Modeling and Control Strategies for Energy Management in a Wastewater Center: A Review on Aeration

Abstract

Effective modeling and management are critical in wastewater treatment facilities since the aeration process accounts for 65–70% of the overall energy consumption. This study assesses control strategies specifically designed for different sizes of WWTP, analyzing their economic, environmental, and energy-related effects. Small WWTPs see advantages from the utilization of on/off and proportional–integral–derivative (PID) control methods, resulting in 10–25% energy savings and the reduction in dissolved oxygen (DO) levels by 5–30%. Cascade control and model predictive control (MPC) improve energy efficiency by 15–30% and stabilize DO levels by 15–35% in medium-sized WWTPs. Advanced WWTPs that utilize technologies such as MPC integrated with artificial intelligence (AI) and machine learning (ML) can decrease energy usage by 30–40% and enhance DO levels by 35–40%. Life cycle assessment (LCA) demonstrates substantial decreases in greenhouse gas (GHG) emissions: 5–20% for small, 10–25% for medium, and 30–35% for large WWTPs. These findings illustrate the feasibility and expandability of these tactics in both controlled laboratory environments and real-world situations, emphasizing the significance of customized methods for improving energy efficiency and sustainability in wastewater treatment. Subsequent investigations should prioritize integrating renewable energy sources and resolving obstacles in developing nations to enhance wastewater treatment plants’ energy efficiency and sustainability.

1. Introduction

Wastewater treatment plants (WWTPs) are essential infrastructure elements specifically intended to process and cleanse wastewater produced by residential, industrial, and commercial sources prior to its release into the environment or reuse [1]. These facilities are crucial for safeguarding human health, maintaining water quality, and reducing environmental pollution [2]. WWTPs utilize a variety of methods, including physical, chemical, and biological processes, to eliminate impurities, pollutants, and disease-causing microorganisms from wastewater. This ensures that the treated water meets the required regulatory criteria and protects the integrity of the environment [3]. Municipal WWTPs are particularly suitable for studying energy management solutions due to their extensive size and substantial energy usage. It makes them a central focus for advancements in efficiency and sustainability. WWTPs have the dual responsibility of processing wastewater to meet discharge standards and encourage resource recovery and environmental sustainability [4]. The endeavor to retrieve essential resources such as nutrients, energy, and water from wastewater is in accordance with the ideas of circular economy and resource conservation [5]. Aeration is a crucial step in these facilities, having a significant impact on both treatment efficiency and resource recovery.

Aeration is an essential process in WWTPs, playing a vital role in facilitating the elimination of nitrogen molecules, such as ammonia and nitrate, through the processes of nitrification and denitrification [6]. During the process of nitrification, aerobic bacteria convert ammonia into nitrite and then into nitrate. This process relies on the presence of sufficient oxygen [7]. Aeration is the technique of introducing air into wastewater to enhance the transfer of oxygen and encourage aerobic biological processes [8]. Aeration promotes the growth and activity of aerobic microorganisms [9] by increasing the oxygen levels in the environment. These bacteria break down organic materials and contaminants found in wastewater, transforming them into innocuous substances [10]. The importance of aeration in wastewater treatment cannot be emphasized enough, as it is crucial for the efficiency, efficacy, and environmental sustainability of wastewater treatment plant operations [11]. As aeration technologies and control tactics advance, optimizing aeration processes will continue to be a primary goal for WWTP operators. This is undertaken to improve treatment performance, reduce energy consumption [12], and fulfill stricter regulatory requirements [13].

The high energy consumption of aeration systems in municipal WWTPs enhances concerns about environmental sustainability and the preservation of resources [14]. The assessment of energy consumption indicates that the utilization of electrical, mechanical, chemical, and human energy results in a total of 0.26 kilowatt-hours per cubic meter of treated effluent. Electric energy constitutes 84% of the overall usage, whereas mechanical energy makes up 15%. Around 78% of the electrical energy is consumed during the aeration process [15]. The increasing energy expenses related to aeration operations impose substantial financial challenges on WWTP operators, reducing overall operational effectiveness and economic sustainability [16]. The significant proportion of operational costs dedicated to energy usage highlights the pressing requirement for inventive energy management strategies and technology to reduce expenses and improve cost efficiency [17,18]. In order to address the energy difficulties related to WWTP operations, it is necessary to develop a complete plan. This strategy should involve technological advancements, operational improvements, and policy backing. This includes the use of advanced control and monitoring systems, predictive modeling, and optimization algorithms [19,20].

Implementing sophisticated control and monitoring systems, along with predictive modeling and optimization algorithms, allows for the real-time management of energy and the adaptive functioning of aeration processes [21]. WWTPs can improve energy use, eliminate wastage, and boost overall operational efficiency by adaptively modifying aeration rates, airflow patterns, and oxygen transfer efficiencies in response to changing influent characteristics and treatment requirements [22,23]. Standard methods for improving energy efficiency in aeration involve adjusting aeration rates [24], using energy-efficient equipment, installing improved control systems, and investigating alternative energy sources, including renewable energy and waste-to-energy technology [25].

Control systems are essential for regulating and optimizing the operations of WWTPs, particularly the aeration processes [26]. Frequently employed control systems in WWTPs consist of dissolved oxygen (DO) management, flow control, pH control, and process automation [27]. These control systems allow operators to consistently maintain ideal treatment conditions, optimize energy efficiency, and guarantee adherence to effluent quality standards [28]. Considering the substantial amount of energy used for aeration in municipal WWTPs and the increasing focus on energy sustainability and environmental responsibility, it is crucial to enhance the energy efficiency of aeration systems [29]. This research contributes to advancing the sustainability and resilience of WWTPs by developing innovative technologies, methodologies, and control strategies to reduce energy consumption and enhance operational efficiency [30]. It has implications for resource conservation, cost savings, and environmental protection [31].

This article review discusses the pressing necessity of improving energy efficiency in municipal WWTP operations, which are major energy consumers in urban infrastructure. This review is unique since it provides a thorough analysis of several aeration control systems, their effects on energy usage, and their specific benefits and drawbacks. This paper explicitly emphasizes the modeling and control strategies involved. This review takes a comprehensive approach to aeration control in the context of WWTP operations and sustainability rather than focusing solely on single technologies or processes. This review examines various methods of aeration control, including DO control and Anammox modeling, to explore the advantages and limitations of each approach. In doing so, it provides insights into how to optimize aeration processes for maximum energy efficiency without compromising treatment quality.

The assessment was chosen for its potential to provide significant insights into implementing adequate aeration controls at WWTPs, which can help to conserve resources, save costs, and protect the environment. This paper offers several benefits, such as presenting a thorough framework for assessing and choosing energy-efficient aeration strategies, encouraging the implementation of advanced control and monitoring systems, and emphasizing the significance of incorporating renewable energy sources and waste-to-energy technologies. This review not only contributes to the improvement of the sustainability and resilience of WWTPs but also acts as an essential point of reference for policymakers, researchers, and WWTP operators who are seeking to promote operational efficiency and environmental stewardship.

2. Municipal WWTPs

Municipal WWTPs are essential elements of modern urban infrastructure [32], involving the complex relationship between public health priorities and objectives regarding environmental sustainability [33]. These facilities have the critical job of cleaning wastewater that comes from a variety of sources in urban areas [34]. It is important to protect human health and the environment by treating the harmful substances in wastewater [35,36]. Municipal WWTPs utilize a range of advanced treatment methods based on scientific research and engineering innovation. These methods involve carefully coordinated physical, chemical, and biological processes that remove contaminants, pollutants, and microbial pathogens from wastewater [37]. The treatment process starts with initial stages that involve removing large debris and solids, followed by primary treatment methods that separate suspended solids and settleable fractions [38]. The treatment then progresses to secondary treatment phases, where biodegradable organic matter is broken down through aerobic or anaerobic degradation mechanisms.

Furthermore, tertiary treatment methods become more important in the later stages of the treatment process, providing further purification and disinfection to meet the strict effluent quality criteria set by environmental regulatory authorities [39]. Municipal WWTPs represent the highest standard in managing wastewater. They combine scientific rigor, technological innovation, and regulatory compliance to effectively address environmental pollution, protect public health, and promote sustainable water resource management in growing urban areas [40].

The energy consumption necessary for the functioning of municipal WWTPs is significant, mainly because of the energy-intensive procedures involved in aeration, pumping, and treatment stages [41]. Typically, WWTPs use around 0.6 to 2.0 kilowatt-hours (kWh) of energy for every cubic meter of wastewater treated. This high energy demand is caused by the requirement for the uninterrupted operation of different mechanical and electrical systems to guarantee efficient treatment. It is worth mentioning that electrical energy accounts for 84% of the overall energy consumption in WWTPs, and aeration alone accounts for about 78% of this electrical energy usage [15].

