Fenghao Cui

Development of modified thermodynamic electron equivalents model for partial nitrification: mechanistic insight into ammonium- and nitrite-oxidising bacteria selectivity

While municipal wastewater treatment plants (MWTPs) contribute to greenhouse gas emissions, they also offer opportunities to reduce net carbon release through process integration. This study assessed plant-wide GHG emissions and biological CO2 mitigation potential in a full-scale MWTP treating combined domestic and industrial wastewater. Long-term operational data, laboratory experiments, and stoichiometric modelling were integrated to quantify CH4, CO2, and N2O emission factors and clarify carbon and nitrogen transformation pathways. Aerobic biological oxidation was the primary direct CO2 source, with emission factors of 0.49–0.61 kg CO2/kg COD that closely matched stoichiometric predictions. Carbon redirection from the liquid line to the sludge line reduced aerobic CO2 emissions and increased methane-rich biogas recovery (67% CH4). Anaerobic digestion and sludge storage were major CH4 sources but also key mitigation opportunities when gas capture and utilization were efficient. N2O showed high potential under low-DO and transient laboratory conditions but remained negligible at full scale under stable operation. The results indicate that plant-wide biological optimization can reduce CO2-equivalent emissions without major infrastructure upgrades and support low-carbon, energy-positive MWTP operation.

A modified thermodynamic electron equivalents model (TEEM) was developed to quantify the bioenergetics of partial nitrification in a sequencing batch reactor. Unlike conventional empirical models, the modified TEEM introduces a dynamic electron transfer efficiency that links microbial growth yields and stoichiometries to free ammonia (FA), free nitrous acid (FNA), dissolved oxygen (DO), and temperature. The model was calibrated and validated against experimental data and reproduced the profiles of ammonium, nitrite, and nitrate with strong agreement (R2 > 0.9, normalized root mean squared error < 0.1). Thermodynamic simulations showed that ammonium-oxidizing bacteria (AOB) maintained a higher ETE than nitrite-oxidizing bacteria (NOB) over a defined operating range, which promoted AOB enrichment and stable nitrite accumulation. The simulations also identified an optimal sludge retention time (SRT) of approximately 40 days and a thermodynamic selectivity window governed by FA and FNA. These results provide a mechanistic explanation for selective NOB suppression and demonstrate that the modified TEEM can support the prediction and optimization of energy-efficient partial nitrification systems.

A modified thermodynamic electron equivalents model (TEEM) is presented to describe the bioenergetics of partial nitrification in a sequencing batch reactor in a mechanistic way. Unlike conventional empirical models, the modified TEEM dynamically links microbial growth yields and stoichiometries to environmental factors by introducing a variable electron transfer efficiency (ETE). This efficiency depends on free ammonia (FA), free nitrous acid (FNA), dissolved oxygen, and temperature. The model is calibrated and validated using experimental data, demonstrating strong agreement with ammonium, nitrite, and nitrate profiles (R2 > 0.9). Thermodynamic simulations indicate that ammonium-oxidising bacteria (AOB) maintain a higher ETE (17~21%) compared to nitrite-oxidising bacteria (NOB) (0.5~1.5%). This disparity results in the selective enrichment of AOB at an optimal sludge retention time of ~40 days. Parametric analysis reveals that concentrations of FA and FNA critically control ETE and the energy-coupling ratio. This defines a thermodynamic selectivity window in which AOB remain active while NOB are suppressed. These findings demonstrate that the modified TEEM provides a robust mechanistic framework for predicting nitrification performance and process stability based on environmental energetics, offering new insights for designing energy-efficient biological nitrogen removal systems.

Accurate biomass concentration measurement is essential for modelling and optimizing activated sludge processes in biological wastewater treatment. This study introduces laser speckle imaging (LSI), a non-invasive optical method traditionally used in biomedical applications, as a soft-sensing technique for estimating biomass concentration in activated sludge systems. Quantitative relationships between LSI-derived speckle decorrelation indices and biomass concentrations were established by analyzing temporal autocorrelation of speckle intensity patterns. The LSI-derived biomass estimates were subsequently integrated into conventional Monod biokinetic models, enabling indirect estimation of key parameters including maximum substrate utilization rate (qmax = 2.5 d⁻¹), half-saturation coefficient (KS = 42 mg/L), yield coefficient (Y = 0.38 g VSS/g BOD), and endogenous decay coefficient (b = 0.05 d⁻¹). These values align well with conventional physicochemical analyses and literature ranges, confirming the feasibility of LSI as an optical biomass sensor for wastewater treatment. The findings demonstrate that LSI captures concentration-dependent optical scattering dynamics related to floc motion and structural characteristics that complement bulk measurements. As a calibration-based soft-sensing tool used alongside conventional methods, the LSI technique offers potential for enhanced monitoring of wastewater treatment systems under controlled conditions, with further validation required for diverse operational environments.

This study introduced an alternative shortcut biological nitrogen removal (SBNR) process for landfill leachate treatment by developing a novel hydrodynamic sequencing batch reactor (H-SBR). The reactor could enhance the oxygen transfer rate (OTR) and nitrite accumulation ratio (NAR) by modifying internal hydrodynamic turbulence intensity. The average chemical oxygen demand (COD) and total nitrogen (TN) concentrations introduced into the reactor were 660 and 250 mg L⁻¹, respectively, and the average removal efficiencies were 93% (COD) and 96% (TN). The effect of geometric parameters on oxygen transfer was estimated by performing a hydrodynamic model and a nonlinear least square analysis. After correcting the constants (α and β) of mass transfer coefficients (K_La) to values of 0.7361 and 1.2639, the model data fit the experiment well with an R-squared value of 0.99. The OTR improved by up to 30%, and hence, increased the NAR by up to 20% with a reduction of about 0.5 kg N kW⁻¹ for power efficiency. The H-SBR development is innovative because the oxygen transfer efficiency was improved by the hydrodynamic modification of internal turbulence intensity, although not by mechanical equipment or chemical supplements. For the SBNR process, the modification of the reactor configuration for OTR enhancement could significantly improve nitrogen removal efficiency with successful nitrite accumulation. In addition to landfill leachate treatment, the H-SBR process can be employed in the treatment of low C/N ratio wastewaters.