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PhD Studentship: Real-Time River Water Quality Forecasting through Integrated Hydrodynamic and Surrogate Modelling Award Summary This studentship provides a tax-free annual living allowance
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study on the Ouseburn in Newcastle upon Tyne to demonstrate its capability for real time water quality forecasting and its ability to support decision-making aimed at protecting river users from health
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systems such as flashback which can occur with hydrogen or blow-off with ammonia. Currently, we cannot accurately forecast such extreme events due to the chaotic nature of the underlying turbulent flows and
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the opportunity for increased confidence in energy yield forecasts and reduction of capital costs. As wind turbines increase in size and wind farms move towards multi-gigawatt scale, use of advanced flow models has
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predict PFAS behaviour in GAC filters. The model will give water companies a practical tool to forecast GAC bed life under different water qualities, reduce monitoring burden, and enable proactive, cost
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will deliver a model that integrates water quality to predict PFAS behaviour in GAC filters. The model will give water companies a practical tool to forecast GAC bed life under different water qualities
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. Overview The global methanol market demand has increased rapidly and is forecasted to grow to over 130 MMT by 2030. Compared to hydrogen and ammonia as low-carbon fuel options for long distance maritime
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maintenance. Development of machine/deep learning methods to detect fault, provide early warning and reporting, and forecast lifetime trend of batteries, to support predictive maintenance and improve energy
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection