A Deep Neural Network Sensor for Visual Servoing in 3D Spaces.

This paper describes a novel stereo vision sensor based on deep neural networks, that can be used to produce a feedback signal for visual servoing in unmanned aerial vehicles such as drones. Two deep convolutional neural networks attached to the stereo camera in the drone are trained to detect wind turbines in images and stereo triangulation is used to calculate the distance from a wind turbine to the drone. Our experimental results show that the sensor produces data accurate enough to be used for servoing, even in the presence of noise generated when the drone is not being completely stable. Our results also show that appropriate filtering of the signals is needed and that to produce correct results, it is very important to keep the wind turbine within the field of vision of both cameras, so that both deep neural networks could detect it.

Multi-Phase Flow Metering in Offshore Oil and Gas Transportation Pipelines: Trends and Perspectives.

Multi-phase flow meters are of huge importance to the offshore oil and gas industry. Unreliable measurements can lead to many disadvantages and even wrong decision-making. It is especially important for mature reservoirs as the gas volume fraction and water cut is increasing during the lifetime of a well. Hence, it is essential to accurately monitor the multi-phase flow of oil, water and gas inside the transportation pipelines. The objective of this review paper is to present the current trends and technologies within multi-phase flow measurements and to introduce the most promising methods based on parameters such as accuracy, footprint, safety, maintenance and calibration. Typical meters, such as tomography, gamma densitometry and virtual flow meters are described and compared based on their performance with respect to multi-phase flow measurements. Both experimental prototypes and commercial solutions are presented and evaluated. For a non-intrusive, non-invasive and inexpensive meter solution, this review paper predicts a progress for virtual flow meters in the near future. The application of multi-phase flows meters are expected to further expand in the future as fields are maturing, thus, efficient utilization of existing fields are in focus, to decide if a field is still financially profitable.

Dynamic Oil-in-Water Concentration Acquisition on a Pilot-Scaled Offshore Water-Oil Separation Facility.

This article is a feasibility study on using fluorescence-based oil-in-water (OiW) monitors for on-line dynamic efficiency measurement of a deoiling hydrocyclone. Dynamic measurements are crucial in the design and validation of dynamic models of the hydrocyclones, and to our knowledge, no dynamic OiW analysis of hydrocyclones has been carried out. Previous studies have extensively studied the steady state efficiency perspective of hydrocyclones, and have related them to different key parameters, such as the pressure drop ratio (PDR), inlet flow rate, and the flow-spilt. Through our study, we were able to measure the dynamics of the hydrocyclone's efficiency ( ϵ ) response to step changes in the inlet flow rate with high accuracy. This is a breakthrough in the modelling, control, and monitoring of hydrocyclones.