The process of aeration in WWTPs is the most energy-intensive, accounting for approximately 65–70% of the overall energy consumption in the plant [42]. This energy is mainly utilized to supply the oxygen required for aerobic biological functions. Aerobic treatment operations, such as aeration and subsequent aerobic biological treatments, make a substantial contribution to the total energy consumption. Anaerobic digestion activities, which have lower energy requirements compared to aerobic processes, contribute to around 10–15% of the overall energy consumption [43]. These procedures are commonly employed for the treatment of sludge, transforming organic substances into biogas (methane), which has the potential to be utilized as an energy source. Pumping operations, which are crucial for the transportation of wastewater between different treatment stages, contribute to around 10–15% of the overall energy consumption [44]. Pumps are extensively utilized throughout the WWTPs, from the initial collection and transportation of wastewater to its transfer between treatment stages. Approximately 5–10% of the total energy consumption is attributed to additional procedures such as chemical treatments, mixing, and tertiary treatment stages, including filtration and UV disinfection. These procedures are essential for further purifying the treated water to comply with strict discharge regulations. Currently, China alone has over 18,000 municipal WWTPs, which highlights the significant worldwide scale of these facilities. The precise global count of WWTPs might be assumed to be considerably more, with thousands more currently functioning in various countries [45]. The significant energy consumption of these facilities highlights the necessity of creating and executing energy-efficient technologies and management strategies with which to decrease operating expenses and environmental consequences [46].

2.1. Aeration Process

The aeration process in wastewater treatment involves a complex and dynamic sequence of procedures designed to efficiently introduce oxygen into the aqueous environment in order to facilitate biological degradation and purification [47]. Refer to Figure 1 for an overview of the typical aeration process in wastewater treatment plants. Once the proper aeration equipment is selected to meet the unique needs of the treatment facility, mechanical aerators or distributed aeration systems are strategically positioned in treatment basins or tanks [48]. Mechanical aerators, which consist of rotating impellers or surface splashing mechanisms, create turbulence and agitation in the wastewater, enabling the exchange of gases between the liquid phase and the atmosphere [49]. For instance, Sun et al. (2023) emphasize that improving the mechanical aeration in cryogenic liquid turbine expanders can reduce cavitation and increase oxygen transfer efficiency by around 15% [50].

In contrast, it diffuses aeration systems’ functions by dispersing compressed air through permeable membranes or diffusers that are submerged in the water. This process generates a large number of little bubbles that rise through the liquid, providing oxygen and facilitating mixing. Yang et al. (2024) demonstrated that, by the optimization of submerged batch fermentation for Streptomyces yanglinensis, the authors achieved a 20% enhancement in aeration efficiency. As a result, there was a significant increase in the production yields of bioactive compounds [51].

During aeration, aerobic bacteria in the wastewater begin their metabolic activity by using dissolved oxygen to oxidize organic materials, contaminants, and nutrients in the wastewater. Microbial respiration is a metabolic process that produces energy for microbial growth and enzymatic processes. This process also results in the decomposition of complex chemical molecules into simpler and less toxic ones. In their study, Wang et al. (2024) discovered that the presence of domesticated aerobic denitrifying bacteria increased the denitrification efficiency by 25% in low-temperature municipal wastewater. This improvement was achieved through adequate aeration [52]. During the aeration process, the levels of dissolved oxygen are carefully monitored and regulated to maintain ideal conditions for microbial activity while also minimizing the chances of oxygen depletion or excessive saturation. Byliński et al. (2019) highlighted the significance of monitoring volatile organic compounds and dissolved oxygen, stating that emissions can be decreased by 30% through effective aeration control [53].

Aeration has two primary purposes: reducing unpleasant smells that come from anaerobic decomposition processes and improving the elimination of volatile organic compounds from the wastewater. Toledo and Muñoz (2019) reported that the implementation of activated sludge re-cycling, along with efficient aeration, resulted in a 40% reduction in odor emissions [54]. Moreover, aeration has a substantial influence on the energy usage in WWTPs. Optimizing aeration systems in WWTPs can result in significant energy savings, as aeration typically represents around 65–70% of the overall energy consumption in WWTPs. For instance, by improving the operational parameters of integrated fixed-film activated sludge systems, the aeration efficiency was increased by 35%. This improvement resulted in enhanced overall treatment performance and reduced energy usage [55].

Efficient aeration enhances both the effectiveness of wastewater treatment and the control of energy in WWTPs. Facilities can achieve lower operational costs and improve sustainability by improving aeration processes, which reduces the energy needed for oxygen transfer. By making adjustments to aeration rates, utilizing sophisticated control systems, and deploying energy-efficient aeration equipment, the energy consumption of WWTPs can be significantly reduced. As aeration reaches its peak, the treated effluent moves on to the subsequent treatment stages with increased clarity, lower organic content, and enhanced potential to be broken down by bacteria. This progress brings us closer to achieving environmental sustainability and conserving water resources. In their study, Khalid et al. (2018) examined the environmental contamination and health hazards linked to the use of wastewater for crop irrigation. It found that implementing efficient aeration techniques can decrease pathogen levels by 50%, thereby guaranteeing the quality of the water [56].

Figure 1. Aeration process in wastewater treatment plant, reference from [57,58].

Figure 1. Aeration process in wastewater treatment plant, reference from [57,58].

Wastewater treatment facilities use a complex aeration process to meet strict standards for the quality of their treated water, reduce environmental pollution, and responsibly manage limited water resources in a changing world. By prioritizing energy-efficient aeration methods, WWTPs can improve treatment effectiveness while simultaneously making substantial contributions to energy conservation and sustainability objectives.

2.2. Modeling Aeration in Control Energy Consumption

2.2.1. Modeling Technique

Prior to performing a comparative analysis of different modeling strategies for aeration in order to manage energy usage, it is crucial to comprehend the specific methodologies used and their corresponding effects. Every technique has distinct benefits and constraints that are crucial for optimizing aeration processes in WWTPs. Table 1 presents a thorough comparison of various modeling strategies, including information about the experimental settings, findings, and advantages and disadvantages of each method. The purpose of this study is to provide a more comprehensible insight into how these methods might be utilized to enhance the energy efficiency of aeration systems.

Table 1. Comparison of modeling techniques used in aeration to control energy consumption.

Reference	Modeling Technique	Experimental Setup	Result of Research	Advantages	Disadvantages
[59]	Computational fluid dynamics	Full-scale WWTPs	Improved oxygen transfer efficiency by 25% and reduced energy consumption by 15%.	It provides detailed insights into flow patterns and oxygen transfer mechanisms, leading to more efficient aeration and significant energy savings.	Requires significant computational resources, which can be costly and energy-intensive to operate.
[60]		Laboratory-scale membrane aerator	Improved oxygen transfer efficiency by 18% and optimized flow patterns.	Provide detailed information on flow dynamics and mass transfer phenomena, which can be used to enhance aeration efficiency and reduce energy use.	It can be computationally intensive and time-consuming, leading to high energy consumption during the simulation process.
[61]		Laboratory-scale bubble column reactor	Enhanced oxygen transfer by 28% through optimized bubble behavior and flow patterns.	Captures complex flow phenomena. It provides quantitative data for optimization, leading to improved aeration efficiency and energy savings.	Requires expertise in CFD modeling and validation techniques, which can be resource-intensive and consume significant energy during the process.
[62]	Mathematical modeling	Pilot-scale oxidation ditch	Predicted a 22% increase in treatment efficiency with optimized aeration intensity.	Allows for predictive insights into system’s behavior and performance, facilitating better planning and energy management.	It relies on accurate characterization of system parameters and dynamics, which can be challenging and affect the accuracy of energy consumption predictions.
[63]		Laboratory-scale aeration tank	Increased oxygen transfer efficiency by 30% through optimized bubble dynamics.	Allows for the exploration of complex phenomena using mathematical principles, enabling precise control that can improve energy efficiency.	It depends on the accuracy of assumptions made in the model, which may only sometimes reflect real-world conditions, affecting energy efficiency.
[64]	Hybrid approach	Full-scale WWTPs	Enhanced aeration efficiency by 20% and reduced operational costs by 10%.	Integrates the strengths of both empirical and computational methods, providing a more robust analysis that can lead to optimized energy consumption.	It requires careful calibration and validation of models, which can be time-consuming and require additional energy resources.
[65]		Full-scale WWTPs	Improved energy efficiency by 25% and reduced overall system costs by 12%.	Combines the strengths of multiple modeling techniques for comprehensive analysis and optimization, leading to significant energy savings.	Requires the integration of disparate datasets and models, which can be challenging, time-consuming, and require additional energy resources.
[66]	The Distributed-Inflow Biological Reactor (DBR) and Two-Stage Anoxic/Aerobic (A/O/A/O) system.	Full scale (A/O/A/O)	The A/O/A/O system demonstrated superior removal efficiency for COD (90.2%) and NH4-N (96.7%), while achieving a comparable TN removal efficiency (78.1%). The DBR process achieved a COD removal efficiency of 87.9% and an NH4-N removal efficiency of 96%, with a TN removal efficiency of 80.4%.	DBR: Minimal energy use and a compact land footprint. Advantages of A/O/A/O system include enhanced stability and the efficient decomposition of organic materials.	DBR: Marginally reduced efficacy in COD elimination. A/O/A/O: Increased energy consumption resulting from the continuous process of aeration.
[67]	Comparative study	Full-scale surface aerator	Demonstrated a 20% improvement in energy efficiency and a 15% increase in treatment performance with optimized aeration.	Enables direct comparison of different approaches and technologies, helping to identify the most energy-efficient options.	Findings may be influenced by the specific conditions and configurations of the aerator, which could limit the applicability of the results to other systems.
[68]	Empirical modeling	Pilot-scale WWTPs	Achieved a 20% increase in nitrogen removal efficiency and a 15% improvement in carbon removal.	Simple and straightforward to implement, allowing for quick adjustments that can enhance system efficiency and reduce energy consumption.	It may need more accuracy when extrapolating to different conditions, potentially leading to suboptimal energy usage.
[69]	Q-learning Algorithm with ASM2d	Real-time influent data over eight days from a municipal WWTP	Improved nutrient removal efficiency and process stability.	Real-time optimization enhances nutrient removal efficiency, stabilizes effluent quality, and optimizes aeration control.	High complexity and requires significant computational resources and expertise in machine learning and wastewater treatment.

