A Cost Effective Methodology for Production Metering and Allocation Using Real-Time Virtual Metering in a Mature Offshore Oilfield- A Case Study of the Greater Angostura Field

2016 ◽  
Author(s):  
Italo Raffaele Acuna
Keyword(s):  
Real Time ◽  
Cost Effective

Risk Management via Digital Dashboards in Statistics Data Centers

2020 ◽  
Vol 13 (1) ◽  
pp. 27-45
Author(s):  
Atif Amin ◽  
Raul Valverde ◽  
Malleswara Talla
Keyword(s):  
Risk Management ◽  
Real Time ◽  
Data Center ◽  
Cost Effective ◽  
Risk Level ◽  
Total Risk ◽  
Counter Measures ◽  
Effective Manner ◽  
Risk Managers

Every system, when connected to a network, is susceptible to threat of being hacked. It is important to protect all systems of an organization in real-time in a cost-effective manner. This article presents a well-designed and integrated database for risk management data using a dashboard interface in real-time risk that makes it easy for risk managers to reach a understanding the level of threats to be able to apply right controls to mitigate them. In this article, a case study of a data center for a statistical management institute is presented that proposes the calculation of total risk at the organization level by using the proposed risk database. A digital dashboard is also designed for presenting the risk level results so that decision makers can apply counter measures. The risk level on a dashboard viewer makes it easy for decision maker to understand the overall risk level at the statistics data center and assists in the creation of a tool to follow-up risk management since the time a threat hits until the time of its mitigation.

Cost-Effective Approach in Stimulating Openhole Water Injectors Using Real-Time Flow Measurement A Case Study

2017 ◽  
Author(s):  
Nasr. Al-Houti ◽  
Y. Al-Matrouk ◽  
M. R. Al-Othman ◽  
H. S. Al-Mehanna ◽  
M. N. Al-Haddad ◽  
...  
Keyword(s):  
Real Time ◽  
Flow Measurement ◽  
Cost Effective ◽  
Time Flow ◽  
Cost Effective Approach

Pollution based real time control strategies for combined sewer systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Author(s):  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Ammonia Nitrogen ◽  
Simple Extension ◽  
Real Time Control ◽  
Time Control ◽  
Sewer Systems ◽  
Receiving Waters ◽  
Tunnel System

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

The socio-spatial politics of royalties and their distribution: A case study of the Surat Basin, Queensland

2021 ◽  
pp. 0308518X2110266
Author(s):  
Neil Argent ◽  
Sean Markey ◽  
Greg Halseth ◽  
Laura Ryser ◽  
Fiona Haslam-McKenzie
Keyword(s):  
Cost Effective ◽  
Indigenous Communities ◽  
Energy Extraction ◽  
Grant Application ◽  
Coal Seam Gas ◽  
Spatial Politics ◽  
Developmental Needs ◽  
Development Fund ◽  
Effective Use

This paper is concerned with the socio-spatial and ethical politics of redistribution, specifically the allocation of natural resources rents from political and economic cores to the economic and geographical peripheries whence the resource originated. Based on a case study of the coal seam gas sector in Queensland's Surat Basin, this paper focuses on the operation of the Queensland State Government's regional development fund for mining and energy extraction-affected regions. Employing an environmental justice framework, we critically explore the operation of these funds in ostensibly helping constituent communities in becoming resilient to the worst effects of the ‘staples trap’. Drawing on secondary demographic and housing data for the region, as well as primary information collected from key respondents from mid-2018 to early 2019, we show that funds were distributed across all of the local government areas, and allocated to projects and places primarily on a perceived economic needs basis. However, concerns were raised with the probity of the funds’ administration. In terms of recognition justice, the participation of smaller and more remote towns and local Indigenous communities was hampered by their structural marginalisation. Procedurally, the funds were criticised for the lack of local consultation taken in the development and approval of projects. While spatially concentrated expenditure may be the most cost-effective use of public monies, we argue that grant application processes should be open, transparent and inclusive, and the outcomes cognisant of the developmental needs of smaller communities, together with the need to foster regional solidarity and coherence.

scholarly journals A Computer Vision-Based Occupancy and Equipment Usage Detection Approach for Reducing Building Energy Demand

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 156
Author(s):  
Paige Wenbin Tien ◽  
Shuangyu Wei ◽  
John Calautit
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Energy Demand ◽  
Energy Savings ◽  
Internal Heat ◽  
Electrical Equipment ◽  
Building Energy ◽  
Hvac Systems ◽  
Equipment Usage

Because of extensive variations in occupancy patterns around office space environments and their use of electrical equipment, accurate occupants’ behaviour detection is valuable for reducing the building energy demand and carbon emissions. Using the collected occupancy information, building energy management system can automatically adjust the operation of heating, ventilation and air-conditioning (HVAC) systems to meet the actual demands in different conditioned spaces in real-time. Existing and commonly used ‘fixed’ schedules for HVAC systems are not sufficient and cannot adjust based on the dynamic changes in building environments. This study proposes a vision-based occupancy and equipment usage detection method based on deep learning for demand-driven control systems. A model based on region-based convolutional neural network (R-CNN) was developed, trained and deployed to a camera for real-time detection of occupancy activities and equipment usage. Experiments tests within a case study office room suggested an overall accuracy of 97.32% and 80.80%. In order to predict the energy savings that can be attained using the proposed approach, the case study building was simulated. The simulation results revealed that the heat gains could be over or under predicted when using static or fixed profiles. Based on the set conditions, the equipment and occupancy gains were 65.75% and 32.74% lower when using the deep learning approach. Overall, the study showed the capabilities of the proposed approach in detecting and recognising multiple occupants’ activities and equipment usage and providing an alternative to estimate the internal heat emissions.

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