Abstract

Safety and reliability of hydrocarbon transportation lines (pipelines) around the world represents a critical aspect for industry, operators and population. Lines failures caused by external agents, corrosion, inadequate designs, among others, generate impacts on population, environment, infrastructure and economy, besides it may be catastrophically. Therefore, it is essential to constantly monitor operating conditions and hydraulic lines to faults and thus to take measures to mitigate the failure. Localization of leakage is more than comparison between simulated and measured flows, from the dynamic of these flows it can be inferred the localization of the leakage, and even its magnitude. One option is to develop an inverse Transient Model (TM) able to calculate parameters of the pipeline by using the measured flow. However, if the calculation of flows is computational expensive, the inverse calculation is even more. These phenomenological models reproduce as closely the response (flow and pressure) of the pipeline. The simulation contains information to optimize the pumping rate, the momentum and energy including a high number of inputs and constraints to consider that growing exponentially with the level of detail to get in the pipeline. Therefore, this method has a high computational cost. The other option is to simulate several scenarios by using TM and train some kind of classifier or predictor with the simulated measurements. The first phase of our complete proposed methodology under development is presented in this work. We have focused on carrying out simulations of pressure along a pipeline using TM and applying Principal Component Analysis (PCA) as a tool to recognize hided patterns which allow classify leakages in different locations and different magnitudes. doi: 10.12783/SHM2015/292 Document type: Conference object

Abstract

The ferrous pipe structures of oil and gas production and, the transmission pipelines are, in majority, buried. Nowadays, phenomena like corrosion, mechanical stress, soil erosion, worker mistakes and damages caused by third parts have generated several problems over pipelines. Thus, major investment on integrity programs with In-Line Inspection Tools has been improved in order to examine the pipelines and avoid environmental, financial and social disasters. Recently in Colombia, the Research Institute of Corrosion - CIC (Corporacion para la Investigacion de la Corrosion) runs their own smart pig ILI tool in pipelines. The inspection technology is based on inertial and operational trends, ITION (Inertial Technology Inspection and Operational Trends). Up to date, the technology has been tested several times inside of pipelines providing valuable information along of thousand kilometres. These records contain a huge amount of data that sometimes is difficult or impossible to understand by themselves. A univariate statistical analysis can be used to determine the thresholds for each observation variable. However, it does not analyse the correlated information between them. In this way, the main contribution of this work is the development of a methodology based on Principal Component Analysis (PCA) to monitor the structure by using the whole available variables gathered by ITION. doi: 10.12783/SHM2015/291 Document type: Conference object

Abstract

In post-reform China, rapid motorisation causes various problems like traffic congestion, diminishing road safety and air pollution. Adequate policies necessitate an understanding of the forces behind changing mode choices, but the rapidly developing literature is not complete yet. This paper aims to help fill that gap with an analysis of mode choice for commuting and shopping-leisure trips in Nanjing. Using the Nanjing Residents Travel Survey, we find that models with the same independent variables explain mode choice in Nanjing better than in other cities in the world. Comparatively, members of ‘adult families’ use public transport and walking more often than the private car and bicycle. And inhabitants of danwei neighbourhoods walk more often than residents in commodity housing estates. These conclusions suggest that ongoing socio-spatial transformations will push mode choice in China further towards private car use.