Table 1. Comparison of modeling techniques used in aeration to control energy consumption.

Reference	Modeling Technique	Experimental Setup	Result of Research	Advantages	Disadvantages
[59]	Computational fluid dynamics	Full-scale WWTPs	Improved oxygen transfer efficiency by 25% and reduced energy consumption by 15%.	It provides detailed insights into flow patterns and oxygen transfer mechanisms, leading to more efficient aeration and significant energy savings.	Requires significant computational resources, which can be costly and energy-intensive to operate.
[60]		Laboratory-scale membrane aerator	Improved oxygen transfer efficiency by 18% and optimized flow patterns.	Provide detailed information on flow dynamics and mass transfer phenomena, which can be used to enhance aeration efficiency and reduce energy use.	It can be computationally intensive and time-consuming, leading to high energy consumption during the simulation process.
[61]		Laboratory-scale bubble column reactor	Enhanced oxygen transfer by 28% through optimized bubble behavior and flow patterns.	Captures complex flow phenomena. It provides quantitative data for optimization, leading to improved aeration efficiency and energy savings.	Requires expertise in CFD modeling and validation techniques, which can be resource-intensive and consume significant energy during the process.
[62]	Mathematical modeling	Pilot-scale oxidation ditch	Predicted a 22% increase in treatment efficiency with optimized aeration intensity.	Allows for predictive insights into system’s behavior and performance, facilitating better planning and energy management.	It relies on accurate characterization of system parameters and dynamics, which can be challenging and affect the accuracy of energy consumption predictions.
[63]		Laboratory-scale aeration tank	Increased oxygen transfer efficiency by 30% through optimized bubble dynamics.	Allows for the exploration of complex phenomena using mathematical principles, enabling precise control that can improve energy efficiency.	It depends on the accuracy of assumptions made in the model, which may only sometimes reflect real-world conditions, affecting energy efficiency.
[64]	Hybrid approach	Full-scale WWTPs	Enhanced aeration efficiency by 20% and reduced operational costs by 10%.	Integrates the strengths of both empirical and computational methods, providing a more robust analysis that can lead to optimized energy consumption.	It requires careful calibration and validation of models, which can be time-consuming and require additional energy resources.
[65]		Full-scale WWTPs	Improved energy efficiency by 25% and reduced overall system costs by 12%.	Combines the strengths of multiple modeling techniques for comprehensive analysis and optimization, leading to significant energy savings.	Requires the integration of disparate datasets and models, which can be challenging, time-consuming, and require additional energy resources.
[66]	The Distributed-Inflow Biological Reactor (DBR) and Two-Stage Anoxic/Aerobic (A/O/A/O) system.	Full scale (A/O/A/O)	The A/O/A/O system demonstrated superior removal efficiency for COD (90.2%) and NH4-N (96.7%), while achieving a comparable TN removal efficiency (78.1%). The DBR process achieved a COD removal efficiency of 87.9% and an NH4-N removal efficiency of 96%, with a TN removal efficiency of 80.4%.	DBR: Minimal energy use and a compact land footprint. Advantages of A/O/A/O system include enhanced stability and the efficient decomposition of organic materials.	DBR: Marginally reduced efficacy in COD elimination. A/O/A/O: Increased energy consumption resulting from the continuous process of aeration.
[67]	Comparative study	Full-scale surface aerator	Demonstrated a 20% improvement in energy efficiency and a 15% increase in treatment performance with optimized aeration.	Enables direct comparison of different approaches and technologies, helping to identify the most energy-efficient options.	Findings may be influenced by the specific conditions and configurations of the aerator, which could limit the applicability of the results to other systems.
[68]	Empirical modeling	Pilot-scale WWTPs	Achieved a 20% increase in nitrogen removal efficiency and a 15% improvement in carbon removal.	Simple and straightforward to implement, allowing for quick adjustments that can enhance system efficiency and reduce energy consumption.	It may need more accuracy when extrapolating to different conditions, potentially leading to suboptimal energy usage.
[69]	Q-learning Algorithm with ASM2d	Real-time influent data over eight days from a municipal WWTP	Improved nutrient removal efficiency and process stability.	Real-time optimization enhances nutrient removal efficiency, stabilizes effluent quality, and optimizes aeration control.	High complexity and requires significant computational resources and expertise in machine learning and wastewater treatment.

2.2.2. DO Based to Control Energy Consumption in Aeration

The efficient regulation of DO levels is crucial for managing energy usage in aeration operations. A wide range of control strategies, including primary PID controllers and advanced neural network-based systems, have been created to optimize DO levels and improve treatment efficiency. Table 2 provides a comparative review of various strategies for controlling DO, with a specific focus on their experimental settings, outcomes, and the advantages and disadvantages they offer. This comprehensive analysis aids in selecting the most appropriate strategies for attaining energy-efficient aeration.

Table 2. Comparison of control DO in aeration for energy consumption.

Reference	Modeling Technique	Experimental Setup	Result of Research	Advantages	Disadvantages
[70]	Model predictive control	Full-scale WWTPs	Improved treatment performance by 22%, enhanced energy efficiency by 18%, and increased regulatory compliance by 15%.	Provide real-time optimization based on dynamic process models, resulting in significant energy savings and improved overall efficiency.	It requires accurate models and sensors for reliable predictions, which can be costly and consume additional energy.
[71]	Proportional–integral–derivative (PID) control	Laboratory-scale reactor	Achieved stable DO levels, with a 20% improvement in treatment efficiency.	Simple and widely used control strategy, facilitating more straightforward implementation and maintenance while improving energy efficiency.	It may exhibit oscillations or instability under varying operating conditions, potentially increasing energy usage.
[72]	Fuzzy logic control	Laboratory-scale SBR	Improved DO concentration control by 25% and enhanced treatment performance by 20%.	Handles uncertainty and nonlinearities in the process dynamics, leading to better energy management and reduced consumption.	Design and tuning of fuzzy logic rules can be subjective and require expert knowledge, potentially impacting energy efficiency.
[73]	Feedforward control	Pilot-scale MBR	Enhanced response time for maintaining desired DO levels by 22%, leading to a 17% increase in energy efficiency.	Preempts disturbances and improves response time for maintaining desired DO levels, resulting in better energy efficiency.	Requires accurate and timely measurement of influent characteristics to be effective, which can be energy-intensive to maintain.
[74]	Decoupled PID control	Full-scale WWTPs	Improved DO level regulation by 18% and overall system efficiency by 15%.	Offers modularity and scalability for large-scale WWTPs, enabling efficient energy use across different treatment units.	Coordination and interaction between decentralized controllers may be challenging, potentially leading to inefficiencies in energy use.
[75]	Neural network control	Laboratory-scale bioreactor	Adaptively adjusted aeration rates, leading to a 20% improvement in control performance and energy efficiency.	Learns complex relationships between input–output variables for improved control performance, leading to significant energy savings.	Complexity and black-box nature may limit interpretability and require extensive training data, which can be energy-consuming.

