Tomas Iešmantas

Risk and uncertainty analysis of gas pipeline failure and gas combustion consequence

Taking into account a general concept of risk parameters and knowing that natural gas provides very significant portion of energy, firstly, it is important to insure that the infrastructure remains as robust and reliable as possible. For this purpose, authors present available statistical information and probabilistic analysis related to failures of natural gas pipelines. Presented historical failure data is used to model age-dependent reliability of pipelines in terms of Bayesian methods, which have advantages of being capable to manage scarcity and rareness of data and of being easily interpretable for engineers. The performed probabilistic analysis enables to investigate uncertainty and failure rates of pipelines when age-dependence is significant and when it is not relevant. The results of age-dependent modeling and analysis of gas pipeline reliability and uncertainty are applied to estimate frequency of combustions due to natural gas release when pipeline failure occurs. Estimated age-dependent combustion frequency is compared and proposed to be used instead of conservative and age-independent estimate. The rupture of a high-pressure natural gas pipeline can lead to consequences that can pose a significant threat to people and property in the close vicinity to the pipeline fault location. The dominant hazard is combustion and thermal radiation from a sustained fire. The second purpose of the paper is to present the combustion consequence assessment and application of probabilistic uncertainty analysis for modeling of gas pipeline combustion effects. The related work includes performance of the following tasks: to study gas pipeline combustion model, to identify uncertainty of model inputs noting their variation range, and to apply uncertainty and sensitivity analysis for results of this model. The performed uncertainty analysis is the part of safety assessment that focuses on the combustion consequence analysis. Important components of such uncertainty analysis are qualitative and quantitative analysis that identifies the most uncertain parameters of combustion model, assessment of uncertainty, analysis of the impact of uncertain parameters on the modeling results, and communication of the results’ uncertainty. As outcome of uncertainty analysis the tolerance limits and distribution function of thermal radiation intensity are given. The measures of uncertainty and sensitivity analysis were estimated and outcomes presented applying software system for uncertainty and sensitivity analysis. Conclusions on the importance of the parameters and sensitivity of the results are obtained using a linear approximation of the model under analysis. The outcome of sensitivity analysis confirms that distance from the fire center has the greatest influence on the heat flux caused by gas pipeline combustion.

Application of Bayesian Methods for Age-Dependent Reliability Analysis

In this article, the authors present a general methodology for age-dependent reliability analysis of degrading or ageing components, structures and systems. The methodology is based on Bayesian methods and inference—its ability to incorporate prior information and on ideas that ageing can be thought of as age-dependent change of beliefs about reliability parameters (mainly failure rate), when change of belief occurs not only because new failure data or other information becomes available with time but also because it continuously changes due to the flow of time and the evolution of beliefs. The main objective of this article is to present a clear way of how practitioners can apply Bayesian methods to deal with risk and reliability analysis considering ageing phenomena. The methodology describes step-by-step failure rate analysis of ageing components: from the Bayesian model building to its verification and generalization with Bayesian model averaging, which as the authors suggest in this article, could serve as an alternative for various goodness-of-fit assessment tools and as a universal tool to cope with various sources of uncertainty. The proposed methodology is able to deal with sparse and rare failure events, as is the case in electrical components, piping systems and various other systems with high reliability. In a case study of electrical instrumentation and control components, the proposed methodology was applied to analyse age-dependent failure rates together with the treatment of uncertainty due to age-dependent model selection. Copyright

