Improved Energy Efficiency of Float Glass Production

This project aims to develop and exploit modern advancements in machine learning to improve the efficiency of float glass production. It is part of a suite of activities that the NSG Group is undertaking to simultaneously improve product quality and reduce carbon emissions. The outcomes of this project will decrease the overall carbon footprint (reduced fuel consumption and GHG emissions) of the float glass process and therefore the environmental impact of products such as energy-efficient glazing (insulating and solar control), solar energy substrates, and lightweight glass for automotive applications. These improvements will help to fight climate change inherent in furnace operations and enable cheaper and more flexible production of energy, establish a lower carbon footprint and create value-added products.

Dr Peter Green

University of Liverpool

Email: P.L.Green@liverpool.ac.uk

Dr Green obtained a PhD in mechanical engineering at the University of Sheffield in 2012, before becoming a lecturer at the University of Liverpool in 2015. He is currently a senior lecturer, also at the University of Liverpool. Dr Green’s background is in structural dynamics, and the development of models whose parametric uncertainty can be analysed using Bayesian approaches. This has
since been expanded into the general development of machine learning (a.k.a. AI) models for engineering applications. His fundamental research concerns the development of data-based models that facilitate uncertainty quantification and are scalable to large datasets.