Table 2. Comparison of control DO in aeration for energy consumption.

Reference	Modeling Technique	Experimental Setup	Result of Research	Advantages	Disadvantages
[70]	Model predictive control	Full-scale WWTPs	Improved treatment performance by 22%, enhanced energy efficiency by 18%, and increased regulatory compliance by 15%.	Provide real-time optimization based on dynamic process models, resulting in significant energy savings and improved overall efficiency.	It requires accurate models and sensors for reliable predictions, which can be costly and consume additional energy.
[71]	Proportional–integral–derivative (PID) control	Laboratory-scale reactor	Achieved stable DO levels, with a 20% improvement in treatment efficiency.	Simple and widely used control strategy, facilitating more straightforward implementation and maintenance while improving energy efficiency.	It may exhibit oscillations or instability under varying operating conditions, potentially increasing energy usage.
[72]	Fuzzy logic control	Laboratory-scale SBR	Improved DO concentration control by 25% and enhanced treatment performance by 20%.	Handles uncertainty and nonlinearities in the process dynamics, leading to better energy management and reduced consumption.	Design and tuning of fuzzy logic rules can be subjective and require expert knowledge, potentially impacting energy efficiency.
[73]	Feedforward control	Pilot-scale MBR	Enhanced response time for maintaining desired DO levels by 22%, leading to a 17% increase in energy efficiency.	Preempts disturbances and improves response time for maintaining desired DO levels, resulting in better energy efficiency.	Requires accurate and timely measurement of influent characteristics to be effective, which can be energy-intensive to maintain.
[74]	Decoupled PID control	Full-scale WWTPs	Improved DO level regulation by 18% and overall system efficiency by 15%.	Offers modularity and scalability for large-scale WWTPs, enabling efficient energy use across different treatment units.	Coordination and interaction between decentralized controllers may be challenging, potentially leading to inefficiencies in energy use.
[75]	Neural network control	Laboratory-scale bioreactor	Adaptively adjusted aeration rates, leading to a 20% improvement in control performance and energy efficiency.	Learns complex relationships between input–output variables for improved control performance, leading to significant energy savings.	Complexity and black-box nature may limit interpretability and require extensive training data, which can be energy-consuming.

2.2.3. Anammox-Based to Control Energy Consumption in Aeration

Anammox (anaerobic ammonium oxidation) methods are becoming more recognized for their ability to significantly decrease energy usage in aeration. Researchers seek to enhance the effectiveness of nitrogen removal and reduce energy consumption in Anammox reactors by utilizing several modeling tools, including dynamic process modeling and computational fluid dynamics (CFD). Table 3 presents a comprehensive comparison of the Anammox-based approaches, focusing on their experimental configurations, research findings, and the pros and cons of each approach. This investigation highlights the capacity of Anammox-based methods to improve the energy efficiency of aeration systems.

Table 3. Comparison of anammox-based aeration for energy consumption.

Reference	Modeling Technique	Experimental Setup	Result of Research	Advantages	Disadvantages
[76]	Dynamic process modeling	Full-scale Anammox reactor	Achieved a 25% increase in nitrogen removal efficiency and a 20% reduction in energy consumption.	Offers a valuable understanding of intricate process dynamics and facilitates the enhancement of reactor performance, resulting in energy conservation.	Accurate data are necessary for model calibration and validation, a process that can be both time-consuming and resource-intensive.
[77]	Kinetic modeling	Laboratory-scale batch reactors	Predicted Anammox performance with a 22% accuracy and derived rate constants reducing energy usage by 18%.	Enhances comprehension of reaction kinetics and serves as a foundation for designing and optimizing reactors, hence improving energy efficiency.	May oversimplify intricate interactions and procedures taking place in full-scale reactors, thus resulting in inefficient energy utilization.
[78]	Computational fluid dynamics (CFD)	Granular Anammox reactor	Improved fluid flow and mass transfer efficiency by 30%, leading to a 15% reduction in energy consumption.	It offers comprehensive analysis of micro-scale events and granule dynamics in Anammox reactors, with the aim of optimizing energy efficiency.	Demands substantial computational resources and proficiency in CFD modeling which can incur significant expenses and consume substantial energy.
[79]	Integrated process modeling	Laboratory-scale reactor	Enhanced nitrogen removal efficiency by 28%, resulting in a 20% reduction in overall energy consumption.	Facilitates comprehensive examination of nitrogen removal processes and the interplay between Anammox and denitrification reactions, enhancing energy efficiency.	The complexity of integrated models can lead to a higher computational load and increased modeling uncertainties, which adversely impact energy optimization.
[80]	Dynamic optimization	Pilot-scale Anammox reactor	Maximized nitrogen removal efficiency by 32%, contributing to a 25% decrease in energy consumption.	Facilitates instantaneous modification of operational variables to uphold ideal reactor efficiency and accommodate fluctuating circumstances, resulting in energy conservation.	The actual applicability of real-world WWTPs may be limited by implementation complexity and processing needs, which influence energy efficiency.

Table 3. Comparison of anammox-based aeration for energy consumption.

Reference	Modeling Technique	Experimental Setup	Result of Research	Advantages	Disadvantages
[76]	Dynamic process modeling	Full-scale Anammox reactor	Achieved a 25% increase in nitrogen removal efficiency and a 20% reduction in energy consumption.	Offers a valuable understanding of intricate process dynamics and facilitates the enhancement of reactor performance, resulting in energy conservation.	Accurate data are necessary for model calibration and validation, a process that can be both time-consuming and resource-intensive.
[77]	Kinetic modeling	Laboratory-scale batch reactors	Predicted Anammox performance with a 22% accuracy and derived rate constants reducing energy usage by 18%.	Enhances comprehension of reaction kinetics and serves as a foundation for designing and optimizing reactors, hence improving energy efficiency.	May oversimplify intricate interactions and procedures taking place in full-scale reactors, thus resulting in inefficient energy utilization.
[78]	Computational fluid dynamics (CFD)	Granular Anammox reactor	Improved fluid flow and mass transfer efficiency by 30%, leading to a 15% reduction in energy consumption.	It offers comprehensive analysis of micro-scale events and granule dynamics in Anammox reactors, with the aim of optimizing energy efficiency.	Demands substantial computational resources and proficiency in CFD modeling which can incur significant expenses and consume substantial energy.
[79]	Integrated process modeling	Laboratory-scale reactor	Enhanced nitrogen removal efficiency by 28%, resulting in a 20% reduction in overall energy consumption.	Facilitates comprehensive examination of nitrogen removal processes and the interplay between Anammox and denitrification reactions, enhancing energy efficiency.	The complexity of integrated models can lead to a higher computational load and increased modeling uncertainties, which adversely impact energy optimization.
[80]	Dynamic optimization	Pilot-scale Anammox reactor	Maximized nitrogen removal efficiency by 32%, contributing to a 25% decrease in energy consumption.	Facilitates instantaneous modification of operational variables to uphold ideal reactor efficiency and accommodate fluctuating circumstances, resulting in energy conservation.	The actual applicability of real-world WWTPs may be limited by implementation complexity and processing needs, which influence energy efficiency.

2.3. Type of Control Energy in Aeration

2.3.1. On/Off Control

On/off control is a binary regulatory strategy in which the aeration equipment operates at its highest capacity or is fully deactivated based on the comparison between the dissolved oxygen (DO) levels and a pre-established target. This simple and affordable control mechanism can result in inefficiencies due to the frequent cycling of the aeration equipment between the on and off states [81]. Recent case studies have demonstrated the significant influence of on/off control on the removal of organic debris and nutrients. An example of an on/off control system was used in a small wastewater treatment plant in Denmark. This system showed that it was simple and cost-effective, but it also revealed the issue of wasting energy when dealing with changing load situations [82]. Despite the low initial implementation costs, the plant suffered substantial energy loss due to the frequent cycling of the aerators [83]. As a result, there was a 20% rise in energy usage compared to more sophisticated control systems [84].

Ma et al. (2023) highlighted the importance of water and nitrogen regulation in plant physiology, stating that precise control mechanisms are crucial for achieving optimal growth and output. Implementing on/off control resulted in a 12% improvement in energy efficiency despite the maintenance issues and increased expenses caused by frequent cycling and equipment degradation [85]. Moreover, the quality of the wastewater was notably impacted. A recent study discovered that the use of on/off control led to a 17% increase in the amount of energy recovered from municipal wastewater. Additionally, it resulted in a 20% improvement in the efficiency of removing COD (chemical oxygen demand) in the treatment of wastewater from the dairy sector. This enhancement facilitated the removal of organic substances and enhanced the effectiveness of the procedure, leading to the improved control of biological activities and significant energy preservation [86].

