Control strategies for nitrous oxide emissions reduction on wastewater treatment plants operation

N2O recovery from wastewater and flue gas via microbial denitrification: Processes and mechanisms

Nitrous oxide (N2O) is increasingly regarded as a significant greenhouse gas implicated in global warming and the depletion of the ozone layer, yet it is also recognized as a valuable resource. This paper comprehensively reviews innovative microbial denitrification techniques for recovering N2O from nitrogenous wastewater and flue gas. Critical analysis is carried out on cutting-edge processes such as the coupled aerobic-anoxic nitrous decomposition operation (CANDO) process, semi-artificial photosynthesis, and the selective utilization of microbial strains, as well as flue gas absorption coupled with heterotrophic/autotrophic denitrification. These processes are highlighted for their potential to facilitate denitrification and enhance the recovery rate of N2O. The review integrates feasible methods for process control and optimization, and presents the underlying mechanisms for N2O recovery through denitrification, primarily achieved by suppressing nitrous oxide reductase (Nos) activity and intensifying competition for electron donors. The paper concludes by recognizing the shortcomings in existing technologies and proposing future research directions, with an emphasis on prioritizing the collection and utilization of N2O while considering environmental sustainability and economic feasibility. Through this review, we aim to inspire interest in the recovery and utilization of N2O, as well as the development and application of related technologies.

Mitigating greenhouse gas emissions from municipal wastewater treatment in China

Municipal wastewater treatment plays an indispensable role in enhancing water quality by eliminating contaminants. While the process is vital, its environmental footprint, especially in terms of greenhouse gas (GHG) emissions, remains underexplored. Here we offer a comprehensive assessment of GHG emissions from wastewater treatment plants (WWTPs) across China. Our analyses reveal an estimated 1.54 (0.92-2.65) × 104 Gg release of GHGs (CO2-eq) in 2020, with a dominant contribution from N2O emissions and electricity consumption. We can foresee a 60-65% reduction potential in GHG emissions with promising advancements in wastewater treatment, such as cutting-edge biological techniques, intelligent wastewater strategies, and a shift towards renewable energy sources.

Assessing greenhouse gas emissions from the printing and dyeing wastewater treatment and reuse system: Potential pathways towards carbon neutrality

The urgency of achieving carbon neutrality needs a reduction in greenhouse gas (GHG) emissions from the textile industry. Printing and dyeing wastewater (PDWW) plays a crucial role in the textile industry. The incomplete assessment of GHG emissions from PDWW impedes the attainment of carbon neutrality. Here, we firstly introduced a more standardized and systematic life-cycle GHG emission accounting method for printing and dyeing wastewater treatment and reuse system (PDWTRS) and proposed possible low-carbon pathways to achieve carbon neutrality. Utilizing case-specific operational data over 12 months, the study revealed that the PDWTRS generated 3.49 kg CO2eq/m3 or 1.58 kg CO2eq/kg CODrem in 2022. This exceeded the GHG intensity of municipal wastewater treatment (ranged from 0.58 to 1.14 kg CO2eq/m3). The primary contributor to GHG emissions was energy consumption (33 %), with the energy mix (sensitivity = 0.38) and consumption (sensitivity = 0.33) exerting the most significant impact on GHG emission intensity respectively. Employing prospective life cycle assessment (LCA), our study explored the potential of the anaerobic membrane bioreactor (AnMBR) to reduce emissions by 0.54 kg CO2eq/m3 and the solar-driven photocatalytic membrane reactor (PMR) to decrease by 0.20 kg CO2eq/m3 by 2050. Our projections suggested that the PDWTRS could achieve net-zero emissions before 2040 through an adoption of progressive transition to low-carbon management, with a GHG emission intensity of -0.10 kg CO2eq/m3 by 2050. Importantly, the study underscored the escalating significance of developing sustainable technologies for reclaimed water production amid water scarcity and climate change. The study may serve as a reminder of the critical role of PDWW treatment in carbon reduction within the textile industry and provides a roadmap for potential pathways towards carbon neutrality for PDWTRS.

Direct and indirect monitoring methods for nitrous oxide emissions in full-scale wastewater treatment plants: A critical review

Mitigation of nitrous oxide (N2O) emissions in full-scale wastewater treatment plant (WWTP) has become an irreversible trend to adapt the climate change. Monitoring of N2O emissions plays a fundamental role in understanding and mitigating N2O emissions. This paper provides a comprehensive review of direct and indirect N2O monitoring methods. The techniques, strengths, limitations, and applicable scenarios of various methods are discussed. We conclude that the floating chamber technique is suitable for capturing and interpreting the spatiotemporal variability of real-time N2O emissions, due to its long-term in-situ monitoring capability and high data acquisition frequency. The monitoring duration, location, and frequency should be emphasized to guarantee the accuracy and comparability of acquired data. Calculation by default emission factors (EFs) is efficient when there is a need for ambiguous historical N2O emission accounts of national-scale or regional-scale WWTPs. Using process-specific EFs is beneficial in promoting mitigation pathways that are primarily focused on low-emission process upgrades. Machine learning models exhibit exemplary performance in the prediction of N2O emissions. Integrating mechanistic models with machine learning models can improve their explanatory power and sharpen their predictive precision. The implementation of the synergy of nutrient removal and N2O mitigation strategies necessitates the calibration and validation of multi-path mechanistic models, supported by long-term continuous direct monitoring campaigns.

Multiobjective Operation Optimization of Wastewater Treatment Process Based on Reinforcement Self-Learning and Knowledge Guidance

This article proposes a multiobjective operation optimization method based on reinforcement self-learning and knowledge guidance for quality assurance and consumption reduction of wastewater treatment process (WWTP) with nonstationary time-varying dynamics. First, operation optimization models are developed by online sequential random vector functional-link (OS-RVFL) neural network, which can realize online sequential learning of model parameters. Then, a knowledge base is established to store typical optimization cases for knowledge guiding the subsequent optimizations. Based on it, a reinforcement self-learning-based multiobjective particle swarm optimization (RSL-MOPSO) algorithm is proposed to perform optimization calculation. In this algorithm, reinforcement self-learning is used for interaction learning between environment and action in optimization, and the particle motion trend of algorithm is adjusted according to the feedback information of the optimization process. The effects of wastewater state parameters on particles are recorded and reused to improve the solution quality and calculation efficiency of optimization. Moreover, to make good use of the information of the previous optimizations and balance the coordination between global search in the early stage and local search in the later stage, a selective information feedback mechanism is further proposed to ensure the diversity and convergence of the algorithm. Finally, prediction-based intelligent decision making is performed to select the final optimization solution as the final setpoints for the lower-level controllers from the Pareto frontier with considering specific technical requirements. Data experiments show that the proposed method can effectively reduce energy consumption and ensure effluent quality.

Automatic control and optimal operation for greenhouse gas mitigation in sustainable wastewater treatment plants: A review

In order to promote low-carbon sustainable operational management of the wastewater treatment plants (WWTPs), automatic control and optimal operation technologies, which devote to improving effluent quality, operational costs and greenhouse gas (GHG) emissions, have flourished in recent years. There is no consensus on the design procedure for optimal control/operation of sustainable WWTPs. In this review, we summarize recent researches on developing control and optimization strategies for GHG mitigation in WWTPs. Faced with the fact that direct carbon dioxide (CO₂) emissions (considered biological origin) are generally not included in the carbon footprint of WWTPs, direct emissions (nitrous oxide (N₂O), methane (CH₄)) and indirect emissions are paid much attention. Firstly, the plant-wide models with GHG dynamic simulation, which are employed to design and evaluate the automatic control schemes as well as representative studies on identifying key factors affecting GHG emissions or comprehensive performance are outlined. Then, both traditional and advanced control methods commonly used in GHG mitigation are reviewed in detail, followed by the multi-objective optimization practices of control/operational parameters. Based on the mentioned control and (or) optimization strategies, a novel design framework for the optimal control/operation of sustainable WWTPs is proposed. The findings and design framework proposed in the paper will provide guidance for GHG mitigation and sustainable operation in WWTPs. It is foreseeable that more accurate and appropriate plant-wide models together with flexible control methods and intelligent optimization strategies will be developed to satisfy the upgrading requirements of WWTPs in the future.

