Optimisation of substrate characterisation for improving monitoring and facilitating the modelling application in the anaerobic digestion of food waste
- Project lead
- Gina Javanbakht
- Institute
- University of Huddersfield
Summary
The UK is scaling up biomethane production by increasing the number of anaerobic digestion (AD) plants, with food waste being a key feedstock. Anaerobic digestion is a sustainable process that converts organic waste into biogas and nutrient-rich digestate, but its efficiency can be hindered by limited knowledge about the characteristics of the feedstock, especially food waste. This project focuses on optimizing food waste characterization and enhancing the predictive accuracy of biogas production models to make AD processes more efficient and reliable.
A critical component of the project is the adaptation and validation of the ADM1 (Anaerobic Digestion Model No. 1), a widely recognized mathematical framework for simulating biogas production. Historically used for sewage sludge, ADM1 faces challenges when applied to food waste due to limited data on the feedstock’s composition. To address this, the project combines experimental research using a specialized AD unit and advanced computational modelling to improve understanding of how variables like additives and feedstock composition influence biogas production. By integrating experimental validation and model-based optimization, the project aims to transform AD systems into more efficient, scalable, and sustainable solutions for energy production and waste management. These advancements support the UK’s goals for renewable energy and circular economy practices.
Aims:
Biogas will play a crucial role in tackling climate change and replacing fossil fuels. Digital tools based on modelling can ensure a more efficient and resilient process; however, proper characterization of substrates remains a challenge when using models. This project aims at optimising a procedure for the characterisation of food waste as a substrate for anaerobic digestion while maximising the accuracy of the model prediction of the biogas production and the quality of the digestate.
Outcomes:
A simplified methodology for food waste characterization has been successfully adapted from a well-established wastewater treatment framework. The method requires only a few standard measurements commonly used in anaerobic digestion (AD) feedstock monitoring, making it efficient and accessible. Experimental data from the MyGug AD unit were used to validate the approach, confirming strong agreement between measured and simulated results using Modela’s ADM1 platform. This enables accurate prediction of biogas yield and digestate quality with minimal laboratory input, providing a foundation for developing digital co-pilot tools to optimise AD performance in both research and practical applications.
Impact:
The developed tool enhances the accuracy of anaerobic digestion model predictions based on food waste composition, enabling virtual prototyping and informed process optimisation. This supports more efficient, resilient, and sustainable biogas production, reduces reliance on trial-and-error experimentation, and facilitates the practical deployment of digital co-pilot systems in AD operations.
Academic partner:Gina Javanbakht, University of Huddersfield
Industrial partner: Dr. Andrés Donoso-Bravo, Modela Limited
