Drones Move From "Nice To Have" to Strategic Resources for Projects

2020 ◽  
Vol 72 (12) ◽  
pp. 29-30
Author(s):  
Judy Feder
Keyword(s):  
Traffic Control ◽  
Autonomous Vehicles ◽  
Oil And Gas ◽  
Work Processes ◽  
Construction Sites ◽  
Strategic Resources ◽  
Digital Twins ◽  
Hazardous Area ◽  
Construction Supervision ◽  
Safety Evacuation

While drones have been used on oil and gas facilities for video inspections and other tasks, they have been operated by an on-site pilot or one positioned on a bobbing workboat adjacent to an offshore platform. Now a proof-of-concept study conducted by TechnipFMC has tested the feasibility of a global drone system with drones operated remotely by pilots based anywhere in the world. The study is the subject of a paper (OTC 30241) presented at the Offshore Technology Conference Asia in Kuala Lumpur in November. Construction supervision and health, safety, and environmental (HSE) monitoring were the main drivers of the study. The construction supervision application is part of a larger digitalization ambition to monitor and manage construction activities with data generated from the drone ultimately feeding an internal software dedicated to this business process. Potential HSE applications include crisis management, human safety, evacuation assistance, hazardous-area identification, traffic control, carbon-footprint reduction, and environmental surveys. One of the study’s main objectives was to move from traditional unmanned autonomous vehicles (UAV) to resident systems and to investigate the possibilities they could offer. Aerial views have been used extensively to reduce personnel exposure in specific situations such as difficult access or potentially dangerous inspection areas like active flares, confined spaces, or high structures. In these cases, the drones are controlled by an on-site pilot who is either within their line of sight or a short distance away. Combining AUV technology with embedded and associated intelligence from the internet of things (IoT), artificial intelligence (AI), and cloud and edge computing should enable drones to fly safely in complex and dynamic environments, resulting in integrated, resident systems that are permanently deployed at construction sites and available 24/7 without the need for an on-site certified pilot. Implementing these technologies will make data accessible and available in real time to people working on the project worldwide and it will also generate new work processes for project management and execution. Flight and Operations Testing According to the paper’s primary author, Nicolas Tcherniguin, manager of offshore business and technology with TechnipFMC, digital tools such as image recognition, machine learning, and simulation of digital twins based on the drone’s flight have been tested. Remaining bottlenecks have been identified, and some have been addressed while others will require additional efforts. AI development will offer additional features, especially if they can be integrated with other ground monitoring devices.

Agiles Modellieren von Servicetätigkeiten/Holistic System Solution for IoT Systems – Agile Modeling of Service Activities

2021 ◽  
Vol 111 (07-08) ◽  
pp. 536-538
Author(s):  
Eckart Uhlmann ◽  
Manuel Bösing ◽  
Julian Polte ◽  
Lucas Kirsch ◽  
Ian Altmann ◽  
...  
Keyword(s):  
Context Information ◽  
Interactive Software ◽  
Work Processes ◽  
Software Application ◽  
Context Sensitive ◽  
Digital Twins ◽  
System Solution ◽  
Service Activities ◽  
Assistance Systems

Kontextsensitive Assistenzsysteme bieten ein großes Potenzial zur Optimierung von Arbeitsabläufen. Durch die Einbindung Digitaler Zwillinge können unmittelbar Kontextinformationen zur Verfügung gestellt werden, wobei die Modellierung der Arbeitsabläufe derzeit wenig standardisiert ist. Die in diesem Beitrag vorgestellte Lösung zeigt eine interaktive Software-Applikation für kontextsensitive Assistenzsysteme in Kombination mit Prozesspatterns für die Modellierung von Servicefällen.   Context-sensitive assistance systems offer a huge potential for the optimization of work processes. Through the integration of digital twins, context information can be provided instantly, whereby a modeling of work processes is hardly standardized. The following article presents a solution of an interactive software application for context-sensitive assistance systems combined with process patterns for a modeling of service activities.