The trade-off between N2O emission and energy saving through aeration control based on dynamic simulation of full-scale WWTP

This study investigated the trade-off between energy saving and N₂O emission reduction of WWTP under the precise control of dissolved oxygen (DO) concentration through model simulation. A long-term dynamic model for full-scale WWTP GHG emissions was established and calibrated with monitored year-round hourly water quality data to quantify the annual GHG emissions from WWTP. Results showed that N₂O dominated the direct emission, up to 76.1%, and the variability of N₂O generation could better be revealed by dynamic simulation. Furthermore, GHG emissions of the WWTP were mainly contributed by electric energy, among which the blower consumes the most electricity. To reduce the electricity consumption of blowers, improve mechanical efficiency and reduce DO concentration should be considered. DO setting played a significant role in the N₂O and CH₄ emission, electricity consumption and effluent quality, which was challenging to balance. The ultralow-oxygen (0-1/0.2-1 mg/L) and low oxygen (1-2 mg/L) control strategies were proposed, and their effects on total GHG emissions and effluent water quality were discussed. If the anaerobic environment (DO<0.2 mg/L)could be avoided, the control frequency (high and low) of the DO set-point did not have a significant effect on the emissions of N₂O and CH₄ and the effluent quality. The ultralow-oxygen strategy (0.2-1 mg/L) with a high-frequency control strategy achieved the lowest GHG emissions under the current energy mix. However, by 2050, as the energy supply gets cleaner, the total GHG emissions of WWTPs with ultralow-oxygen aeration (0.2-1 mg/L) will exceed low-oxygen aeration by 3.6%-4.2%, as N₂O dominates 61.6%. Therefore, considering the trade-off between N₂O emission and energy saving in WWTP, ultralow-oxygen aeration is a transition scheme to cleaner energy.

A plant-wide model describing GHG emissions and nutrient recovery options for water resource recovery facilities

In this study, a plant-wide model describing the fate of C, N and P compounds, upgraded to account for (on-site/off-site) greenhouse gas (GHG) emissions, was implemented within the International Water Association (IWA) Benchmarking Simulation Model No. 2 (BSM2) framework. The proposed approach includes the main biological N₂O production pathways and mechanistically describes CO₂ (biogenic/non-biogenic) emissions in the activated sludge reactors as well as the biogas production (CO₂/CH₄) from the anaerobic digester. Indirect GHG emissions for power generation, chemical usage, effluent disposal and sludge storage and reuse are also included using static factors for CO₂, CH₄ and N₂O. Global and individual mass balances were quantified to investigate the fluxes of the different components. Novel strategies, such as the combination of different cascade controllers in the biological reactors and struvite precipitation in the sludge line, were proposed in order to obtain high plant performance as well as nutrient recovery and mitigation of the GHG emissions in a plant-wide context. The implemented control strategies led to an overall more sustainable and efficient plant performance in terms of better effluent quality, reduced operational cost and lower GHG emissions. The lowest N₂O and overall GHG emissions were achieved when ammonium and soluble nitrous oxide in the aerobic reactors were controlled and struvite was recovered in the reject water stream, achieving a reduction of 27% for N₂O and 9% for total GHG, compared to the open loop configuration.

Evaluating the potential impact of energy-efficient ammonia control on the carbon footprint of a full-scale wastewater treatment plant

An assessment was performed for elucidating the possible impact of different aeration strategies on the carbon footprint of a full-scale wastewater treatment plant. Using a calibrated model, the impact of different aeration strategies was simulated. The ammonia controller tested showed its ability in ensuring effluent ammonia concentrations compliant with regulation along with significant savings on aeration energy, compared to fixed oxygen set point (DO_sp) control strategies. At the same time, nitrous oxide emissions increased due to accumulation of nitrification intermediates. Nevertheless, when coupled with the carbon dioxide emissions due to electrical energy consumption for aeration, the overall carbon footprint was only marginally affected. Using the local average CO₂ emission factor, ammonia control slightly reduced the carbon footprint with respect to the scenario where DO_sp was fixed at 2 mg·L^-1. Conversely, no significant change could be detected when compared against the scenarios where the DO_sp was fixed. Overall, the actual impact of ammonia control on the carbon footprint compared to other aeration strategies was found to be strictly connected to the sources of energy employed, where the larger amount of low CO₂-emitting energy is, the higher the relative increase in the carbon footprint will be.

