In situ process monitoring of nuclear waste glass melts using non-contact microwave sensor

Sivaprakasam, Balamurugan T.; Gopalakrishnan, S.; Shah, J.G.; Kaushik, C.P.; Krishnamurthy, C. V.; Arunachalam, Kavitha

doi:10.1016/j.measurement.2020.107793

Cited by 6 publications

(2 citation statements)

References 17 publications

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“…For instance, an electronic nose can be developed using sensors to quantify odor concentration in real time to help treat wastewater (Burgués et al 2021 ). Additionally, Sivaprakasam et al ( 2020 ) proposed using a non-contact microwave sensor for in situ process monitoring of nuclear waste glass melts in cold crucible induction melting furnaces. In addition, they designed infrared sensors to determine the filling level of the carriages, gas sensors to detect hazardous gases, temperature, humidity sensors, and sound sensors to monitor noise pollution.…”

Section: Artificial Intelligence In Waste Managementmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

et al. 2023

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The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

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Section: Artificial Intelligence In Waste Managementmentioning

confidence: 99%

Artificial intelligence for waste management in smart cities: a review

et al. 2023

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“…When the robot is ready to be decommissioned, an operator can remove the battery from the robot and the polythene bag. This allows the robot to be decommissioned using standard waste routes (Jacoby, 2013; Sivaprakasam et al, 2020), with the battery available for free release provided the integrity of the polythene bag has been retained during deployment.…”

Section: System Overviewmentioning

confidence: 99%

Vega—A small, low cost, ground robot for nuclear decommissioning

Bird

Nancekievill

West

et al. 2021

Journal of Field Robotics

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This paper presents the Vega robot, which is a small, low cost, potentially disposable ground robot designed for nuclear decommissioning. Vega has been developed specifically to support characterization and inspection operations, such as 2D and 3D mapping, radiation scans and sample retrieval. The design and construction methodology that was followed to develop the robot is described and its capabilities detailed. Vega was designed to provide flexibility, both in software and hardware, is controlled via tele‐operation, although it can be extended to semi and full autonomy, and can be used in either tethered or untethered configurations. A version of the tethered robot was designed for extreme radiation tolerance, utilizing relay electronics and removing active electronic systems. Vega can be outfitted with a multitude of sensors and actuators, including gamma spectrometers, alpha/beta radiation sensors, LiDARs and robotic arms. To demonstrate its flexibility, a 5 degree‐of‐freedom manipulator has been successfully integrated onto Vega, facilitating deployments where handling is required. To assess the tolerance of Vega to the levels of ionizing radiation that may be found in decommissioning environments, its individual components were irradiated, allowing estimates to be made of the length of time Vega would be able to continue to operate in nuclear environments. Vega has been successfully deployed in an active environment at the Dounreay nuclear site in the UK, deployed in nonactive environments at the Atomic Weapons Establishment, and demonstrated to many other organizations in the UK nuclear industry including Sellafield Ltd, with the goal of moving to active deployments in the future.

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Revolutionizing waste management: unleashing the power of artificial intelligence and machine learning

Farghali,

Osman

2024

Advances in Energy From Waste

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In situ process monitoring of nuclear waste glass melts using non-contact microwave sensor

Cited by 6 publications

References 17 publications

Artificial intelligence for waste management in smart cities: a review

Artificial intelligence for waste management in smart cities: a review

Vega—A small, low cost, ground robot for nuclear decommissioning

Revolutionizing waste management: unleashing the power of artificial intelligence and machine learning

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