Prediction of Vehicular Traffic Flow using Levenberg-Marquardt Artificial Neural Network Model: Italy Road Transportation System

2022 ◽  
Vol 24 (2) ◽  
pp. E74-E86
Author(s):  
Isaac Oyeyemi Olayode ◽  
Alessandro Severino ◽  
Tiziana Campisi ◽  
Lagouge Kwanda Tartibu
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Autonomous Vehicles ◽  
Time Of Day ◽  
Vehicle Speed ◽  
Road Transportation ◽  
Artificial Neural

In the last decades, the Italian road transport system has been characterized by severe and consistent traffic congestion and in particular Rome is one of the Italian cities most affected by this problem. In this study, a LevenbergMarquardt (LM) artificial neural network heuristic model was used to predict the traffic flow of non-autonomous vehicles. Traffic datasets were collected using both inductive loop detectors and video cameras as acquisition systems and selecting some parameters including vehicle speed, time of day, traffic volume and number of vehicles. The model showed a training, test and regression value (R2) of 0.99892, 0.99615 and 0.99714 respectively. The results of this research add to the growing body of literature on traffic flow modelling and help urban planners and traffic managers in terms of the traffic control and the provision of convenient travel routes for pedestrians and motorists.

A Survey on the Use of Digital Twins for Maintenance and Safety in the Offshore Oil and Gas Industry

2021 ◽  
Author(s):  
Tom Ivar Pedersen ◽  
Håkon Grøtt Størdal ◽  
Håvard Holm Bjørnebekk ◽  
Jørn Vatn
Keyword(s):  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Gas Industry ◽  
Digital Twins ◽  
Offshore Oil And Gas ◽  
Offshore Oil

Human Factors Considerations for Teaming between Construction Workersand Voice-based Intelligent Virtual Agent (VIVA)

2021 ◽  
Vol 65 (1) ◽  
pp. 72-76
Author(s):  
Maryam Rahimi Movassagh ◽  
Nazila Roofigari-Esfahan ◽  
Sang Won Lee ◽  
Carlos Evia ◽  
David Hicks ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Human Factors ◽  
Research Agenda ◽  
Task Environment ◽  
Virtual Agent ◽  
Work Processes ◽  
Construction Sites

Construction sites experience low productivity due to particular characteristics such as unique designs in each project, sporadic arrival of projects, and complexity of the process. Another contributing factor to low productivity is poor communication among workers, supervisors, and a site’s centralized knowledge hub. Research shows that introducing advanced artificial intelligence (AI) technology in construction can tackle these problems. In this paper, we analyzed human factors considerations–user, task, environment, and technology and identified their characteristics and challenges to design an interactive AI system to facilitate communication between workers and other stakeholders. Based on the analysis, we propose a voice-based intelligent virtual agent (VIVA) as a multi-purpose AI system on construction sites with a further research agenda. We hope that this effort can guide the design of construction-specific AI systems and that this worker-AI teaming can improve overall work processes, enhance productivity, and promote safety in construction.

Prognostics and Health Management of Choke Valves Subject to Erosion

2013 ◽  
pp. 313-331
Author(s):  
Giulio Gola ◽  
Bent H. Nystad
Keyword(s):  
Condition Monitoring ◽  
Performance Monitoring ◽  
Oil And Gas ◽  
Health Management ◽  
Cost Savings ◽  
Remaining Useful Life ◽  
Work Processes ◽  
Offshore Oil And Gas ◽  
And Performance ◽  
Monitoring Technologies

Oil and gas industries are constantly aiming at improving the efficiency of their operations. In this respect, focus is on the development of technology, methods, and work processes related to equipment condition and performance monitoring in order to achieve the highest standards in terms of safety and productivity. To this aim, a key issue is represented by maintenance optimization of critical structures, systems, and components. A way towards this goal is offered by Condition-Based Maintenance (CBM) strategies. CBM aims at regulating maintenance scheduling based on data analyses and system condition monitoring and bears the potential advantage of obtaining relevant cost savings and improved operational safety and availability. A critical aspect of CBM is its integration with condition monitoring technologies for handling a wide range of information sources and eventually making optimal decisions on when and what to repair. In this chapter, a CBM case study concerning choke valves utilized in Norwegian offshore oil and gas platforms is proposed and investigated. The objective is to define a procedure for optimizing maintenance of choke valves by on-line monitoring their condition and determining their Remaining Useful Life (RUL). Choke valves undergo erosion caused by sand grains transported by the oil-water-gas mixture extracted from the well. Erosion is a critical problem which can affect the correct valve functioning, resulting in revenue losses and cause environmental hazards.