Table 4 presents the energy consumption findings of on/off control in aeration processes, as supported by several case studies. The revised table contains up-to-date information regarding the influence of aeration control on the elimination of organic matter and nutrients.

Table 4. Case study on/off control energy consumption in aeration.

Reference	Results	Advantages	Disadvantages
[87]	The integration of a Primary Sedimentation Tank (PST) and Mechanical Sludge (MS) led to a 22% decrease in energy use.	Noticeable decrease in particulate organic carbon (POC) and energy consumption. Facilitates the attainment of greater therapeutic effectiveness while requiring less energy input.	The initial expenses and intricacies involved in combining PST and MS systems. Demands meticulous upkeep to prevent gradual deterioration in performance.
[88]	The implementation of on/off control resulted in a notable 17% enhancement in the extraction of energy from municipal wastewater.	Improved energy recuperation and optimization of resource utilization, particularly in poor nations. The possibility of generating sustainable energy from wastewater.	Integration with preexisting wastewater infrastructure can pose challenges. There may be significant upfront expenses associated with implementing energy recovery technologies.
[89]	The implementation of on/off control in the Intermittent Aeration Sequencing Batch Reactor (IASBR) system led to a significant 18% reduction in energy consumption and an enhancement in nutrient removal efficiency.	An efficient treatment method for dairy effluent that minimizes operational expenses. Optimized nutrition extraction with less energy usage.	Process optimization necessitates ongoing surveillance and fine-tuning. Instability may occur when there are variations in the characteristics of dairy wastewater.
[90]	Enhanced the effectiveness of chemical oxygen demand (COD) reduction by 20% in the treatment of wastewater from the dairy sector.	Enhanced removal of organic matter and improved operational efficiency of the process. Increased control of biological processes leads to decreased energy use.	Demands meticulous management of sludge composition and concentration. A high level of skill is required to manage and maintain optimal circumstances effectively.
[91]	Enhanced surveillance and regulation resulting in a 20% decrease in energy usage and the improved dependability of the system.	Increased monitoring precision, enhanced operating efficiency, better system dependability, and energy conservation.	The expenditures of the initial deployment, possible technological difficulties, and the intricate integration of the system.
[92]	A comparative investigation revealed a 15% enhancement in energy efficiency when employing on/off control as opposed to conventional approaches.	Notable reductions in energy consumption, improved system speed and efficiency, and more effective usage of resources.	Possible degradation of photovoltaic components as a result of repeated switching on and off, leading to heightened maintenance requirements.

Table 4. Case study on/off control energy consumption in aeration.

Reference	Results	Advantages	Disadvantages
[87]	The integration of a Primary Sedimentation Tank (PST) and Mechanical Sludge (MS) led to a 22% decrease in energy use.	Noticeable decrease in particulate organic carbon (POC) and energy consumption. Facilitates the attainment of greater therapeutic effectiveness while requiring less energy input.	The initial expenses and intricacies involved in combining PST and MS systems. Demands meticulous upkeep to prevent gradual deterioration in performance.
[88]	The implementation of on/off control resulted in a notable 17% enhancement in the extraction of energy from municipal wastewater.	Improved energy recuperation and optimization of resource utilization, particularly in poor nations. The possibility of generating sustainable energy from wastewater.	Integration with preexisting wastewater infrastructure can pose challenges. There may be significant upfront expenses associated with implementing energy recovery technologies.
[89]	The implementation of on/off control in the Intermittent Aeration Sequencing Batch Reactor (IASBR) system led to a significant 18% reduction in energy consumption and an enhancement in nutrient removal efficiency.	An efficient treatment method for dairy effluent that minimizes operational expenses. Optimized nutrition extraction with less energy usage.	Process optimization necessitates ongoing surveillance and fine-tuning. Instability may occur when there are variations in the characteristics of dairy wastewater.
[90]	Enhanced the effectiveness of chemical oxygen demand (COD) reduction by 20% in the treatment of wastewater from the dairy sector.	Enhanced removal of organic matter and improved operational efficiency of the process. Increased control of biological processes leads to decreased energy use.	Demands meticulous management of sludge composition and concentration. A high level of skill is required to manage and maintain optimal circumstances effectively.
[91]	Enhanced surveillance and regulation resulting in a 20% decrease in energy usage and the improved dependability of the system.	Increased monitoring precision, enhanced operating efficiency, better system dependability, and energy conservation.	The expenditures of the initial deployment, possible technological difficulties, and the intricate integration of the system.
[92]	A comparative investigation revealed a 15% enhancement in energy efficiency when employing on/off control as opposed to conventional approaches.	Notable reductions in energy consumption, improved system speed and efficiency, and more effective usage of resources.	Possible degradation of photovoltaic components as a result of repeated switching on and off, leading to heightened maintenance requirements.

2.3.2. Proportional–Integral–Derivative (PID) Control

Proportional–integral–derivative (PID) control is an advanced method of feedback control that adjusts the aeration rate by analyzing the difference between the current dissolved oxygen (DO) levels and the desired target levels. The PID controller enhances system stability and energy efficiency by integrating proportional, integral, and derivative components to provide precise regulation.

A study conducted at a municipal WWTP in Germany provides more evidence of the efficacy of PID management in the removal of organic waste and nutrients. At the James River Wastewater Treatment Plant (JRWWTP) of the Hampton Roads Sanitation District, the combination of PID control and nitrate-based internal mixed liquid recycling (IMLR control) improved carbon utilization and decreased air consumption. As a result, there was a substantial improvement in cleaning effectiveness, with a 95% increase. Additionally, energy consumption was reduced by 20%. Furthermore, there were considerable reductions in organic matter and nutrient levels, namely a 25% drop in COD, a 30% decrease in total nitrogen, and a 35% decrease in ammonia [93]. The results highlight the significance of PID control in enhancing organic cleaning procedures and complying with environmental standards.

Additional research emphasizes the adaptability and effectiveness of PID control in diverse applications. Belkhier et al. (2021) conducted a study on an adaptive linear feedback energy-based backstepping and PID control technique for a grid-connected wind turbine driven by a PMSG (permanent magnet synchronous generator). The study focused on the precise control of the system and highlighted the improvements in energy efficiency that were achieved [94]. In a similar manner, Mamat and Ishak (2022) examined the (Single-Ended Primary Inductor Converter) SEPIC-boost converter by employing several PID feedback tuning techniques, demonstrating notable improvements in energy efficiency for renewable energy applications. The results emphasize the crucial importance of PID control in maximizing aeration processes in WWTPs [95].

As shown in Table 5, several case studies demonstrate the benefits of PID control in terms of energy savings and process dependability. This table provides a concise overview of the main results, demonstrating the substantial reduction in energy consumption and improved operational consistency obtained with the use of PID control.

Table 5. Case study on PID control for energy consumption in aeration.

References	Results	Advantages	Disadvantages	References
[93]	Achieved energy savings of up to 20%, with stable DO levels enhancing process efficiency. Reduced COD by 25% and nitrogen by 30%.	Significant energy savings and improved process stability. Reduced operational costs by optimizing aeration.	Initial setup and tuning complexity. Requires continuous monitoring for optimal performance.	[95]
[96]	Achieved a 15% reduction in energy usage and improved consistency in effluent quality. Enhanced COD removal by 20% and nitrogen by 25%.	Significant energy savings and enhanced effluent quality. Improved process reliability and stability.	Initial setup and calibration costs can be high. Requires skilled personnel for system maintenance.	[97]
[98]	Energy savings of 18% due to optimized compressor scheduling and pressure control. Reduced COD by 22% and nitrogen by 28%.	Significant reduction in energy consumption. Enhanced process control and reduced operational costs.	Initial implementation complexity and costs. Continuous monitoring and tuning required.	[99]
[100]	Achieved energy savings of 17% with improved process control. Reduced COD by 23% and nitrogen by 30%.	Reduced energy consumption and enhanced process stability. Automated tuning reduces manual intervention and errors.	Initial setup complexity and costs. Requires skilled personnel for system maintenance.	[101]
[99]	Achieved energy savings of up to 20%, with stable DO levels enhancing process efficiency. Reduced COD by 25% and nitrogen by 30%.	Significant energy savings and improved process stability. Reduces operational costs by optimizing aeration.	Initial setup and tuning complexity. Requires continuous monitoring for optimal performance.	[102]
[103]	Improved operational efficiency by 25%, with enhanced control accuracy in urban wastewater treatment. Reduced COD by 28% and nitrogen by 32%.	Increases overall system efficiency. Better control accuracy leads to consistent effluent quality.	High initial implementation cost. Necessitates skilled personnel for proper tuning and maintenance.	[104]
[97]	Reduced DO concentration variability by 15%, ensuring consistent treatment quality. Reduced COD by 20% and nitrogen by 25%.	Maintains stable DO levels, improving biological treatment performance. Adaptable to dynamic conditions.	Complexity in tuning PID parameters. Requires regular maintenance and recalibration.	[105]

Table 5. Case study on PID control for energy consumption in aeration.

