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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.

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.

Interior and exterior wind turbine blade inspections are necessary to extend the lifetime of wind turbine generators. The use of unmanned vehicles is an alternative to exterior wind turbine blade inspections performed by technicians that require the use of cranes and ropes. Interior wind turbine blade inspections are even more challenging due to the confined spaces, lack of illumination, and the presence of potentially harmful internal structural components. Additionally, the cost of manned interior wind turbine blade inspections is a major limiting factor. This paper analyses all aspects of the viability of using manually controlled or autonomous aerial vehicles for interior wind turbine blade inspections. We discuss why the size, weight, and flight time of a vehicle, in addition to the structure of the wind turbine blade, are the main limiting factors in performing internal blade inspections. We also describe the design issues that must be considered to provide autonomy to unmanned vehicles and the control system, the sensors that can be used, and introduce some of the algorithms for localization, obstacle avoidance and path planning that are best suited for the task. Lastly, we briefly describe which non-destructive test instrumentation can be used for the purpose.

The offshore de-oiling process is a vital part of current oil recovery, as it separates the profitable oil from water and ensures that the discharged water contains as little of the polluting oil as possible. With the passage of time, there is an increase in the water fraction in reservoirs that adds to the strain put on these facilities, and thus larger quantities of oil are being discharged into the oceans, which has in many studies been linked to negative effects on marine life. In many cases, such installations are controlled using non-cooperative single objective controllers which are inefficient in handling fluctuating inflows or complicated operating conditions. This work introduces a model-based robust H ∞ control solution that handles the entire de-oiling system and improves the system’s robustness towards fluctuating flow thereby improving the oil recovery and reducing the environmental impacts of the discharge. The robust H ∞ control solution was compared to a benchmark Proportional-Integral-Derivative (PID) control solution and evaluated through simulation and experiments performed on a pilot plant. This study found that the robust H ∞ control solution greatly improved the performance of the de-oiling process.

In recent years, the development of ground robots with human-like perception capabilities has led to the use of multiple sensors, including cameras, lidars, and radars, along with deep learning techniques for detecting and recognizing objects and estimating distances. This paper proposes a computer vision-based navigation system that integrates object detection, segmentation, and monocular depth estimation using deep neural networks to identify predefined target objects and navigate towards them with a single monocular camera as a sensor. Our experiments include different sensitivity analyses to evaluate the impact of monocular cues on distance estimation. We show that this system can provide a ground robot with the perception capabilities needed for autonomous navigation in unknown indoor environments without the need for prior mapping or external positioning systems. This technique provides an efficient and cost-effective means of navigation, overcoming the limitations of other navigation techniques such as GPS-based and SLAM-based navigation. Graphical Abstract

There has been a continued increase in the load on the current offshore oil and gas de-oiling systems that generally consist of three-phase gravity separators and de-oiling hydrocyclones. Current feedback control of the de-oiling systems is not done based on de-oiling efficiency, mainly due to lack of real-time monitoring of oil-in-water concentration, and instead relies on an indirect method using pressure drop ratio control. This study utilizes a direct method where a real-time fluorescence-based instrument was used to measure the transient efficiency of a hydrocyclone combined with an upstream gravity separator. Two control strategies, a conventional PID control structure and an H ∞ robust control structure, both using conventional feedback signals were implemented, and their efficiency was tested during severely fluctuating flow rates. The results show that the direct method can measure the system’s efficiency in real time. It was found that the efficiency of the system can be misleading, as fluctuations in the feed flow affect the inlet concentration more than the outlet oil concentration, which can lead to a discharge of large oil quantities into the ocean.

Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study, we trained YOLOv5 neural networks to detect points of interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP) compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models’ precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.

Aiming at reducing their emissions, wastewater treatment plants (WWTP) seek to reduce their energy consumption, where a large amount is used for the aeration. The case plant, Grindsted WWTP uses an alternating aeration strategy, with a common air supply system facilitating the process in four aeration tanks and thus making optimisation challenging. In this work, a nonlinear model of the air supply system is designed, in which multiple key parameters are estimated by data-driven optimization. Subsequently, a model-based control strategy for scheduling of compressors and desired airflow is proposed, to save energy without compromising the aeration performance. The strategy is based upon partly static- partly dynamic models of the compressors, describing their efficiency in terms of system head and volumetric airflow rate. The simulation study uses real plant data and shows great potential for improvement of energy efficiency, regardless of the aeration pattern in any of the four process tanks, and furthermore contributes to a reduction in compressor restarts per day. The proposed method is applicable to other WWTP with multiple compressors in the air supply system, as this study is conducted using first principle models validated on data from the daily operation.

In this work, operational data collected from four Danish wastewater treatment plants (WWTP) are assessed for quality issues and analyzed to investigate the feasibility of data-driven modeling for control purposes. All plants have permanent N2O sensors installed in the biological reactors, and N2O data are collected on the same terms as other operational data. We present and deploy a six-dimensional data quality assessment to the operational data evaluating (1) relevance, (2) accuracy, (3) completeness, (4) consistency, (5) comparability, and (6) accessibility. To increase the accuracy and completeness of the stored data, it is suggested that future initiatives are taken toward the collection and storing of metadata in WWTPs. Furthermore, seasonal variations and time-varying relationships between N2O, nitrogenous variables, and oxygen are investigated and compared across various case plants and process designs. Results show that the quality of the operational data varies substantially between plants. The investigation of time-varying interrelation between N2O and nitrogenous variables showed no clear pattern within or across different case plants. Furthermore, it is recommended that future research should consider adapting models so that more influence is linked to reliable measurements, contrary to assuming that all variables are of equal quality.

Drones equipped with thermal cameras have recently become readily available, broadening the possibilities for monitoring wildlife. The European hare (Lepus europaeus) is a nocturnal mammal that is closely monitored in Denmark due to populations declining since the mid-1900s. The limitations of current population-assessment methods, such as, spotlight counts and hunting game statistics, could be overcome by relying on drone surveys with thermal imaging for population counts. The aim of this study was to investigate the use of a DJI Mavic 2 Enterprise Advanced drone with thermal imaging as a tool for monitoring the Danish hare population. Multiple test flights were conducted over agricultural areas in Denmark in spring 2022, testing various flight altitudes, camera settings, and recording methods. The test flights were used to suggest a method for identifying and counting hares. The applied use of this methodology was then evaluated through a case survey that had the aim of identifying and counting hares over an agricultural area of 242 ha. Hares could be detected with thermal imaging at flight altitudes up to 80 m, and it was possible to fly as low as 40 m without observing direct behaviorial changes. Thermal images taken at these altitudes also provided enough detail to differentiate between species, and animal body size proved to be a good species indicator. The case study supported the use of thermal imaging-based drone surveys to identify hares and conduct population counts, thus indicating the suggested methodology as a viable alternative to traditional counting methods.