Machine learning-based optimization of enhanced nitrogen removal in a full-scale urban wastewater treatment plant with ecological combination ponds.

Two mathematical models were used to optimize the performance of a full-scale biological nutrient removal (BNR) activated treatment plant, a plug-flow bioreactors operated in a 3-stage phoredox process configuration, anaerobic anoxic oxic (A2/O). The ASM2d implemented on the platform of WEST2011 software and the BioWin activated sludge/anaerobic digestion (AS/AD) models were used in this study with the aim of consistently achieving the designed effluent criteria at a low operational cost. Four ASM2d parameters (the reduction factor for denitrification \((\eta _{NO_3 H} )\), the maximum growth rate of heterotrophs (µH), the rate constant for stored polyphosphates in PAOs (qpp), and the hydrolysis rate constant (kh)) were adjusted. Whereas three BioWin parameters (aerobic decay rate (bH), heterotrophic dissolved oxygen (DO) half saturation (KOA), and YP/acetic) were adjusted. Calibration of the two models was successful; both models have average relative deviations (ARD) less than 10% for all the output variables. Low effluent concentrations of nitrate nitrogen (N-NO3), total nitrogen (TN), and total phosphorus (TP) were achieved in a full-scale BNR treatment plant having low influent chemical oxygen demand (COD) to total Kjeldahl nitrogen (TKN) ratio (COD/TKN). The effluent total nitrogen and nitrate nitrogen concentrations were improved by 50% and energy consumption was reduced by approximately 25%, which was accomplished by converting the two-pass aerobic compartment of the plug-flow bioreactor to anoxic reactors and being operated in an alternating mode. Findings in this work are helpful in improving the operation of wastewater treatment plant while eliminating the cost of external carbon source and reducing energy consumption.

Development and application of an intelligent nitrogen removal diagnosis and optimization framework for WWTPs: Low-carbon and stable operation

Field-scale application of spent sulfidic caustic as a source of alternative electron donor for autotrophic denitrification

A novel two-stage anoxic/oxic-moving bed biofilm reactor process for biological nitrogen removal in a full-scale municipal WWTP: Performance and bacterial community analysis

The objective of this study was to assess the role of cyclic aeration, vegetation, and temperature on nitrogen removal by subsurface-flow engineered wetlands. Aeration was shown to enhance total nitrogen and ammonia removal and to enhance removal of carbonaceous biochemical oxygen demand, chemical oxygen demand, and phosphorus. Effluent ammonia and total nitrogen concentrations were significantly lower in aerated wetland cells when compared with unaerated cells. There was no significant difference in nitrogen removal between planted and unplanted cells. Effluent total nitrogen concentrations ranged from 9 to 12 mg N/L in the aerated cells and from 23 to 24 mg N/L in unaerated cells. Effluent ammonia concentrations ranged from 3 to 7 mg N/L in aerated wetland cells and from 22 to 23 mg N/L in unaerated cells. For the conditions tested, temperature had only a minimal effect on effluent ammonia or total nitrogen concentrations. The tanks-in-series and the PkC models predicted the general trends in effluent ammonia and total nitrogen concentrations, but did not do well predicting short-term variability. Rate coefficients for aerated systems were 2 to 10 times greater than those for unaerated systems.

The low carbon–nitrogen ratio (C/N) of influent wastewater results in the insufficient carbon source for the process of denitrification in urban wastewater treatment plants (WWTPs). A denitrifying bacterial strain, Pseudomonas sp. JMSTP, was isolated and demonstrated effective denitrification ability under a low C/N ratio of 1.5–4 (w/w) in anoxic conditions. Sequencing batch reactor (SBR) studies were conducted to test the bioaugmentation of JMSTP on total nitrogen (TN) removal under the influent COD/N ratio of 3/1. After the second bioaugmentation, the TN of effluent in the bioaugmented SBR was significantly lower than that in the control SBR. Redundancy analysis results showed that there was a positive correlation between Pseudomonas sp. abundance and TN removal in the bioaugmented SBR. Microbial community analysis showed that, especially after the second bioaugmentation, the abundance of Pseudomonas sp. decreased rapidly, but it was still much higher than that in the control SBR. Correlation network analysis showed that after the addition, Pseudomonas sp. had no significant co-occurrence relationship with other native bacteria, owing to the quick increase and decrease. Our results suggest that JMSTP shows the potential to enhance TN removal through bioaugmentation. Since the effect of bioaugmentation gradually diminishes, further research is still needed to investigate its long-lasting applications.

Occurrence, fate, and ecological risk of PAEs, PFASs, antibiotics in industrial, urban and rural WWTPs in Shaanxi Province, China

Differences of bacterial communities in two full-scale A2/O municipal wastewater treatment plants and their effects on effluent total nitrogen removal

Wastewater treatment plants (WWTPs) play a crucial role in modern urban water environmental protection. However, they face challenges related to high operational costs and carbon emissions. This study focused on addressing these issues through an analysis of four urban WWTPs in Jiujiang city, China. The study involved comparing the size and processes of the plants, evaluating influent and effluent water quality, assessing energy consumption and chemical usage, and calculating both direct and indirect carbon emissions. The results demonstrated that the high operational costs and increased carbon emissions in these WWTPs were primarily attributed to low hydraulic loadings, low influent concentration, and high energy and chemical consumption. In response, three targeted scenarios were proposed to enhance the efficiency of the WWTPs and reduce carbon emissions. These scenarios involved adjusting the amount of wastewater imported into the WWTPs to meet the designed capacity, optimizing operating costs, or combining both approaches. Among the scenarios, Scenario 3 emerged as the most effective in terms of improving efficiency and reducing carbon emissions. The operational costs for WWTPs could be reduced in the range of 0.42–1.04 RMB/m3, representing a reduction rate of 35%–57%. Additionally, carbon emissions could be lowered from 15.02 to 598.85 gCO2e/m3, corresponding to a reduction of 2.91%–41.38%. Although Scenario 2 exhibited a lower carbon emission reduction of 14.8–316.33 gCO2e/m3, it was identified as the most feasible and easily implementable high-efficiency solution at present, with a reduction in operational costs ranging from 0.43 to 1.31 RMB/m3. To achieve zero energy consumption and zero carbon emissions in wastewater treatment in the future, it is recommended to undertake additional measures, such as enhancing dosing system accuracy, implementing tail gas collection, adopting photovoltaic power generation, implementing carbon sequestration techniques, and exploring wastewater heat source recycling. These findings provide valuable insights for optimizing the operational efficiency of urban WWTPs, reducing carbon emissions, and promoting sustainable wastewater treatment practices in Jiujiang city, China.

Performance evaluation of eight years experience of constructed wetland systems in Catalonia as alternative treatment for small communities

Effluent ammonia-nitrogen (NH3-N), chemical oxygen demand (COD) and total nitrogen (TN) removals are the most common environmental and process performance indicator for all types of wastewater treatment plants (WWTPs). In this paper, a soft computing approach based on the back propagation (BP) neural networks and fuzzy-rough sets (FR-BP) has been applied for forecasting effluent NH3-N, COD and TN concentration of a real WWTP, in which the fuzzy-rough sets theory is employed to perform input selection of neural network which can reduce the influence due to the drawbacks of BP such as low training speed and easily affected by noise and weak interdependency data. The model performance is evaluated with statistical parameters and the simulation results indicates that the FR-BP modeling approach achieves much more accurate predictions as compared with the other traditional modeling approaches.

Does stricter sewage treatment targets policy exacerbate the contradiction between effluent water quality improvement and carbon emissions mitigation? An evidence from China

Model-based strategy for nitrogen removal enhancement in full-scale wastewater treatment plants by GPS-X integrated with response surface methodology

Advanced nitrogen (N)-removal onsite wastewater treatment systems (OWTS) are installed in coastal areas throughout the USA to reduce N loading to groundwater and marine waters. However, final effluent total nitrogen (TN) concentration from these systems is not always routinely monitored, making it difficult to determine the extent to which they contribute to N loads. We monitored the final effluent TN concentration of 42 advanced N-removal OWTS within the Greater Narragansett Bay Watershed, Rhode Island between March 2015 and August 2016. The compliance rate with the State of Rhode Island final effluent standard (TN ≤ 19 mg N/L) was 64.3, 70.6, and 75.0% for FAST, Advantex, and SeptiTech systems, respectively. The median (range) final effluent TN concentration (mg N/L) was 11.3 (0.1–41.6) for SeptiTech, 14.9 (0.6–61.6) for Advantex, and 17.1 (0.6–104.9) for FAST systems. Variation in final effluent TN concentration was not driven by temperature; TN concentrations plotted against effluent temperature values resulted in R 2 values of 0.001 for FAST, 0.007 for Advantex, and 0.040 for SeptiTech systems. The median effluent TN concentration for all the systems in our study (16.7 mg N/L) was greater than reported for Barnstable County, MA systems (13.3 mg N/L), which are monitored quarterly. Depending on technology type, ammonium (NH4 +), nitrate (NO3 −), alkalinity, forward flow, biochemical oxygen demand (BOD), and effluent temperature best predicted effluent TN concentrations. Service providers made adjustments to seven underperforming systems, but TN was reduced to 19 mg N/L in only two of the seven systems. Advanced N-removal OWTS can reduce TN to meet regulations, and monitoring of these systems can enable service providers to proactively manage systems. However, improvement of performance may require recursive adjustments and long-term monitoring.

The operation of wastewater treatment plants (WWTPs) is frequently characterized by complexity, largely attributable to the properties of the influent and the nonlinear fluctuations that occur throughout the wastewater treatment process. Accurate modeling of wastewater quality within WWTPs is essential for informed decision-making. In this research, we utilized a stacking model to amalgamate five foundational models, thereby enhancing the precision of the total nitrogen (TN) prediction model for effluent. This methodology mitigates the inherent risk of overfitting associated with individual base models while preserving robust predictive capabilities in relation to feature inputs and intricate influent conditions. Following the integration of the models, the coefficient of determination (R2) for the stacking model achieved a value of 0.90. Furthermore, through SHAP analysis, we elucidated the model and identified the parameters that exert the most significant influence on the prediction of effluent TN in WWTPs, notably electricity, Inf_BOD5, Inf_TN, and MLSS. To further augment the model's applicability in optimizing effluent TN, we performed simulations by adjusting the controllable parameter MLSS to forecast effluent TN. The findings indicate a correlation between increased MLSS concentration and reduced effluent TN, with the predicted trends facilitating the analysis of scenarios involving elevated effluent TN concentrations. This, in turn, offers valuable engineering insights for the reduction of effluent TN in wastewater treatment facilities.