Optimal Placement of Reservation-Based Intersections in Urban Networks

2019 ◽  
Vol 2673 (10) ◽  
pp. 781-792
Author(s):  
Rahul Patel ◽  
Prashanth Venkatraman ◽  
Stephen D. Boyles
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Large City ◽  
Ranking Method ◽  
Optimal Placement ◽  
Total System ◽  
Travel Times ◽  
Urban Networks ◽  
High Performing

Reservation-based traffic control is a revolutionary intersection management system which involves the communication of autonomous vehicles and an intersection to request space-time trajectories through the intersection. Although previous studies have found congestion and throughput benefits of reservation-based control that surpass signalized control, other studies have found negative impacts at peak travel times. The main purpose of this paper is to find and characterize favorable mixed configurations of reservation-based controls and signalized controls in a large city network which minimize total system travel times. As this optimization problem is bi-level and challenging, three different methods are proposed to heuristically find effective mixed configurations. The first method is an intersection ranking method that uses simulation to assign a score to each intersection in a network based on localized potential benefit to system travel time under reservation control and then ranks all intersections accordingly. The second is another ranking method; however, it uses linear regression to predict an intersection’s localized score. Finally, a genetic algorithm is presented that iteratively approaches high-performing network configurations yielding minimal system travel times. The methods were tested on the downtown Austin network and configurations found that are less than half controlled by reservation intersections that improve travel times beyond an all-reservation controlled network. Overall, the results show that the genetic algorithm finds the best performing configurations, with the initial score-assigning ranking method performing similarly but much more efficiently. It was finally find that favorable reservation placement is in consecutive chains along highly trafficked corridors.

scholarly journals An autonomous vehicle sequencing problem at intersections: A genetic algorithm approach

2013 ◽  
Vol 23 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Fei Yan ◽  
Mahjoub Dridi ◽  
Abdellah El Moudni
Keyword(s):  
Genetic Algorithm ◽  
Traffic Control ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Control Methods ◽  
Traffic Condition ◽  
Sequencing Problem ◽  
Genetic Algorithm Approach ◽  
Short Time ◽  
Intersection Management

This paper addresses a vehicle sequencing problem for adjacent intersections under the framework of Autonomous Intersection Management (AIM). In the context of AIM, autonomous vehicles are considered to be independent individuals and the traffic control aims at deciding on an efficient vehicle passing sequence. Since there are considerable vehicle passing combinations, how to find an efficient vehicle passing sequence in a short time becomes a big challenge, especially for more than one intersection. In this paper, we present a technique for combining certain vehicles into some basic groups with reference to some properties discussed in our earlier works. A genetic algorithm based on these basic groups is designed to find an optimal or a near-optimal vehicle passing sequence for each intersection. Computational experiments verify that the proposed genetic algorithms can response quickly for several intersections. Simulations with continuous vehicles are carried out with application of the proposed algorithm or existing traffic control methods. The results show that the traffic condition can be significantly improved by our algorithm.

Approaches to the Determination of Oil Content in Oil Shale

2011 ◽  
Vol 320 ◽  
pp. 441-444
Author(s):  
Zhi Yang Yuan
Keyword(s):  
Oil Shale ◽  
Fossil Fuel ◽  
Oil And Gas ◽  
Heating Temperature ◽  
Raw Material ◽  
Shale Oil ◽  
Strategic Resources ◽  
Fuel Resource ◽  
The Impact

Oil and gas are important energy minerals and strategic resources. Moreover, as their substitute, oil shale is the non-renewable fossil fuel resource. In this paper, regarding the oil shale of Huadian in Jilin Province as raw material, we made a research on the approaches to extracting shale oil from oil shale as well as an experimental determination on the impact of heating temperature, shale grain size and holding time.

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