Improving the quality of wastewater treatment plant monitoring by adopting proper sampling strategies and data processing criteria

Monitoring is a crucial operation for plant management. However, proper sampling procedures and data processing criteria are not always adopted. Wastewater treatment plants work under dynamic conditions, which poses a challenge for a correct performance assessment. The aim of this work is to analyse some important aspects of wastewater sampling and data processing, to identify case by case methods which should to be adopted in order to obtain reliable and consistent information on plant performance. The study was conducted through simulations and real data analyses. It turned out that: a) the preferable 24-hour composite sampling procedure is the flow-proportional mode; in addition, the required sampling frequency (i.e. the number of sub-samples to be taken to make the 24-h composite sample) increases as the percentage of population discontinuously discharging the monitored substance decreases; b) a Variability Index was defined to help find the minimum sampling frequency (i.e. the number of 24-h composite samples per year) for the calculation of annual mass flows with an acceptable uncertainty; and c) criteria were proposed for the identification of pseudo-steady state periods needed to calculate reliable mass balances and plant performance indicators.

A supervisory fuzzy logic control scheme to improve effluent quality of a wastewater treatment plant

The application of control strategies in wastewater treatment plants has increased to improve their performance for treating influent. The fuzzy logic controller plays a vital role in this work and simulation work was carried out in a benchmark simulation model no.1 (BSM1) framework. The attempted work proposes two control schemes with the objectives of improving the effluent quality and minimizing the number of measurements taken from the plant. The design of fuzzy control schemes is based on five inputs and six outputs in order to accomplish the objectives. Experimental results show improvement in the effluent quality and increase in the efficacy of the control system. The proposed design is implemented using MATLAB with the adaptation in 2014a.

Analysis of empirical methods for the quantification of N2O emissions in wastewater treatment plants: Comparison of emission results obtained from the IPCC Tier 1 methodology and the methodologies that integrate operational data

Wastewater is a source of N₂O emission that is generated, both directly from advanced treatment plants and indirectly from the discharge of wastewater into the natural environment, due to its remaining nitrogen content. There are a variety of methods based on different parameters used to calculate N₂O emission in wastewater treatment plants. The methodology proposed by the IPCC is used as an international reference for national inventories. In this work, we use five international methodologies to calculate the N₂O emission of the WWTPs in two areas with high population density: The Metropolitan Area of Barcelona (MAB) and Mexico City (MXC). The MAB has 100% population served and has advanced treatment plants (five WWTP) and traditional wastewater treatment plants (two WWTP), the MXC served 14% of its population and had advanced treatment plants (six WWTP) and traditional plants (nineteen WWTP) in 2016. The results obtained show that the IPCC and Das methodologies underestimate the emission of N₂O by considering the per capita consumption of proteins as a constant nitrogen value and also by the suggested emission factors. The methodologies that use the operational data of each plant provide emission results closer to those found in the literature. The value of TN should be the parameter to be considered for a correct estimate of the N₂O emission in the WWTPs. The emission factors currently used are very low, with a low level of confidence of up to 1.3%. The range currently used should be increased and have a minimum range of 0.03 kg N₂O-N/kg N. The emission factors reported in the literature are very variable and with very high levels of uncertainty, and therefore underestimate the emission of N₂O in WWTPs. More research should be done to obtain higher and more reliable emission factors than those currently used.

Global Internal Recirculation Alternative Operation to Reduce Nitrogen and Ammonia Limit Violations and Pumping Energy Costs in Wastewater Treatment Plants

The internal recirculation plays an important role in different areas of the biological treatment of wastewater treatment plants because it has a great influence on the concentration of pollutants, especially nutrients. A usual manipulation of the internal recirculation flow rate is based on the target of controlling the nitrate concentration in the last anoxic tank. This work proposes an alternative for the manipulation of the internal recirculation flow rate instead of nitrate control, with the objective of avoiding limit violations of nitrogen and ammonia concentrations and reducing operational costs. A fuzzy controller is proposed to achieve it based on the effects of the internal recirculation flow rate in different areas of the biological treatment. The proposed manipulation of the internal recirculation flow rate is compared to the application of the usual nitrate control in an already established and published operation strategy by using the internationally known benchmark simulation model no. 2 as a working scenario. The results show improvements with reductions of 59.40% in ammonia limit violations, 2.35% in total nitrogen limit violations, and 38% in pumping energy costs.

