Geospatial Data Modeling to Support Energy Pipeline Integrity Management

Several hundred thousand miles of energy pipelines span the whole of North America -- responsible for carrying the natural gas and liquid petroleum that power the continent\u27s homes and economies. These pipelines, so crucial to everyday goings-on, are closely monitored by various operating companies to ensure they perform safely and smoothly. Happenings like earthquakes, erosion, and extreme weather, however -- and human factors like vehicle traffic and construction -- all pose threats to pipeline integrity. As such, there is a tremendous need to measure and indicate useful, actionable data for each region of interest, and operators often use computer-based decision support systems (DSS) to analyze and allocate resources for active and potential hazards. We designed and implemented a geospatial data service, REST API for Pipeline Integrity Data (RAPID) to improve the amount and quality of data available to DSS. More specifically, RAPID -- built with a spatial database and the Django web framework -- allows third-party software to manage and query an arbitrary number of geographic data sources through one centralized REST API. Here, we focus on the process and peculiarities of creating RAPID\u27s model and query interface for pipeline integrity management; this contribution describes the design, implementation, and validation of that model, which builds on existing geospatial standards

REST API to Access and Manage Geospatial Pipeline Integrity Data

Today’s economy and infrastructure is dependent on raw natural resources, like crude oil and natural gases, that are optimally transported through a net- work of hundreds of thousands of miles of pipelines throughout America[28]. A damaged pipe can negatively a↵ect thousands of homes and businesses so it is vital that they are monitored and quickly repaired[1]. Ideally, pipeline operators are able to detect damages before they occur, but ensuring the in- tegrity of the vast amount of pipes is unrealistic and would take an impractical amount of time and manpower[1]. Natural disasters, like earthquakes, as well as construction are just two of the events that could potentially threaten the integrity of pipelines. Due to the diverse collection of data sources, the necessary geospatial data is scat- tered across di↵erent physical locations, stored in di↵erent formats, and owned by di↵erent organizations. Pipeline companies do not have the resources to manually gather all input factors to make a meaningful analysis of the land surrounding a pipe. Our solution to this problem involves creating a single, centralized system that can be queried to get all necessary geospatial data and related informa- tion in a standardized and desirable format. The service simplifies client-side computation time by allowing our system to find, ingest, parse, and store the data from potentially hundreds of repositories in varying formats. An online web service fulfills all of the requirements and allows for easy remote access to do critical analysis of the data through computer based decision support systems (DSS). Our system, REST API for Pipeline Integrity Data (RAPID), is a multi- tenant REST API that utilizes HTTP protocol to provide a online and intuitive set of functions for DSS. RAPID’s API allows DSS to access and manage data stored in a geospatial database with a supported Django web framework. Full documentation of the design and implementation of RAPID’s API are detailed in this thesis document, supplemented with some background and validation of the completed system

A Tour of MOOS-IvP Autonomy Software Modules

This paper provides an overview of the MOOS-IvP autonomy software modules. The MOOS-IvP collection of software, i.e., codebase, described here has been developed and is currently maintained by three organizations - Oxford University, Massachusetts Institute of Technology (MIT), and the Naval Undersea Warfare Center (NUWC) Division Newport Rhode Island. The objective of this paper is to provide a comprehensive list of modules and provide for each (a) a general description of functionality, (b) dependency relationships to other modules, (c) rough order of magnitude in complexity or size, (d) authorship, and (e) current and planned distribution access

An Extensible User Interface for Lean 4

Contemporary proof assistants rely on complex automation and process libraries with millions of lines of code. At these scales, understanding the emergent interactions between components can be a serious challenge. One way of managing complexity, long established in informal practice, is through varying external representations. For instance, algebraic notation facilitates term-based reasoning whereas geometric diagrams invoke spatial intuition. Objects viewed one way become much simpler than when viewed differently. In contrast, modern general-purpose ITP systems usually only support limited, textual representations. Treating this as a problem of human-computer interaction, we aim to demonstrate that presentations - UI elements that store references to the objects they are displaying - are a fruitful way of thinking about ITP interface design. They allow us to make headway on two fronts - introspection of prover internals and support for diagrammatic reasoning. To this end we have built an extensible user interface for the Lean 4 prover with an associated ProofWidgets 4 library of presentation-based UI components. We demonstrate the system with several examples including type information popups, structured traces, contextual suggestions, a display for algebraic reasoning, and visualizations of red-black trees. Our interface is already part of the core Lean distribution

