Xiaobo (Agnes) Shen
Academic and research departments
天美传媒
My research project
Reducing Nitrous Oxide (N鈧侽) emission in sewage treatment works via a machine learning assisted framework and critical control point mapping for practical implementationThe UK鈥檚 commitment to achieving net-zero carbon emissions by 2050 places significant responsibility on the water industry, which consumes around 3% of the nation鈥檚 energy and has pledged to reach net-zero operational emissions by 2030. A major challenge in meeting this goal is managing nitrous oxide (N鈧侽) emissions at sewage treatment works, where complex biochemical pathways and limited real-world data make accurate prediction and control difficult.
This PhD research aims to bridge the gap between academic understanding and industry practice by identifying key process parameters, developing a machine-learning-assisted hybrid model, mapping critical control points for N鈧侽 emissions. The work will provide a practical decision-support framework to guide process design, operational strategies, and investment planning to reduce N鈧侽 emissions and support the industry鈥檚 net-zero ambitions.
The UK鈥檚 commitment to achieving net-zero carbon emissions by 2050 places significant responsibility on the water industry, which consumes around 3% of the nation鈥檚 energy and has pledged to reach net-zero operational emissions by 2030. A major challenge in meeting this goal is managing nitrous oxide (N鈧侽) emissions at sewage treatment works, where complex biochemical pathways and limited real-world data make accurate prediction and control difficult.
This PhD research aims to bridge the gap between academic understanding and industry practice by identifying key process parameters, developing a machine-learning-assisted hybrid model, mapping critical control points for N鈧侽 emissions. The work will provide a practical decision-support framework to guide process design, operational strategies, and investment planning to reduce N鈧侽 emissions and support the industry鈥檚 net-zero ambitions.
Sustainable development goals
My research interests are related to the following:
Publications
Side-stream partial nitritation-anammox (PN/A) process is energy efficient for nitrogen removal but highly sensitive to disturbances such as high organic loadings from anaerobic digestion (AD) liquor. How such disturbance affects downstream treatment performance and greenhouse gas (GHG) emissions in full-scale plants remains insufficiently understood. Here, a full-scale Anoxic-Aerobic-AD-PN/A wastewater treatment plant was compared during satisfied and disturbed PN/A operation. Under disturbed operation, the mainstream apparent liquid-phase TN removal rate decreased from 472.4 to 298.2鈥塳g-N/d and estimated N2O and CH4 emissions increased by 69.39鈥塳g-N2O/day and 10.99鈥塳g-CH4/day, respectively. Estimated mainstream GHG emissions rose from 61784.2 to 82768.9 CO2-eq.kg/day. A broader comparison including PN/A N2O estimates and biogas-electricity offset also suggested increased climate burden (58997.2 to 77742.1 CO2-eq鈥塳g/day). Metagenomic analysis showed marked reductions in the relative abundance of Ca. Brocadia and Nitrosomonas in PN/A, together with a strong depletion of anammox genes, hdh/hzo. Elevated Nitrosomonas-associated cytochrome P460 and higher abundances of nirK/norBC (assigned to Nitrosomonas and Nitrospira) in the Anoxic and Aerobic tanks were consistent with increased nitrifier-associated N2O-production potential. These findings indicate that disturbance of side-stream PN/A can propagate to the mainstream and increase estimated mainstream GHG emissions, highlighting the need to balance AD intensification with stable PN/A operation.