Project overview
Anaerobic digestion (AD) is a technology where microorganisms break down organic matter to produce biogas, thereby generating renewable energy from waste. Biogas can be combusted to produce electricity or purified and used as a substitute for natural gas (NG). Because it provides a carbon-neutral substitute for fossil fuels, while also preventing methane emissions at landfills by processing organic waste, AD is noted as an important part of the UK Net Zero Strategy: Build Back Greener.
This project aims to develop artificial intelligence (AI) tools to enable radical efficiency improvements in AD biogas production.
Currently, there are about 650 operational AD sites in the UK, which reduce UK greenhouse gas emissions by an estimated 1%. This contribution is meaningful, but modest in comparison to AD's potential. The fundamental roadblock at present is a lack of flexibility. Due to the complexities of predicting how different waste feedstocks and different microbial communities will interact under varying operating conditions, AD biogas producers must minimise risk by purchasing only the highest-quality, consistent feedstock, which may also be seasonal; any errors could result in long and costly downtimes. Thus, available waste streams are vastly under-utilised; feedstock prices are driven up, weakening the economic viability of AD biogas production; and limited feedstocks may need to be transported longer distances, increasing carbon emissions.
AI holds crucial promise for the optimisation and future expansion of AD biogas production. As an industry that does not have the central research capabilities of other large energy sectors, it furthermore presents exceptional challenges due to the complexities and inherent uncertainties across interacting chemical, biological, and - if reductions in total life-cycle emissions are to be achieved - environmental systems.
The project team therefore unites expertise in AI, process optimisation, systems microbiology, and life-cycle assessment to develop whole-systems decision-making tools informed by detailed sub-system modelling.
The outputs will include decision-making tools, specifically:
- a hybrid machine-learning digital twin of the biodigesters, based on novel mechanistic modelling approaches combined with process data from industrial partners and new experimental data from the project; and
- optimisation-based system models of other components of a site, to perform site-wide real-time optimisation through a multi-layer digital twin that includes economic and environmental indicators.
By linking the digital twin of the biodigester to feedstock procurement and downstream processes, it will be possible to quickly determine the impact of different feedstocks, their combinations, and their prices on biogas quality, while also tracking quantified environmental impacts across AD value chains in real-time and assessing negative emissions potential in future.
Increasing the flexibility of UK AD industry will expand waste markets and lower prices to grow the sector with more capacity, boost profits and productivity, and enhance the overall attractiveness of AD as an investment. Increasing biogas output will help lower UK dependence on foreign NG sources and lower overall emissions from the energy system.
The project is supported by partners from across the UK to ensure the aims and objectives can be met, to result in a step-change in the AD industry and position the UK as a global AD leader.
The knowledge, tools, and methods developed will be applicable in wastewater treatment, where AD is also used.
Beyond that, our AI approaches to systems biology will have potential for widespread application in bioprocessing sectors more generally, such as biopharmaceuticals, biofuels, food, and fermentation.
With our network of partners, we will explore potential commercialisation and licensing of our digital techniques to maximise impact and work across sectors toward the common goal of Net Zero.
This project aims to develop artificial intelligence (AI) tools to enable radical efficiency improvements in AD biogas production.
Currently, there are about 650 operational AD sites in the UK, which reduce UK greenhouse gas emissions by an estimated 1%. This contribution is meaningful, but modest in comparison to AD's potential. The fundamental roadblock at present is a lack of flexibility. Due to the complexities of predicting how different waste feedstocks and different microbial communities will interact under varying operating conditions, AD biogas producers must minimise risk by purchasing only the highest-quality, consistent feedstock, which may also be seasonal; any errors could result in long and costly downtimes. Thus, available waste streams are vastly under-utilised; feedstock prices are driven up, weakening the economic viability of AD biogas production; and limited feedstocks may need to be transported longer distances, increasing carbon emissions.
AI holds crucial promise for the optimisation and future expansion of AD biogas production. As an industry that does not have the central research capabilities of other large energy sectors, it furthermore presents exceptional challenges due to the complexities and inherent uncertainties across interacting chemical, biological, and - if reductions in total life-cycle emissions are to be achieved - environmental systems.
The project team therefore unites expertise in AI, process optimisation, systems microbiology, and life-cycle assessment to develop whole-systems decision-making tools informed by detailed sub-system modelling.
The outputs will include decision-making tools, specifically:
- a hybrid machine-learning digital twin of the biodigesters, based on novel mechanistic modelling approaches combined with process data from industrial partners and new experimental data from the project; and
- optimisation-based system models of other components of a site, to perform site-wide real-time optimisation through a multi-layer digital twin that includes economic and environmental indicators.
By linking the digital twin of the biodigester to feedstock procurement and downstream processes, it will be possible to quickly determine the impact of different feedstocks, their combinations, and their prices on biogas quality, while also tracking quantified environmental impacts across AD value chains in real-time and assessing negative emissions potential in future.
Increasing the flexibility of UK AD industry will expand waste markets and lower prices to grow the sector with more capacity, boost profits and productivity, and enhance the overall attractiveness of AD as an investment. Increasing biogas output will help lower UK dependence on foreign NG sources and lower overall emissions from the energy system.
The project is supported by partners from across the UK to ensure the aims and objectives can be met, to result in a step-change in the AD industry and position the UK as a global AD leader.
The knowledge, tools, and methods developed will be applicable in wastewater treatment, where AD is also used.
Beyond that, our AI approaches to systems biology will have potential for widespread application in bioprocessing sectors more generally, such as biopharmaceuticals, biofuels, food, and fermentation.
With our network of partners, we will explore potential commercialisation and licensing of our digital techniques to maximise impact and work across sectors toward the common goal of Net Zero.
Staff
Lead researchers
