Ammonia-Based Aeration Control Research Articles

Integrated supervisory fuzzy control framework for biological wastewater treatment plants operation and their effect on GHG emissions

In this research, the effect on the greenhouse gases (GHG) emissions from biological wastewater treatment plants (WWTP) operation by implementing an integrated supervisory control framework on the bench-mark simulation model 2 gas (BSM2G) platform is studied. Ammonia based aeration control is considered for improving the efficiency of the nitrification process and also nitrate control is considered to enhance the performance of the overall plant. Different control strategies are proposed by considering PI control, Model Predictive control and Fuzzy controllers. A notable change is observed in GHG emissions due to bio-treatment because of the impact of control strategies on N2O emissions during denitrification with respect to the nitration where dissolved oxygen (DO) control alone is considered. In addition, when the nitrate loop is considered, a better change is observed in the overall plant performance. On comparison with other approaches, the proposed control strategies resulted in a reduction in the GHG emissions with marginal effect on the other performance indexes effluent quality index (EQI) and operational cost index (OCI).

Municipal wastewater for energy generation: a favourable approach for developing nations

This study focuses on understanding municipal wastewater (MWW) constituents and assessing technological options to harness the energy content of wastewater in developing countries. There are numerous research studies related to water treatment technologies and wastewater energy value. However, it remains to be seen which perspectives actually make technology adoption feasible. This study explores and presents the potential for some viable and innovative MWW treatment plant (WWTP) systems as a paradigm shift towards resource recovery, energy neutrality and the production of renewable energy by WWTPs. Various cost-effective opportunities related to operational strategies, plant redesign and the upgrading of current WWTPs that can foster self-reliant communities were visualised. Thermal and chemical pretreatments, sequential batch reactors, anaerobic membrane fluidised bioreactors, ammonia-based aeration control and combined heat and power systems can collectively contribute to energy recovery by WWTPs, ranging from 85 to 111%. The study suggests that upgrading the system to become an energy self-reliant water treatment system outweighs the multimode costs associated with health and ecological damages by reducing diseases, pollution and poor productivity regimes.

Systematic Performance Evaluation of Reinforcement Learning Algorithms Applied to Wastewater Treatment Control Optimization.

Treatment of wastewater using activated sludge relies on several complex, nonlinear processes. While activated sludge systems can provide high levels of treatment, including nutrient removal, operating these systems is often challenging and energy intensive. Significant research investment has been made in recent years into improving control optimization of such systems, through both domain knowledge and, more recently, machine learning. This study leverages a novel interface between a common process modeling software and a Python reinforcement learning environment to evaluate four common reinforcement learning algorithms for their ability to minimize treatment energy use while maintaining effluent compliance within the Benchmark Simulation Model No. 1 (BSM1) simulation. Three of the algorithms tested, deep Q-learning, proximal policy optimization, and synchronous advantage actor critic, generally performed poorly over the scenarios tested in this study. In contrast, the twin delayed deep deterministic policy gradient (TD3) algorithm consistently produced a high level of control optimization while maintaining the treatment requirements. Under the best selection of state observation features, TD3 control optimization reduced aeration and pumping energy requirements by 14.3% compared to the BSM1 benchmark control, outperforming the advanced domain-based control strategy of ammonia-based aeration control, although future work is necessary to improve robustness of RL implementation.

Interrogating nitritation at a molecular level: Understanding the potential influence of Nitrobacter spp.

Water resource recovery facilities (WRRFs) increasingly must maximize nitrogen and phosphorus removal, but concurrently face challenges to reduce their energy usage and environmental footprint. In particular, biological nutrient removal (BNR), which targets removal of phosphorus and nitrogen, exhibits a large energy demand. However, a BNR process achieving partial oxidation of NH3 to NO2 (nitritation) could reduce energy demands, with secondary environmental emission benefits. Research was conducted on bench-scale systems performing nitritation and nitrification to better understand how mixed microbial consortia, cultured on real wastewater, can sustain nitritation. BNR configurations achieved nitrite accumulation ratios of 64–82%, with excellent overall effluent quality. Applying phylogenetic, transcriptomic, and metabolomic methods, coupled with process monitoring, results indicate that partial nitritation may be induced through a combination of: (1) Employing ammonia-based aeration control, with an ammonia setpoint of 2, 3 mgN/L; (2) Maintaining an aerobic period DO of 1.0–2.0 mg/L; and (3) Operating BNR post-anoxically, integrated within enhanced biological phosphorus removal (EBPR). Significant nitritation was achieved despite the presence Nitrobacter spp., but nitrite oxidoreductase must be functionally impaired or structurally incomplete. Overall, this research demonstrated the value of interrogating a mixed microbial consortia at a macro and molecular level to explore unique metabolic responses such as nitritation.

