Bioenergy is an important part of the UK’s energy mix with over 5,200 GWh of renewable energy produced in 2020 by the >650 anaerobic digestion (AD) sites alone. Due to the complexity and variability of feedstocks and processes, bioenergy sites typically rely on simple models to assess whether a feedstock (agricultural residue or food waste) is suitable and affordable, generally focusing solely on maximising energy output with minimal risk of process upsets. To make optimal real-time decisions that could allow for higher process flexibility, an integrated whole-site model is required that can produce highly accurate predictions of future consequences of decisions, which can be solved using optimisation-based methods in time to aid decisions and enhance process control. Unlocking enhanced flexibility via uncertainty-aware predictive modelling of complex bioenergy processes, simultaneously with the up- and downstream processes and supply chains would be transformative for the sector.
In this project, we will deliver a roadmap and open-source toolkit for rapid digital twinning for bioenergy process site-wide optimisation/control to increase flexibility and efficiency. We will flexibly support our industry partners and projects, co-creating real-time optimisation solutions based on modelling strategies to be co-developed throughout the project. Examples of the models to be developed include:
- Real-time supply chain optimisation models
- Models for optimal storage and scheduling of feedstocks
- Co-digestion strategies for anaerobic digestion
- Process control using hybrid artificial intelligence
- First-principles models incorporating uncertainty
- Integrated decision-making platforms for real-time optimal decision-making.
This project is led by Michael Short.