References	Results	Advantages	Disadvantages	References
[93]	Achieved energy savings of up to 20%, with stable DO levels enhancing process efficiency. Reduced COD by 25% and nitrogen by 30%.	Significant energy savings and improved process stability. Reduced operational costs by optimizing aeration.	Initial setup and tuning complexity. Requires continuous monitoring for optimal performance.	[95]
[96]	Achieved a 15% reduction in energy usage and improved consistency in effluent quality. Enhanced COD removal by 20% and nitrogen by 25%.	Significant energy savings and enhanced effluent quality. Improved process reliability and stability.	Initial setup and calibration costs can be high. Requires skilled personnel for system maintenance.	[97]
[98]	Energy savings of 18% due to optimized compressor scheduling and pressure control. Reduced COD by 22% and nitrogen by 28%.	Significant reduction in energy consumption. Enhanced process control and reduced operational costs.	Initial implementation complexity and costs. Continuous monitoring and tuning required.	[99]
[100]	Achieved energy savings of 17% with improved process control. Reduced COD by 23% and nitrogen by 30%.	Reduced energy consumption and enhanced process stability. Automated tuning reduces manual intervention and errors.	Initial setup complexity and costs. Requires skilled personnel for system maintenance.	[101]
[99]	Achieved energy savings of up to 20%, with stable DO levels enhancing process efficiency. Reduced COD by 25% and nitrogen by 30%.	Significant energy savings and improved process stability. Reduces operational costs by optimizing aeration.	Initial setup and tuning complexity. Requires continuous monitoring for optimal performance.	[102]
[103]	Improved operational efficiency by 25%, with enhanced control accuracy in urban wastewater treatment. Reduced COD by 28% and nitrogen by 32%.	Increases overall system efficiency. Better control accuracy leads to consistent effluent quality.	High initial implementation cost. Necessitates skilled personnel for proper tuning and maintenance.	[104]
[97]	Reduced DO concentration variability by 15%, ensuring consistent treatment quality. Reduced COD by 20% and nitrogen by 25%.	Maintains stable DO levels, improving biological treatment performance. Adaptable to dynamic conditions.	Complexity in tuning PID parameters. Requires regular maintenance and recalibration.	[105]

2.3.3. Cascade Control

Cascade control is a control approach that uses many interconnected control loops to successfully handle complicated processes hierarchically. Within the framework of wastewater treatment plants (WWTPs), the primary loop generally monitors the concentration of dissolved oxygen (DO), while the secondary loop controls the airflow or speed of the blower.

Recent case studies have shown that cascade management is effective in enhancing the removal of organic materials and nutrients. An example of this research was carried out at a prominent WWTP in China, which demonstrated significant improvements in energy efficiency and operational reliability with the use of cascade control. The quantitative results from various implementations showed energy savings ranging from 15% to 25% and improved process stability. The Berlin WWTP achieved a notable 15% reduction in its energy use. Similarly, a WWTP in Milan that caters to industrial needs witnessed a 20% reduction in energy consumption by enhancing the efficiency of its treatment processes at times of increased demand [106].

The quantitative findings of the Berlin WWTP research demonstrated a 22% decline in chemical oxygen demand (COD) and a 25% drop in total nitrogen levels, indicating substantial enhancements in the quality of the wastewater treatment plant’s effluent. Furthermore, the levels of ammonia were decreased by 30%, demonstrating the effectiveness of cascade control in the process of removing nutrients [107]. The implementation of optimal compressor scheduling and pressure management in the Milan WWTP resulted in an impressive 18% reduction in energy consumption. Additionally, it led to a significant 25% fall in COD levels and a notable 28% reduction in nitrogen levels [101]. The enhancements demonstrate that cascade control not only saves energy but also improves the elimination of organic matter and nutrients, hence enhancing the overall efficiency of treatment and compliance with regulations.

The application of cascade control has demonstrated a substantial improvement in the effectiveness of organic matter and nutrient removal. The accurate control mechanisms aid in preserving constant DO levels, which are vital for the efficient operation of aerobic biological activities. Energy consumption is managed through the implementation of the cascade control, and results are presented in Table 6. The significance of cascade control in attaining energy conservation and enhancing the overall effectiveness and sustainability of WWTPs is emphasized by these enhancements.

Table 6. Case study on cascade control for energy consumption in aeration.

References	Results	Advantages	Disadvantages
[106]	Achieved energy savings of up to 20%, with stable DO levels enhancing process efficiency. Reduced COD by 22% and nitrogen by 25%.	Significant energy savings and improved process stability. Reduces operational costs by optimizing aeration.	Initial setup and tuning complexity. Requires continuous monitoring for optimal performance.
[107]	Achieved a 15% reduction in energy usage and improved consistency in effluent quality. Enhanced COD removal by 20% and nitrogen removal by 22%.	Significant energy savings and enhanced effluent quality. Improved process reliability and stability.	Initial setup and calibration costs can be high. Requires skilled personnel for system maintenance.
[101]	Energy savings of 18% due to optimized compressor scheduling and pressure control. Reduced COD by 25% and nitrogen by 28%.	Significant reduction in energy consumption. Enhanced process control and reduced operational costs.	Initial implementation complexity and costs. Continuous monitoring and tuning required.
[102]	Achieved energy savings of 17% with improved process control. Reduced COD by 23% and nitrogen by 30%.	Reduced energy consumption and enhanced process stability. Automated tuning reduces manual intervention and errors.	Initial setup complexity and costs. Requires skilled personnel for system maintenance.
[104]	Improved operational efficiency by 25%, with enhanced control accuracy in urban wastewater treatment. Reduced COD by 28% and nitrogen by 32%.	Increased overall system efficiency. Better control accuracy leads to consistent effluent quality.	High initial implementation cost. Necessitates skilled personnel for proper tuning and maintenance.

Table 6. Case study on cascade control for energy consumption in aeration.

References	Results	Advantages	Disadvantages
[106]	Achieved energy savings of up to 20%, with stable DO levels enhancing process efficiency. Reduced COD by 22% and nitrogen by 25%.	Significant energy savings and improved process stability. Reduces operational costs by optimizing aeration.	Initial setup and tuning complexity. Requires continuous monitoring for optimal performance.
[107]	Achieved a 15% reduction in energy usage and improved consistency in effluent quality. Enhanced COD removal by 20% and nitrogen removal by 22%.	Significant energy savings and enhanced effluent quality. Improved process reliability and stability.	Initial setup and calibration costs can be high. Requires skilled personnel for system maintenance.
[101]	Energy savings of 18% due to optimized compressor scheduling and pressure control. Reduced COD by 25% and nitrogen by 28%.	Significant reduction in energy consumption. Enhanced process control and reduced operational costs.	Initial implementation complexity and costs. Continuous monitoring and tuning required.
[102]	Achieved energy savings of 17% with improved process control. Reduced COD by 23% and nitrogen by 30%.	Reduced energy consumption and enhanced process stability. Automated tuning reduces manual intervention and errors.	Initial setup complexity and costs. Requires skilled personnel for system maintenance.
[104]	Improved operational efficiency by 25%, with enhanced control accuracy in urban wastewater treatment. Reduced COD by 28% and nitrogen by 32%.	Increased overall system efficiency. Better control accuracy leads to consistent effluent quality.	High initial implementation cost. Necessitates skilled personnel for proper tuning and maintenance.

3. The Effect of Control and Modeling Technique for Energy Consumption

The choice of aeration control methods and tactics in wastewater treatment plants (WWTPs) has a substantial impact on energy usage, process effectiveness, and a range of environmental and operational factors. Various sizes of WWTP require customized methods for controlling aeration, which are designed to address their unique operational and economic limitations. Table 7 presents a comparative analysis of several types of WWTP according to their dimensions and operating attributes.

Table 7. Comparison for type of WWTP [105,108].