Appraisal of suspended growth process for treatment of mixture of simulated petroleum, textile, domestic, agriculture and pharmaceutical wastewater

The unrestricted discharge of domestic and industrial wastewaters along with agricultural runoff water into the environment as mixed-wastewater pose serious threat to freshwater resources in many countries. Mixed-wastewater pollution is a common phenomenon in the developing countries as the technologies to treat the individual waste streams at source are lacking due to high operational and maintenance costs. Therefore, the need to explore the potential of the suspended growth process which is a well-established process technology for biological wastewater treatment is the focus of this paper. Different wastewater constituents: representing domestic, pharmaceutical, textile, petroleum, and agricultural runoff were synthesized as a representative of mixed-wastewater and treated in two semi-continuous bioreactors (R1 & R2) operated at constant operating conditions, namely MLSS (mg/L): 4640-R1, 4440-R2, SRT: 21-d, HRT: 48-72 h, and uncontrolled pH. The system attained stable condition in day 97, with average COD, BOD and TSS reduction as 84.5%, 86.2%, and 72.2% for R1; and 85.1%, 87.9%, and 75.1% for R2, respectively. Phosphate removal on average was by 74.3% in R1 and 76.6% in R2, while average nitrification achieved in systems 1 and 2 were 56.8% and 54.7%, respectively. The biological treatment system has shown potential for improving the quality of mixed-wastewater to the state where reuse may be considered and tertiary treatment can be employed to polish the effluent quality.

Mitigating nitrous oxide emissions at a full-scale wastewater treatment plant

Mitigation of nitrous oxide (N₂O) emissions is of primary importance to meet the targets of reducing carbon footprints of wastewater treatment plants (WWTPs). Despite of a large amount of N₂O mitigation studies conducted in laboratories, full-scale implementation of N₂O mitigation is scarce, mainly due to uncertainties of mitigation effectiveness, validation of N₂O mathematical model, risks to nutrient removal performance and additional costs. This study aims to address the uncertainties by investigating the quantification, development and implementation of N₂O mitigation strategies at a full-scale sequencing batch reactor (SBR). To achieve this, N₂O emission dynamics, nutrient removal performance and operation of the SBR were monitored to quantify N₂O emissions, and identify the N₂O generation mechanisms. N₂O mitigation strategies centered on reducing dissolved oxygen (DO) levels were consequently proposed and evaluated using a multi-pathway N₂O production mathematical model before implementation. The implemented mitigation strategy resulted in a 35% reduction in N₂O emissions (from the emission factor of 0.89 ± 0.05 to 0.58 ± 0.06%), which was equivalent to annual reduction of 2.35 tonne of N₂O from the studied WWTP. This could be mainly attributed to reductions in N₂O generated via the NH₂OH oxidation pathway due to the lowering of DO level. As the first reported mitigation strategy permanently implemented at a full scale WWTP, it showcased that the mitigation of N₂O emissions at full-scale is feasible and that widely accepted N₂O mitigation strategies developed in laboratory studies are also likely effective in full-scale plants. Furthermore, the close agreement between the validated and predicted N₂O emission factors (0.58% vs 0.55%, respectively), showed that the N₂O mathematical model is a useful tool to evaluate N₂O mitigation strategies at full-scale. Importantly this work demonstrated that N₂O mitigation does not necessarily require additional operational cost to meet reduction targets. In contrast, the N₂O mitigation applied here reduced energy requirements for aeration by 20%. Equally important, long-term monitoring identified that N₂O mitigation did not affect the nutrient removal performance of the plant. Finally, with the knowledge acquired in this study, a standard approach for mitigating N₂O emissions from full-scale treatment plants was proposed.

