We report on a first field test in which miniaturized sensor motes were used to explore and inspect an operational pipeline by performing in situ measurements. The spherical sensor motes with a diameter of 39 mm were equipped with an inertial measurement unit (IMU) measuring 3-D acceleration, rotation, and magnetic field, as well as an ultrasound emitter. The motes were injected into the pipeline and traversed a 260-m section of it with the flow of water. After the extraction of the motes from the pipeline, the recorded IMU data were read out for the off-line analysis. Unlike dead-reckoning techniques, we analyze the IMU data to reveal structural information about the pipeline and locate pipe components, such as hydrants and junctions. The recorded data show different and distinct patterns that are a result of the fluid dynamics and the interaction with the pipeline. Using the magnetic data, pipe sections made from different materials and pipe components are identified and localized. A preliminary analysis on the motes' interaction with the pipeline shows differences in pipe wall roughness and locates structural anomalies. The results of this field test show that sensor motes can be used as a versatile and cost-effective tool for exploration and inspection of a wide variety of pipelines.
DOCUMENT
The main goal of this study is to identify knowledge gaps and uncertainties in Quantitative Risk Assessments (QRA) for CO2 pipelines and to assess to what extent those gaps and uncertainties affect the final outcome of the QRA. The impact of methodological choices and uncertain values for input parameters on the results of QRA’s have been assessed through an extensive literature review and by using commercially available release, dispersion and effect models. It is made apparent that over the full life cycle of a QRA knowledge gaps and uncertainties are present that may have large scale impact on the accuracy of assessing risks of CO2 pipelines. These encompass the invalidated release and dispersion models, the currently used failure rates, choosing the type of release to be modeled and the dose-effect relationships assumed. Also recommendations are presented for the improvement of QRA’s for CO2 pipelines.
MULTIFILE
Biogas is produced from biomass by means of digestion. Treated to so-called ‘green gas’, it can replace natural gas. Alternatively, biogas can be used to produce electrical power and heat in a combined heat power (CHP) installation. In 2014 global biogas production was only 1% of natural gas production. In the future, biogas is expected to play a role in specific applications, e.g. to provide flexibility in electricity supply
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At gas stations, tetrahydrothiophene (THT) is added to odorless biogas (and natural gas) for quick leak detection through its distinctive smell. However, for low bio and natural gas velocities, evaporation is not complete and the odorization process is compromised, causing odor fluctuations and undesired liquid accumulation on the pipeline. Inefficient odorization not only endangers the safety and well-being of gas users, but also increases gas distribution companies OPEX. To enhance THT evaporation during low bio and natural gas flow, an alternative approach involves improve the currently used atomization process. Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology that uses strong electric fields to create nano and micro droplets with a narrow size distribution. This relatively new atomization technology can improve the odorization process as it can manipulate droplet sizes according to the natural and bio gas flow. BiomEHD aims to develop, manufacture, and test an EHDA odorization system for applying THT in biogas odorization.
The pipelines are buried structures. They move together with the soil during a seismic event. They are affected from ground motions. The project aims to find out the possible effects of Groningen earthquakes on pipelines of Loppersum and Slochteren.This project is devised for conducting an initial probe on the available data to see the possible actions that can be taken, initially on these two pilot villages, Loppersum and Slochteren, for detecting the potential relationship between the past damages and the seismic activity.Lifeline infrastructure, such as water mains and sewerage systems, covering our urbanised areas like a network, are most of the times, sensitive to seismic actions. This sensitivity can be in the form of extended damage during seismic events, or other collateral damages, such as what happened in Christchurch Earthquakes in 2011 in New Zealand when the sewerage system of the city was filled in with tonnes of sand due to liquefaction.Regular damage detection is one of key solutions for operational purposes. The earthquake mitigation, however, needs large scale risk studies with expected spatial distribution of damages for varying seismic hazard levels.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