Pilot-scale comparison of biological nutrient removal (BNR) using intermittent and continuous ammonia-based low dissolved oxygen aeration control systems.

Sensor driven aeration control strategies have recently been developed as a means to efficiently carry out biological nutrient removal (BNR) and reduce aeration costs in wastewater treatment plants. Under load-based aeration control, often implemented as ammonia-based aeration control (ABAC), airflow is regulated to meet desired effluent standards without specifically setting dissolved oxygen (DO) targets. Another approach to reduce aeration requirements is to constantly maintain low DO conditions and allow the microbial community to adapt to the low-DO environment. In this study, we compared the performance of two pilot-scale BNR treatment trains that simultaneously used ABAC and low-DO operation to evaluate the combination of these two strategies. One pilot plant was operated with continuous ABAC while the other one used intermittent ABAC. Both processes achieved greater than 90% total Kjehldal nitrogen (TKN) removal, 60% total nitrogen removal, and nearly 90% total phosphorus removal. Increasing the solids retention time (SRT) during the period of cold (∼12 °C) water temperatures helped maintain ammonia removal performance under low-DO conditions. However, both processes experienced poor solids settling characteristics during winter. While settling was recovered under warmer temperatures, improving settling quality remains a challenge under low-DO operation.

Enhancement of biological nutrient removal process with advanced process control tools in full-scale wastewater treatment plant

One of the major challenges in existing WasteWater Treatment Plants (WWTPs) is to comply with the increasingly stringent nutrient discharge limits established by the competent authorities to enhance environmental protection, while keeping operation costs as low as possible. This paper describes the results obtained from upgrading a full-scale WWTP during seven years (2013–2020) applying five different Advanced Process Control (APC) strategies. Results show that implementation of APC and the development of ammonia-based aeration control, aeration/non-aeration cycles, improved internal/external recirculation and chemical dosage controls resulted in an improvement in nutrients removal rates (+25.48% and +9.25%, for nitrogen and phosphorus, respectively) and in a reduction (-21.79%) of the specific energy ratio. In addition, the promotion of an Enhanced Biological Phosphorous Removal (EBPR) process by APC resulted in an improvement in biological phosphorous removal (+43.90%), chemical savings (-20.00%) and nutrient recovery in the dewatered sludge. Molecular biology tools and laboratory batch tests confirmed the Polyphosphate Accumulating Organisms (PAOs) activity, specifically Tetrasphaera genera. Finally, an economic analysis was conducted, showing a rate of return for the incurred capital expenses with the implemented APC systems of about five years, being an affordable alternative to the upgrading existing WWTPs.

Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process

Abstract Aeration control is a way to have a wastewater treatment plant (WWTP) that uses less energy and produces higher effluent quality to meet state and federal regulations. The goal of this research is to develop a neural network (NN) ammonia-based aeration control (ABAC) that focuses on reducing total nitrogen and ammonia concentration violations by regulating dissolved oxygen (DO) concentration based on the ammonia concentration in the final tank, rather than maintaining the DO concentration at a set elevated value, as most studies do. Simulation platform used in this study is Benchmark Simulation Model No. 1, and the NN ABAC is compared to the Proportional-Integral (PI) ABAC and PI controller. In comparison to the PI controller, the simulation results showed that the proposed controller has a significant improvement in reducing the AECI up to 23.86%, improving the EQCI up to 1.94%, and reducing the overall OCI up to 4.61%. The results of the study show that the NN ABAC can be utilized to improve the performance of a WWTP’s activated sludge system.

Impact of Ammonia-Based Aeration Control (ABAC) on Energy Consumption

An Ammonia-Based Aeration Control (ABAC) system is installed in the primary aeration basins of a regional wastewater treatment facility. The energy consumption of the system of air blowers, measured in kilowatts per hour by an existing meter, is analyzed for seven months after the installation of the ABAC system and compared to system performance prior to commissioning of the ABAC system. Processed data, including volume flow rate, ammonia loading, and treatment equipment efficiency, are evaluated for periods before and after the ABAC system installation. Ammonia mass loading and air transfer ratio in the aeration basins are determined to be the leading factors affecting the performance of the ABAC system and thus impacting the metered energy consumption. The metered energy consumption data are normalized by the two calculated ratios, which reflect the change in ammonia loading and air transfer ratio. The normalized and metered energy consumption data are compared, and the results show a reduction in energy consumption since the installation of the ABAC system. A yearly savings of approximately 9 ± 1% in energy costs is estimated with the installation of the ABAC system. The savings in energy consumption calculated as well as the improvements in nitrification efficiency confirm the benefit of an ABAC system in reducing operation costs and enhancing process control.

Hybrid statistical-machine learning ammonia forecasting in continuous activated sludge treatment for improved process control

In this work, a statistical stability metric and novel hybrid statistical-machine learning ammonia forecasting model are developed to improve the accuracy and precision of municipal wastewater treatment. Aeration for biological nutrient removal is typically the largest energy expense for municipal wastewater treatment plants (WWTP). Ammonia-based aeration control (ABAC) is one approach designed to minimize excessive aeration by adjusting air blower output from online ammonia measurements rather than from a dissolved oxygen (DO) sensor, which is the conventional aeration control approach. We propose a quantitative stability metric, Total Sample Variance, to compare system-wide variability of competing aeration control strategies. Using this metric, the performance of traditional DO and ABAC control strategies with varying setpoints and control parameters were compared in a medium-sized WWTP, and the most stable strategy was identified and implemented at the facility. To further improve ABAC performance, ammonia forecasting models were constructed using both statistical and machine learning to improve the accuracy of the aeration control system. Diurnal, diurnal-linear, artificial neural network (ANN), and hybrid diurnal-linear-ANN forecasting models were trained on real-time plant-wide process data. The diurnal-linear and diurnal-linear-ANN forecasts were found to most accurately forecast ammonia; improving upon the existing ammonia measurement by up to 32% and 46%, respectively, whereas the ANN model forecast was only able to improve by up to 8%. This work demonstrates the ease and flexibility of integrating statistics and machine learning methods for developing new treatment models in conventional WWTP for features in full-scale conventional activated sludge systems.

Comparison of sensor driven aeration control strategies for improved understanding of simultaneous nitrification/denitrification.

A pilot scale process was operated with A-stage effluent (ASE) and primary clarifier effluent (PCE) in MLE, all tanks aerated, A/O, and A2O configurations. Continuous DO control at high DO (2mg/L), low DO (0.1-0.3mg/L), ammonia-based aeration control (ABAC), and ammonia versus NOx (AvN) control (both continuous and intermittent operation) were compared on the basis of total inorganic nitrogen (TIN) removal, and simultaneous nitrification-denitrification (SND). The highly loaded adsorption/bio-oxidation (A/B) process configuration (4hr HRT) with intermittent aeration was capable of achieving a maximum TIN removal of 80%, while the A2O process with PCE feed, an 11hr HRT, and 0.2-0.3mg/L DO continuous aeration achieved a maximum of 88% TIN removal. ABAC and AvN control did not always result in DO setpoints low enough to achieve SND, and even if setpoints were low enough to achieve SND that did not always result in increased overall TIN removal over continuous DO control of 2mg/L. While there are other benefits to transitioning to sensor driven aeration control strategies such as ABAC and AvN, increased TIN removal during continuous aeration is not guaranteed. Results suggest that although low DO is a prerequisite for SND, carbon availability for denitrification in the aerobic zone is more likely to be the limiting factor once low DO conditions are met. PRACTITIONER POINTS: Intermittent aeration control results in higher TIN removal than continuous aeration at the same total SRT Continuous aeration AvN control is not likely to result in more TIN removal than continuous aeration ABAC for a given COD and nitrogen load Configurations that are designed to maximize predenitrification (e.g., MLE and A2O) are less likely to achieve increased SND in the aerobic zone from low DO operation than configurations that are not (e.g., A/O).

Assessment of aeration control strategies for biofilm-based partial nitritation/anammox systems.