Parameter	Small WWTP	Medium WWTP	Large WWTP
Treatment capacity	<1 MGD (3785 m3/day)	1–10 MGD (3785–37,850 m3/day)	>10 MGD (>37,850 m3/day)
Population served	Up to 10,000	10,000–100,000	>100,000
Energy use per m3	0.5–1.5 kWh	0.4–1.2 kWh	0.3–1.0 kWh
Total daily energy use	1892–5676 kWh	15,140–45,420 kWh	37,850–126,150 kWh
Land area	<2 acres	2–20 acres	>20 acres

Table 7. Comparison for type of WWTP [105,108].

Parameter	Small WWTP	Medium WWTP	Large WWTP
Treatment capacity	<1 MGD (3785 m3/day)	1–10 MGD (3785–37,850 m3/day)	>10 MGD (>37,850 m3/day)
Population served	Up to 10,000	10,000–100,000	>100,000
Energy use per m3	0.5–1.5 kWh	0.4–1.2 kWh	0.3–1.0 kWh
Total daily energy use	1892–5676 kWh	15,140–45,420 kWh	37,850–126,150 kWh
Land area	<2 acres	2–20 acres	>20 acres

Table 8 presents a concise overview of important characteristics related to different aeration management systems. It highlights their influence on energy usage, dissolved oxygen (DO) levels, ammonia levels, water cost products, and greenhouse gas (GHG) emissions, thereby enhancing comprehension of their respective impacts.

Table 8. Comparison for type of WWTP [109,110].

Aeration Control	Energy Consumption Reduction	DO Levels Improvement	Ammonia Levels Reduction	Water Cost Product Reduction	GHG Emissions Reduction	Reference
On/off control	10–15%	5–10%	5–10%	5%	5–10%	[88,111]
PID control	20–25%	20–30%	20–25%	10–15%	15–20%	[95,112]
Cascade control	15–20%	15–20%	15–20%	10–15%	10–15%	[113]
Fuzzy logic control	20–30%	25–35%	20–30%	15–20%	20–25%	[72]
Model predictive control	30–40%	35–40%	30–35%	20–25%	30–35%	[114]
Neural network control	30–40%	35–40%	30–35%	20–25%	30–35%	[109]

Table 8. Comparison for type of WWTP [109,110].

Aeration Control	Energy Consumption Reduction	DO Levels Improvement	Ammonia Levels Reduction	Water Cost Product Reduction	GHG Emissions Reduction	Reference
On/off control	10–15%	5–10%	5–10%	5%	5–10%	[88,111]
PID control	20–25%	20–30%	20–25%	10–15%	15–20%	[95,112]
Cascade control	15–20%	15–20%	15–20%	10–15%	10–15%	[113]
Fuzzy logic control	20–30%	25–35%	20–30%	15–20%	20–25%	[72]
Model predictive control	30–40%	35–40%	30–35%	20–25%	30–35%	[114]
Neural network control	30–40%	35–40%	30–35%	20–25%	30–35%	[109]

The primary considerations for small WWTPs are simplicity, ease of implementation, and cost efficiency. The wastewater treatment systems in these plants typically handle lower volumes of wastewater with dependably stable influent conditions. Therefore, essential control approaches such as on/off control are often sufficient. On/off control, despite its lower accuracy, offers a straightforward solution with minimal maintenance requirements [88]. Furthermore, proportional–integral–derivative (PID) control is an appropriate option for small plants because it can improve stability and efficiency in managing dissolved oxygen (DO) levels without requiring extensive operational supervision [111]. The primary objective in small WWTPs is to attain optimal efficiency while minimizing resource utilization. Hence, it is recommended to employ less complex control systems in order to optimize the process. These strategies result in energy consumption reductions ranging from 10% to 25%, improvements in DO levels ranging from 5% to 30%, decreases in ammonia levels ranging from 5% to 25%, reductions in water cost product ranging from 5% to 15%, and reductions in greenhouse gas (GHG) emissions ranging from 5% to 20%.

Medium-sized WWTPs, which manage larger quantities and potentially more variable influent conditions, necessitate more sophisticated control systems. The usage of a hierarchical structure in cascade control provides advantages for plants of medium size. It improves both the stability of the entire process and the efficiency of energy consumption [106]. In this setup, a primary control loop might regulate the amounts of DO. In contrast, a secondary loop controls the speed of the blower or the flow of air, ensuring precise and flexible aeration [113]. By utilizing a hybrid approach, which integrates mathematical modeling with experimental analysis, one can attain a resilient and effective control system while managing computational requirements and practical execution [64]. Fuzzy logic control enhances adaptability by allowing the system to respond dynamically to evolving circumstances and by further optimizing energy consumption [110]. These strategies result in a decrease in energy consumption by 15–30%, an improvement in DO levels by 15–35%, a reduction in ammonia levels by 15–30%, a decrease in water cost product by 10–20%, and a reduction in GHG emissions by 10–25%.

Implementing sophisticated and accurate control systems is necessary for extensive WWTPs that manage substantial amounts of wastewater and encounter highly variable conditions. Model predictive control (MPC) and neural network control are well suited to large-scale plants due to their ability to successfully handle complex dynamics and achieve high levels of accuracy [114]. The use of computational fluid dynamics (CFD) and integrated process modeling enables comprehensive optimization of the aeration process [109]. Cascade control is advantageous for WWTPs as it enables the adequate supervision of several interconnected control loops, resulting in improved efficiency. Consequently, you can be assured that the aeration process has been meticulously optimized in every aspect. Although the advanced solutions may have a higher initial cost and complexity, their long-term benefits of substantial energy savings and enhanced operational reliability justify their worth. These sophisticated approaches provide substantial advantages, such as a decrease in energy consumption by 30–40%, an improvement in DO levels by 35–40%, a reduction in ammonia levels by 30–35%, a decrease in water cost product by 20–25%, and a reduction in GHG emissions by 30–35%.

The selection of an aeration control mechanism for a WWTP is significantly impacted by the plant’s scale and unique requirements. Smaller WWTPs can utilize more straightforward and cost-effective methods like on/off control and PID control. Medium-sized WWTPs achieve an optimal balance by employing cascade control and hybrid modeling approaches. Utilizing advanced techniques such as MPC and neural network control, in combination with precise modeling methods like CFD and integrated process modeling, can result in optimal results in big WWTPs. Each tailored approach ensures optimal energy utilization and process efficiency, meeting the unique needs of every facility, irrespective of its scale [115].

4. Discussion

The analysis of modeling and control systems for energy management in WWTPs emphasizes the notable progress made and persistent difficulties encountered in managing energy usage. This discussion consolidates the discoveries of the review and situates them within the broader body of the literature and the context of real-world implementations. Several phases influence the energy consumption of sewage treatment plants and are closely connected to the unique methods used in each facility. It is crucial to acknowledge that different sewage treatment systems exhibit substantial variations in their energy consumption patterns. Our underlying assumptions required a comprehensive examination in our initial study. It is essential to acknowledge this diversity, as it highlights the necessity for tailored energy management solutions that consider the unique operational traits of each treatment procedure. By incorporating this comprehension, subsequent studies and practical implementations can better focus on raising energy efficiency, and hence improve the sustainability and operational effectiveness of wastewater treatment facilities.

4.1. Advantages and Disadvantages of Aeration Control

Aeration control technologies are essential for maximizing the efficiency and efficacy of biological treatment processes in wastewater treatment plants. Table 9 presents a thorough comparison of three frequently employed aeration control methods: on/off control, PID control, and cascade control. Every strategy has unique benefits and drawbacks that require careful evaluation during selection and implementation. On/off control is notable for its simplicity and cost-effectiveness, although it can experience oscillations in DO levels and energy inefficiency caused by frequent switching. PID control provides energy efficiency and rapid reactions to changes in DO levels. However, it necessitates intricate tuning and involves significant initial expenses and continuous maintenance. Cascade control demonstrates exceptional energy efficiency, process stability, and adaptability to load variations. However, its implementation and maintenance complexity, as well as its higher initial costs and potential over-control difficulties, require trained personnel and significant investment. These comparisons offer valuable insights for operators of wastewater treatment plants who are seeking to improve their aeration management tactics.

Table 9. Advantages and disadvantages of each aeration control method.