Automated Theorem Proving in GeoGebra: Current Achievements

GeoGebra is an open-source educational mathematics software tool, with millions of users worldwide. It has a number of features (integration of computer algebra, dynamic geometry, spreadsheet, etc.), primarily focused on facilitating student experiments, and not on formal reasoning. Since including automated deduction tools in GeoGebra could bring a whole new range of teaching and learning scenarios, and since automated theorem proving and discovery in geometry has reached a rather mature stage, we embarked on a project of incorporating and testing a number of different automated provers for geometry in GeoGebra. In this paper, we present the current achievements and status of this project, and discuss various relevant challenges that this project raises in the educational, mathematical and software contexts. We will describe, first, the recent and forthcoming changes demanded by our project, regarding the implementation and the user interface of GeoGebra. Then we present our vision of the educational scenarios that could be supported by automated reasoning features, and how teachers and students could benefit from the present work. In fact, current performance of GeoGebra, extended with automated deduction tools, is already very promising—many complex theorems can be proved in less than 1 second. Thus, we believe that many new and exciting ways of using GeoGebra in the classroom are on their way

An intelligent Geographic Information System for design

Recent advances in geographic information systems (GIS) and artificial intelligence (AI) techniques have been summarised, concentrating on the theoretical aspects of their construction and use. Existing projects combining AI and GIS have also been discussed, with attention paid to the interfacing methods used and problems uncovered by the approaches. AI and GIS have been combined in this research to create an intelligent GIS for design. This has been applied to off-shore pipeline route design. The system was tested using data from a real pipeline design project. [Continues.

De-Linearizing Learning

Given the fast changing and accelerating world, reforming education constantly requires our full attention. The requirements to knowledge and skills will change on a constant basis. But, how to keep up to acquiring the latest and most relevant knowledge in such a dynamic time? And how do we deal with the almost exploding sources of knowledge that can be used in the learning process? This paper introduces a new way of looking at education, where it is not only the students who learn; not only teachers who educate; not only the researchers who find out new developments; and not only the practitioners who use knowledge and skills. The paper is meant to sketch a de-linearized way of looking at learning as opposed to the traditional linear learning approach. It develops a view on a new learning reality that needs further elaboration to proof the relevance of this approach

Technical report of data mining

No abstract availabl

Administrative Justice, Environmental Governance, and The Rule of Law in Malawi

This paper examines the impact of administrative law on the rule of law and governance in Malawi, using environmental agencies as a case study. Its findings expose a number of limitations of administrative law in facilitating good environmental governance. These include regulatory or power capture by “invisible Barons” who wield their power to undermine the effectiveness of administrative law and the rule of law. Procedural fairness and improprieties are drawn from specific cases, then analyzed and discussed. The study found that political considerations affect the rate and direction of rulemaking more than judicial considerations

Energy efficiency and carbon dioxide emissions across different scales of iron ore mining operations in Western Australia

During the last two decades, Western Australian iron ore mining industry experienced an exponential production growth arising from increased global demand for steel. The upturn in the iron ore price and considerably lower production cost encouraged extensive mining and consequently high-grade ore reserves were gradually depleted. Despite the energy-intensive nature of mining, high profitability motivated the mining companies to extract marginal-grade deposits with additional processing requirements, which increased energy consumption and ultimately increased the cost of iron ore production. This thesis sought to identify the energy efficiencies of open-cut iron ore mining operations, in terms of scale of operation as well as within individual mining processes, so that energy consumption could be reduced, and sustainability enhanced. Efficiency indices were used to determine energy efficiency across different scales of operation. Overall energy consumption (per unit of processed ore) was directly related to the scale of operation, where large-scale mining operations are more energy efficient compared to medium and small scales requiring the lowest amount of energy to process a unit of ore. This suggests that an economy of scale based on energy efficiency can be observed in iron ore mining operations. Small-scale mining operations recorded the highest energy consumption to process a unit of ore, indicating the lowest energy efficiency among the three different scales of operation. However, the composite energy indicator indicated that the energy efficiency of a particular mining operation is also influenced by the geological and physical parameters of individual factors including the waste-ore ratio, grade of ore, average haulage distance and production capacity. The results of the regression analysis confirmed that it is the combined effect of all the aforementioned parameters that has a pronounced effect on the amount of energy consumed to process a unit of ore. Energy consumption per unit of processed ore at different process stages revealed that the loading and hauling phase is the most energy intensive process stage in an iron ore mining operation regardless of the scale at which it is operating. The milling and stockpiling phase was the second highest energy consuming process stage, while the drilling and blasting phase was the subsequent energy demanding process stage in iron ore mining operations. Small-scale operations recorded a higher energy consumption in loading and hauling than the medium-scale operations, suggesting that the equipment with high load capacities and energy efficient technologies such as overland conveyor belts, and advanced technologies including autonomous haulage trucks resulted lower energy consumption in medium scale mining operations. However, the energy consumed to mill and stockpile a unit of ore in medium-scale operations was high compared to the small-scale operations, suggesting that the energy consumption in milling and stockpiling is mainly influenced by the properties of the mill feed, such as moisture content. Further, the amount of processing needed to achieve sufficient final product quality can also influence energy consumption. Findings from this study support the idea that an economy of scale can be observed across iron ore mining operations in Western Australia based on energy efficiency. The study also provided essential baseline information for future studies on the variations in energy efficiency across different iron ore mining operational scales in Western Australia