A critical review on life cycle assessment and plant-wide models towards emission control strategies for greenhouse gas from wastewater treatment plants

For decades, there has been a strong interest in mitigating greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). Numerous models were developed to measure the emissions and propose the quantification. Existing studies looked at the relationship between GHG emissions and operational cost (OCI), which is one of the most important indicators for decision-makers. Other parameters that can influence the control strategies include the effluent quality (EQI) and total environmental impacts. Plant-wide models are reliable methods to examine the OCI, EQI and GHG emissions while Life cycle assessment (LCA) works to assess the potential environmental impacts. A combined LCA and plant-wide model proved to be a valuable tool evaluating and comparing strategies for the best performance of WWTPs. For this study involving a WWTP, the benchmark model is used while LCA is the decision tool to find the most suitable treatment strategy. LCA adds extra criteria that complement the existing criteria provided by such models. Complementing the cost/performance criteria is proposed for plant-wide models, including environmental evaluation, based on LCA, which provides an overall better assessment of WWTPs. It can capture both the dynamic effects and potential environmental impacts. This study provides an overview of the integration between plant-wide models and LCA.

Light-driven nitrous oxide production via autotrophic denitrification by self-photosensitized Thiobacillus denitrificans

N₂O (Nitrous oxide, a booster oxidant in rockets) has attracted increasing interest as a means of enhancing energy production, and it can be produced by nitrate (NO₃^-) reduction in NO₃^--loading wastewater. However, conventional denitrification processes are often limited by the lack of bioavailable electron donors. In this study, we innovatively propose a self-photosensitized nonphototrophic Thiobacillus denitrificans (T. denitrificans-CdS) that is capable of NO₃^- reduction and N₂O production driven by light. The system converted >72.1 ± 1.1% of the NO₃^--N input to N₂ON, and the ratio of N₂O-N in gaseous products was >96.4 ± 0.4%. The relative transcript abundance of the genes encoding the denitrifying proteins in T. denitrificans-CdS after irradiation was significantly upregulated. The photoexcited electrons acted as the dominant electron sources for NO₃^- reduction by T. denitrificans-CdS. This study provides the first proof of concept for sustainable and low-cost autotrophic denitrification to generate N₂O driven by light. The findings also have strong implications for sustainable environmental management because the sunlight-triggered denitrification reaction driven by nonphototrophic microorganisms may widely occur in nature, particularly in a semiconductive mineral-enriched aqueous environment.

Evaluation of the Nitrous Oxide Emission Reduction Potential of an Aerobic Bioreactor Packed with Carbon Fibres for Swine Wastewater Treatment

Nitrous oxide (N2O) is a potent greenhouse gas that is emitted from wastewater treatment plants. To reduce emissions of N2O from swine wastewater treatment plants, we constructed an experimental aerobic bioreactor packed with carbon fibres (ca. 1 m3 bioreactor) as an alternative to conventional activated sludge treatment. The N2O emission factor for the aerobic bioreactor packed with carbon fibres (CF) was 0.002 g N2O-N/g TN-load and the value for the typical activated sludge (AS) reactor was 0.013 g N2O-N/g TN-load. The CF treatment method achieved more than 80% reduction of N2O emissions, compared with the AS treatment method. The experimental introduction of a CF carrier into an actual wastewater treatment plant also resulted in a large reduction in N2O generation. Specifically, the N2O emission factors decreased from 0.040 to 0.005 g N2O-N/g TN-load following application of the carrier. This shows that it is possible to reduce N2O generation by more than 80% by using a CF carrier during the operation of an actual wastewater treatment plant. Some bacteria from the phylum Chloroflexi, which are capable of reducing N2O emissions, were detected at a higher frequency in the biofilm on the CF carrier than in the biofilm formed on the AS reactor.

Fuzzy logic for plant-wide control of biological wastewater treatment process including greenhouse gas emissions

The application of control strategies is increasingly used in wastewater treatment plants with the aim of improving effluent quality and reducing operating costs. Due to concerns about the progressive growth of greenhouse gas emissions (GHG), these are also currently being evaluated in wastewater treatment plants. The present article proposes a fuzzy controller for plant-wide control of the biological wastewater treatment process. Its design is based on 14 inputs and 6 outputs in order to reduce GHG emissions, nutrient concentration in the effluent and operational costs. The article explains and shows the effect of each one of the inputs and outputs of the fuzzy controller, as well as the relationship between them. Benchmark Simulation Model no 2 Gas is used for testing the proposed control strategy. The results of simulation results show that the fuzzy controller is able to reduce GHG emissions while improving, at the same time, the common criteria of effluent quality and operational costs.