify failures (pipe breaks, contamination events). The composition of techniques results in cost-accuracy-latency tradeoffs in fault identification, inherent in CPHIs due to the constraints imposed by cyber components, physical mechanics and human operators. Three key resilience problems are addressed in this thesis; isolation of multiple faults under a small number of failures, state estimation of the water systems under extreme events such as earthquakes, and contaminant source identification in water networks using human-in-the-loop based sensing. By working with real world water agencies (WSSC, DC and LADWP, LA), we first develop an understanding of operations of water CPHI systems. We design and implement a sensor-simulation-data integration framework AquaSCALE, and apply it to localize multiple concurrent pipe failures. We use a mixture of infrastructure measurements (i.e., historical and live water pressure/flow), environmental data (i.e., weather) and human inputs (i.e., twitter feeds), combined and enhanced with the domain model and supervised learning techniques to locate multiple failures at fine levels of granularity (individual pipeline level) with detection time reduced by orders of magnitude (from hours/days to minutes). We next consider the resilience of water infrastructures under extreme events (i.e., earthquakes) - the challenge here is the lack of apriori knowledge and the increased number and severity of damages to infrastructures. We present a graphical model based approach for efficient online state estimation, where the offline graph factorization partitions a given network into disjoint subgraphs, and the belief propagation based inference is executed on-the-fly in a distributed manner on those subgraphs. Our proposed approach can isolate 80% broken pipes and 99% loss-of-service to end-users during an earthquake.Finally, we address issues of water quality - today this is a human-in-the-loop process where operators need to gather water samples for lab tests. We incorporate the necessary abstractions with event processing methods into a workflow, which iteratively selects and refines the set of potential failure points via human-driven grab sampling. Our approach utilizes Hidden Markov Model based representations for event inference, along with reinforcement learning methods for further refining event locations and reducing the cost of human efforts.The proposed techniques are integrated into a middleware architecture, which enables components to communicate/collaborate with one another. We validate our approaches through a prototype implementation with multiple real-world water networks, supply-demand patterns from water utilities and policies set by the U.S. EPA. While our focus here is on water infrastructures in a community, the developed end-to-end solution is applicable to other infrastructures and community services which operate in disruptive and resource-constrained environments

Adaptive swarm optimisation assisted surrogate model for pipeline leak detection and characterisation.

Pipelines are often subject to leakage due to ageing, corrosion and weld defects. It is difficult to avoid pipeline leakage as the sources of leaks are diverse. Various pipeline leakage detection methods, including fibre optic, pressure point analysis and numerical modelling, have been proposed during the last decades. One major issue of these methods is distinguishing the leak signal without giving false alarms. Considering that the data obtained by these traditional methods are digital in nature, the machine learning model has been adopted to improve the accuracy of pipeline leakage detection. However, most of these methods rely on a large training dataset for accurate training models. It is difficult to obtain experimental data for accurate model training. Some of the reasons include the huge cost of an experimental setup for data collection to cover all possible scenarios, poor accessibility to the remote pipeline, and labour-intensive experiments. Moreover, datasets constructed from data acquired in laboratory or field tests are usually imbalanced, as leakage data samples are generated from artificial leaks. Computational fluid dynamics (CFD) offers the benefits of providing detailed and accurate pipeline leakage modelling, which may be difficult to obtain experimentally or with the aid of analytical approach. However, CFD simulation is typically time-consuming and computationally expensive, limiting its pertinence in real-time applications. In order to alleviate the high computational cost of CFD modelling, this study proposed a novel data sampling optimisation algorithm, called Adaptive Particle Swarm Optimisation Assisted Surrogate Model (PSOASM), to systematically select simulation scenarios for simulation in an adaptive and optimised manner. The algorithm was designed to place a new sample in a poorly sampled region or regions in parameter space of parametrised leakage scenarios, which the uniform sampling methods may easily miss. This was achieved using two criteria: population density of the training dataset and model prediction fitness value. The model prediction fitness value was used to enhance the global exploration capability of the surrogate model, while the population density of training data samples is beneficial to the local accuracy of the surrogate model. The proposed PSOASM was compared with four conventional sequential sampling approaches and tested on six commonly used benchmark functions in the literature. Different machine learning algorithms are explored with the developed model. The effect of the initial sample size on surrogate model performance was evaluated. Next, pipeline leakage detection analysis - with much emphasis on a multiphase flow system - was investigated in order to find the flow field parameters that provide pertinent indicators in pipeline leakage detection and characterisation. Plausible leak scenarios which may occur in the field were performed for the gas-liquid pipeline using a three-dimensional RANS CFD model. The perturbation of the pertinent flow field indicators for different leak scenarios is reported, which is expected to help in improving the understanding of multiphase flow behaviour induced by leaks. The results of the simulations were validated against the latest experimental and numerical data reported in the literature. The proposed surrogate model was later applied to pipeline leak detection and characterisation. The CFD modelling results showed that fluid flow parameters are pertinent indicators in pipeline leak detection. It was observed that upstream pipeline pressure could serve as a critical indicator for detecting leakage, even if the leak size is small. In contrast, the downstream flow rate is a dominant leakage indicator if the flow rate monitoring is chosen for leak detection. The results also reveal that when two leaks of different sizes co-occur in a single pipe, detecting the small leak becomes difficult if its size is below 25% of the large leak size. However, in the event of a double leak with equal dimensions, the leak closer to the pipe upstream is easier to detect. The results from all the analyses demonstrate the PSOASM algorithm's superiority over the well-known sequential sampling schemes employed for evaluation. The test results show that the PSOASM algorithm can be applied for pipeline leak detection with limited training datasets and provides a general framework for improving computational efficiency using adaptive surrogate modelling in various real-life applications

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

Energy savings measures in compressed air systems

Compressed air is one of the most widely used application energies in the industry, such as good transportability, safety, purity, cleanliness, storage capacity and ease of use. In many countries, compressed air systems account for approximately 10% of the industry’s total electricity consumption. Despite all its advantages, compressed air is expensive, only between 10% and 30% of the energy consumed reaches the point of final use. Energy is lost as heat, leaks, pressure drop, misuse, among others. Energy efficiency measures such as: reducing compressor pressure, lowering air inlet temperature, adequate storage capacity, recovering residual heat from the air compressor and reducing leakage, can produce energy savings between 20% and 60%, with an average return on investment lower than 2 years. This paper analyzes the main energy efficiency measures that can be applied in the CASs, the potential energy savings, implementation costs and return rate of each of them are being calculated giving a necessary tool for companies in their objectives to reduce greenhouse gas emissions and energy consumption