Wet gas metering is essential to enable optimised and cost-effective production of natural gas. There is demonstrably significant industrial demand for a smart metering device with diagnostic capabilities. The project addresses major technological challenges:
The project partners, GCU, TUV-SUD National Engineering Laboratory and McMenon, will contribute significant resources to design, manufacture, test and train an AI enabled instrument and bring a device to market which can play a vital role in bringing marginal fields into operation and in optimizing production of natural gas.
Antimicrobial drugs, such as antibiotics, are widely used in farming to treat a range of diseases caused by microorganisms. On dairy farms, there is a potential risk that milk containing antimicrobial residues contaminates the bulk storage tank and therefore milk destined for commercial use. Contaminated milk can no longer be sold, resulting in large financial losses across the supply chain. Additionally, contaminated milk is disposed of in the environment, dispersing antimicrobials and potentially accelerating the emergence of antimicrobial resistance. This project will deliver a low cost, real time sensor technology that can detect antibiotic contaminated milk during milking. The technology will reduce risk and increase productivity. Through industrial collaboration, this will place Scotland the forefront of inline real-time monitoring devices for milk.
Preventing children from accessing online adult content has become a challenging issue for parents and governments. This is due to the digital revolution and the fact that people use Internet services from a very young age. Conventional methods of age verification have proven to be ineffective and have raised considerable security and privacy concerns. Based on previous research, this project will explore a fast, secure and effective identification mechanism for classifying a digital user as an adult or a child using a number of sensors, including keystroke information, webcam assessment of saccadic eye movement and sensors to effect voice recognition.
Power systems are undergoing unprecedented changes worldwide, from relatively few large, centralised power stations, to massive amounts of distributed energy resources. The significantly different characteristics this transition imposes, along with limited monitoring capabilities at present, results in conventional emergency systems being less effective or ineffective during complex disturbances. This project will leverage state-of-the-art distributed sensing techniques and data analytics approaches to develop a novel intelligent situational awareness scheme that can identify early signs of critical grid situations. Using this capability, proactive real-time control actions can be taken to better secure energy supplies.