The objective of this work was to compare the nitrogen removal in mainstream, biofilm-based partial nitritation anammox (PN/A) systems employing (1) constant setpoint dissolved oxygen (DO) control, (2) intermittent aeration, and (3) ammonia-based aeration control (ABAC). A detailed water resource recovery facility (WRRF) model was used to study the dynamic performance of these aeration control strategies with respect to treatment performance and energy consumption. The results show that constant setpoint DO control cannot meet typical regulatory limits for total ammonia nitrogen (NHx-N). Intermittent aeration shows improvement but requires optimisation of the aeration cycle. ABAC shows the best treatment performance with the advantages of continuous operation and over 20% lower average energy consumption as compared to intermittent aeration.

Ammonia-based aeration control with optimal SRT control: improved performance and lower energy consumption.

Ammonia-based aeration control (ABAC) is a cascade control concept for controlling total ammonia nitrogen (NHx-N) in the activated sludge process. Its main goals are to tailor the aeration intensity to the NHx-N loading and to maintain consistent nitrification, to meet effluent limits but minimize energy consumption. One limitation to ABAC is that the solids retention time (SRT) control strategy used at a water resource recovery facility (WRRF) may not be consistent with the goals of ABAC. ABAC-SRT control is a strategy for aligning the goals of ammonia-based aeration control and SRT control. A supervisory controller is used to ensure that the SRT is always optimal for ABAC. The methodology has the potential to reduce aeration energy consumption by over 30% as compared to traditional dissolved oxygen (DO) control. Practical implementation aspects are highlighted for implementation at full scale, such as proper selection of the set point for the supervisory controller, proper calculation of the rate of change in sludge inventory, using a mixed liquor suspended solids (MLSS) controller, and tuning of the controllers. In conclusion, ABAC-SRT is a promising approach for coordinated control of SRT, total ammonia nitrogen, and dissolved oxygen in the activated sludge process that balances both treatment performance and energy savings.

Simulation of dissolved oxygen-and ammonia-based aeration control strategies in a mainstream deammonification biofilm process

Simulation of dissolved oxygen-and ammonia-based aeration control strategies in a mainstream deammonification biofilm process

Taking Ammonia-Based Aeration Control to the Next Level — Real World Experience and Lessons Learned

Taking Ammonia-Based Aeration Control to the Next Level — Real World Experience and Lessons Learned

Ammonia-Based Aeration Control of an Oxidation Ditch to Improve Effluent Quality and Reduce Operating Cost

Ammonia-Based Aeration Control of an Oxidation Ditch to Improve Effluent Quality and Reduce Operating Cost

Saving Carbon with SND Using Ammonia Based Aeration Control

Saving Carbon with SND Using Ammonia Based Aeration Control

Modelling Ammonia Based Aeration Control in Real Time with Online Instrumentation

Modelling Ammonia Based Aeration Control in Real Time with Online Instrumentation

Easy as A-B-A-C 1-2-3. Case Studies of Ammonia Based Aeration Control at Multiple Advanced Wastewater Treatment Facilities

Easy as A-B-A-C 1-2-3. Case Studies of Ammonia Based Aeration Control at Multiple Advanced Wastewater Treatment Facilities

The Prius of the Racetrack: 15 Years of Successful Ammonia-based Aeration Control at an Advanced BNR Secondary Treatment Plant Achieving Net Zero Energy

The Prius of the Racetrack: 15 Years of Successful Ammonia-based Aeration Control at an Advanced BNR Secondary Treatment Plant Achieving Net Zero Energy

Role of Solids Retention Time in Ammonia-Based Feedback Aeration Control

AbstractAmmonia-based feedback aeration control could avoid an unnecessary air supply and, therefore, save energy. However, this control method could be impacted by many factors such as solids retention time (SRT), nitrite oxidation rate, dissolved oxygen (DO) variation, influent load, temperature, and regular low DO operation. This study indicated that, with an unlimited DO (DO≥2 mg/L), the minimal SRT required to achieve complete nitrification was between 5 and 10 days. If the SRT was ≤20 days, reducing the DO could result in accumulation of nitrite. Nitrite could also accumulate under a peak ammonia load with a SRT of less than 15 days. In contrast, ammonia-based feedback control resulted in regular low DO operation. Because low DO enriches nitrifiers and selects nitrite-oxidizing bacteria (NOB) with a greater oxygen affinity, the required SRT to avoid the accumulation of nitrite can be substantially reduced. The authors recommend using a minimal SRT of 20 days to start a system. When the system reac...