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) 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 B) 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 licencing of our digital techniques to maximise impact and work across sectors toward the common goal of Net Zero.

Methane causes 30% of today's man-made global warming, but our understanding of industrial emissions across different regions and sectors is critically lacking: this makes it difficult to reduce emissions at pace to meet climate targets. Methane emissions are hard to measure, partly because only small emissions are needed to cause strong climate impacts. Historically we have relied on 'bottom-up' source-level quantification methods, e.g. using optical gas imaging cameras to detect and quantify leaks from equipment or applying emission factors to known exhaust flow rates. But with these methods there is a risk of underrepresentation, particularly not accounting for the leaks that we don't know about. More recently there has been an increased focus on conducting site-level methane monitoring rather than source-level. These typically involve measuring concentrations of methane from the local atmosphere and then estimating an equivalent emission rate needed to reach these concentrations. Methane sensors can be placed on drones to collect data sufficient to monitor relatively large site boundaries, but there are several limitations that reduce the effectiveness of emissions quantifications from this approach. High estimation uncertainties. There exist many points of uncertainty in the emissions quantification method, including from the methane and weather measurement sensors, and assumptions made in the estimation method such as constant weather and emission conditions over time and space. High cost. This method requires both equipment cost but also high labour cost associated with flight and monitoring expertise. High failure rates. The high labour cost is exacerbated by the high failure rates of current systems: weather conditions must be within the window to fly (wind speed range, cloud height, no rain, daylight) leading to long resource waiting times. This project aims to produce a step-change in UAV methane monitoring to reduce uncertainty and address the cost/uncertainty trade-off. To do this, we will develop an automated, multi-drone monitoring system that characterises methane and meteorological characteristics in much more detail over both time and space. The system will be reactive to what the drones are measuring to optimise their movement. We will then produce a downstream emissions estimation method that uses this multi-drone data to drive down uncertainties. Three potential technical options for design are envisaged, in which one or a combination will be taken to design and test phases: improved wind mapping over spatial and temporal scales; the use of a partner receiver drone for an open path tuneable diode sensor; and triangulating emissions with multiple lower cost methane sensors. Our team combines methane measurement experts, industrial engineers and drone swarm robotics experts to design, build, test and optimise a system to prototype stage. We also have project partners that include key users and routes to commercialisation so that we can maximise impact and the pace of impact. This new monitoring system will place the UK at the forefront of methane measurement at a time where the UK, EU, US and Australia are increasing emissions monitoring stringency for industrial sites relating to oil and gas, coal, biogas, landfill, water treatment and landfill: all key sources of methane emissions. Enhanced monitoring will give us tools to provide rapid emission reductions, where methane is one of the quickest routes to slowing global warming due to its potency and short atmospheric lifespan.

The Supergen Bioenergy Hub will bring together academic, industrial an policy stakeholders to focus on sustaianable bioenergy systems. It will adopt an interdisciplinary approach focused on key innovation stages. Research at UK universities will generate new knowledge and insights in sustainable bioenergy, while incubating UK science to deliver its commerical potential and working with researchers to ensure their knowledge is diffused across the innovation community for wider benefit. This will deliver impact with policy makers via our well-established policy connections and a focused policy-makers only forum to address their key concerns. It will deliver impact with industrialists via an industry forum that will connect innovators with UK scientists and engineers who can support them. It will deliver impact with the wider sustainable energy and product community by establishing a professional forum which will support training of commercial professionals and key knowledge transfer in new knowledge areas. Above all it will foster stronger connections between the academic, industrial and policy sectors in a way that supports advancement of sustainable bioenergy in the U.K.