Bayesian Reliability of Gas Network Under Varying Incident Registration Criteria

Gas transmission pipeline network is of great importance to any country using natural gases in its various technological processes. However, the usefulness cannot overshadow the threat posed to people and property by the grid failures. In order to quantify the reliability of the grid, se veral widely recognized pipeline incident databases have been established. However, each database contains data about pipelines operated in remote geographical regions with varying soil types, under different incident registration criterion. For a longer time period even in single database, there is variation of these incident registration criteria. Therefore, analysis of an entire sample without regard to the incident criteria change raises suspicions about the validity of resulting inferences. Authors move beyond the qualitative pipeline incident database comparison and provide a methodology for quantitative integration of all available statistical information to improve gas pipeline network reliability evaluation. We develop a new model called Criteria-dependent Poisson model, which takes into account various incident data collection criteria and extend it to the hierarchical (Bayesian) case when different databases with differing incident registration criteria can be joined in the same analysis. With the real data examples, we demonstrate the applicability of our method, which unfolds itself to be of great usefulness in reliability prediction. The Lithuanian pipeline network failure rate assessment shows the advantages of hierarchical structuring of Criteria-dependent Poisson model in small sample problems. Copyright

Bayesian analysis of heterogeneous reliability data for vulnerability assessment

Abstract Bayesian models are already present in almost every branch of reliability theory and its applications. This class of models provides an easy and intuitive way to infer about the issue at hand with a clear handling of data uncertainty. It is often the case that data used for the systems reliability assessment come from more than one information source. Whether it is power plants at different geographical locations, gas transmission pipelines operating in different environment or power transmission networks deployed within various areas. Therefore, different operating conditions, varying maintenance programs and efficiencies have their share in influencing the vulnerability and variability of reliability data. However, in practice, it is usually the case that this heterogeneity is neglected leading to the underestimation of underlying uncertainty of the data. Bayesian models are capable of dealing with this kind of uncertainty as opposed to the frequentists’ statistical methods. Hierarchical Bayesian modelling technique provides means to quantify not only within-source, but also between-source uncertainties. Even in the case of small data samples, it performs well, unlike for example the classical likelihood method which may provide degenerate estimates. In this paper, authors investigate the possibility to incorporate this kind of uncertainty into the systems reliability and vulnerability assessment through the Bayesian framework in several cases: instrumentation and control components of nuclear power plants, gas transmission networks and power transmission grids.

Energy Efficient Heat Treatment Process Design and Optimisation

Currently ICT (Information and Communication Technology) solutions are being developed for energy saving in buildings and, to some extent, for the manufacturing domain as well. This paper describes an approach and ICT tool developed for manufacturing process energy efficiency design and optimization, in particular focused on the heat treatment process of steel casting parts. Traditionally this manufacturing process is designed based on experts experience selecting a predefined temperature-time curve provided customer specifications for the resulting steel parts. However this curve can actually be optimised in terms of energy consumption while keeping required mechanical properties. This improved design is what the tool here described provides, using knowledge based approach for process design and multivariate optimisation and simulation techniques for process optimisation.

Service-Oriented Architecture for Designing of Physical Systems with Efficient Power Consumption

A distributed service oriented system which is developed to provide physical system or process monitoring, specification, analysis and simulation services are introduced in this paper. These services in separate components are developed to be used along with computer aided design systems to provide a support for designers in designing physical systems with efficient power consumption. All components of distributed system are presented to describe their role and interaction with Energy Simulator, which is developed by authors of this paper as a key component for simulation of the physical system and for calculation of energy consumption. Then, structure of Energy Simulator and its development process as well as solutions made to create flexible and scalable service oriented architecture is presented. Finally, the implementation of Energy Simulator and a case study devoted for designing of compressed air system is described together with discussion of various constraints of service oriented performance and practical simulation applying MATLAB based tools.

Energy Efficiency Optimisation in Heat Treatment Process Design

Information and Communication Technology (ICT) tools to support design in terms of energy efficiency optimisation are beginning to come into the market, in particular for energy savings and management in buildings, however little has yet been developed for manufacturing. The work here described is related to the enhancement of existing manufacturing process design methodology and tools with a set of ICT components developed allowing process knowledge based design, simulation and optimisation in terms of energy efficiency. In particular the work described is focused on the heat treatment process of steel casting parts. This process design traditionally consists of a suitable predefined temperature-time curve selection based on personal experience and given customer requirements (material and mechanical properties), yet this curve can actually be optimised in terms of energy consumption and maintenance cost, while keeping required mechanical properties.