Rapid digitalisation of bioenergy for higher efficiency and profit

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.