It is important to produce a modelling framework that can aid a company in selecting sufficient energy systems that can considering their energy demand over time, and their varying conditions, with the best way to provide this. Companies are required to make rapid changes over the next decade to meet UK and global commitments towards net zero. To achieve this, companies are facing large capital expenditures, with a huge impact on their operational costs. This work would provide the ability to guide selection of energy systems and optimisation to meet their requirements by producing tested models. This is a substantial change that energy companies face, and therefore the likelihood of companies not making optimal decisions is extremely high. Especially with the large capital cost involved, the impact of an un-optimised decision is large, and will have a long-term impact to the company, so it's critical that the tool is made available soon. The methodology must be built in a way that considers a huge variety of different processes, with the ability to re-use and recycle materials from the processes to ensure that these net-zero and lower energy requirements are met. The proposed methodology to carry out the research is optimisation and process modelling using python, aspen and gPROMS, along with machine learning tools to further develop modelling. This project would work in collaboration with several industry partners who are willing to provide data as case studies, to be used to develop the model and demonstrate the methodology.

This will work in collaboration with the Government Cabinet office and Go Science advisory board work that Joan Cordiner does on resilience for the UK.