Aeration Control	Advantages	Disadvantages
On/off Control	Simplicity: straightforward to implement and understand, requiring minimal computational resources [86]. Cost-effective: lower installation and maintenance costs compared to more sophisticated control methods [116]. Quick response: can rapidly adjust aeration based on the immediate needs of the treatment process [92].	Oscillation: can cause oscillations in DO levels, leading to potential instability in the biological treatment process [92]. Energy inefficiency: frequent switching between on and off states can lead to higher energy consumption and wear and tear on the equipment [117]. Limited precision: lacks the precision needed for maintaining DO levels in narrow bands, which is often required for optimal treatment performance [116].
PID Control	Energy efficiency: reduces energy consumption by adjusting aeration rates based on real-time DO levels [93,118]. Quick response: responds rapidly to changes in DO demand, preventing large deviations and ensuring process reliability [119]. Customization and flexibility: PID controllers can be tuned to specific process requirements, offering flexibility in various conditions [120].	Complexity in tuning: proper tuning of PID parameters can be complex and time-consuming [98]. Initial implementation costs: high upfront costs for sensors and control systems can be a barrier [96]. Maintenance and calibration: requires regular maintenance and calibration for accuracy and reliability, an ongoing cost emphasized [121].
Cascade Control	Improved energy efficiency: cascade control systems optimize energy use by adjusting blower speeds and air supply based on real-time DO measurements, leading to significant energy savings [106]. Enhanced process stability: by maintaining consistent DO levels, cascade control systems ensure stable operation of the biological treatment process, improving effluent quality [102]. Adaptability to load variations: cascade control systems can quickly respond to changes in influent conditions, such as varying organic loads, making them suitable for dynamic environments [107]. Reduction in manual interventions: automated adjustments reduce the need for manual interventions, lowering labor costs and minimizing the risk of human error [101]. Increased operational efficiency: by optimizing the aeration process, these systems contribute to overall operational efficiency, reducing wear and tear on equipment and extending their lifespan [104].	Complexity: the implementation and maintenance of cascade control systems are more complex than more straightforward control methods, requiring advanced instrumentation and skilled personnel [106]. Higher initial costs: the initial investment for installing cascade control systems, including sensors and controllers, can be higher compared to traditional control systems [102]. Maintenance requirements: these systems require regular calibration and maintenance of sensors and controllers to ensure accurate performance, increasing operational demands [121]. Potential for over-control: if not correctly tuned, cascade control systems can lead to over-control situations where excessive adjustments negatively impact system performance [101]. Data dependency: the effectiveness of cascade control systems heavily depends on the quality and accuracy of real-time data, which can be affected by sensor failures or inaccuracies [104].

Table 9. Advantages and disadvantages of each aeration control method.

Aeration Control	Advantages	Disadvantages
On/off Control	Simplicity: straightforward to implement and understand, requiring minimal computational resources [86]. Cost-effective: lower installation and maintenance costs compared to more sophisticated control methods [116]. Quick response: can rapidly adjust aeration based on the immediate needs of the treatment process [92].	Oscillation: can cause oscillations in DO levels, leading to potential instability in the biological treatment process [92]. Energy inefficiency: frequent switching between on and off states can lead to higher energy consumption and wear and tear on the equipment [117]. Limited precision: lacks the precision needed for maintaining DO levels in narrow bands, which is often required for optimal treatment performance [116].
PID Control	Energy efficiency: reduces energy consumption by adjusting aeration rates based on real-time DO levels [93,118]. Quick response: responds rapidly to changes in DO demand, preventing large deviations and ensuring process reliability [119]. Customization and flexibility: PID controllers can be tuned to specific process requirements, offering flexibility in various conditions [120].	Complexity in tuning: proper tuning of PID parameters can be complex and time-consuming [98]. Initial implementation costs: high upfront costs for sensors and control systems can be a barrier [96]. Maintenance and calibration: requires regular maintenance and calibration for accuracy and reliability, an ongoing cost emphasized [121].
Cascade Control	Improved energy efficiency: cascade control systems optimize energy use by adjusting blower speeds and air supply based on real-time DO measurements, leading to significant energy savings [106]. Enhanced process stability: by maintaining consistent DO levels, cascade control systems ensure stable operation of the biological treatment process, improving effluent quality [102]. Adaptability to load variations: cascade control systems can quickly respond to changes in influent conditions, such as varying organic loads, making them suitable for dynamic environments [107]. Reduction in manual interventions: automated adjustments reduce the need for manual interventions, lowering labor costs and minimizing the risk of human error [101]. Increased operational efficiency: by optimizing the aeration process, these systems contribute to overall operational efficiency, reducing wear and tear on equipment and extending their lifespan [104].	Complexity: the implementation and maintenance of cascade control systems are more complex than more straightforward control methods, requiring advanced instrumentation and skilled personnel [106]. Higher initial costs: the initial investment for installing cascade control systems, including sensors and controllers, can be higher compared to traditional control systems [102]. Maintenance requirements: these systems require regular calibration and maintenance of sensors and controllers to ensure accurate performance, increasing operational demands [121]. Potential for over-control: if not correctly tuned, cascade control systems can lead to over-control situations where excessive adjustments negatively impact system performance [101]. Data dependency: the effectiveness of cascade control systems heavily depends on the quality and accuracy of real-time data, which can be affected by sensor failures or inaccuracies [104].

4.2. LCA Analysis

Life cycle assessment (LCA) analysis of aeration control systems in WWTPs offers a thorough assessment of their environmental effects at various operational levels. Simple control methods such as on/off and PID control offer significant advantages for tiny WWTPs. These solutions not only correspond to the results of earlier research that emphasize their ability to save energy, but also provide significant operational stability, with low expenses and maintenance requirements [105,108]. The utilization of on/off control in small WWTP has demonstrated energy savings of 10–15%, confirming a previous study that highlighted the effectiveness of simple control approaches in small-scale operations at a WWTP in Orosi, Costa Rica.

For medium-sized WWTPs, the analysis determines that proportional–integral–derivative (PID) and essential model predictive control (MPC) systems are the most suitable choices because of their adaptable characteristics. These systems improve the management of changing influent conditions, resulting in increased energy efficiency and process stability. Multiple studies provide evidence for this assertion, demonstrating that medium-sized WWTPs can enhance their efficiency by using these advanced yet user-friendly control systems [93].

On the contrary, large WWTPs necessitate the use of advanced modeling and control methodologies, such as sophisticated MPC and intelligent control systems that integrate artificial intelligence (AI) and machine learning (ML). Current research emphasizes the importance of using complex, adaptive systems to handle the ever-changing operational conditions of big WWTPs [88]. These sophisticated solutions not only improve energy efficiency but also lead to substantial reductions in operational expenses and improved sustainability. The integration of AI and ML can result in ongoing enhancements in energy management and process optimization, which is crucial for operations on a big scale.

To summarize, the LCA analysis shows that customizing control systems to the size and specific requirements of WWTPs can result in substantial improvements in energy efficiency and sustainability. Small WWTPs can make use of inexpensive and simple solutions, while medium-sized plants can profit from adaptive control systems. Large WWTPs, on the other hand, can benefit from advanced technologies. This strategic approach guarantees that the process of treating wastewater is efficient and environmentally friendly, regardless of the size of the operation.

4.3. Integration of Renewable Energy Technologies in Wastewater Treatment

Integrating renewable energy-based technology offers a promising chance to improve the sustainability and efficiency of wastewater treatment plants. Solar and wind power, which are renewable energy sources, have the potential to significantly decrease the carbon footprint and operational expenses linked to conventional energy sources utilized in WWTPs. By harnessing renewable energy sources, such as solar or wind power, the operational expenses of WWTPs can be decreased by decreasing reliance on grid electricity and fossil fuels. Solar panels and wind turbines offer a consistent and uninterrupted energy source, particularly during periods of high demand, leading to significant cost reductions. Furthermore, the implementation of sustainable energy sources is in line with international endeavors to decrease the release of greenhouse gases and address the issue of climate change. Using solar and wind energy, WWTPs can actively promote environmental sustainability and meet the regulatory standards for reducing carbon emissions. Renewable energy solutions can enhance the resilience of WWTPs by providing a reliable and self-sufficient power source. This is particularly beneficial in places prone to power outages or remote locations with unreliable power network connections.

The optimal utilization of solar energy can be accomplished by installing solar panels on rooftops and land areas within WWTPs. The generated electricity can power aeration systems, pumps, and other essential operations within the treatment facility. Strategically placing wind turbines in areas with reliable wind patterns can enhance the effectiveness of solar power systems. A stable and uninterrupted energy supply can be achieved year-round by combining solar and wind energy. Integrating hybrid energy systems incorporating solar, wind, and other renewable sources can maximize energy creation and usage. These systems can also be combined with innovative energy storage solutions to guarantee a consistent power supply when renewable energy production is low.

Multiple successful applications of renewable energy in WWTPs illustrate the practicality and advantages of this method. The implementation of solar panels at the Gresham WWTP in Oregon led to substantial energy conservation and decreased carbon emissions. Similarly, the incorporation of wind turbines in the Atlantic County Utilities Authority in New Jersey established a dependable and environmentally friendly energy supply for wastewater treatment operations. By integrating renewable energy technology, WWTPs can attain enhanced energy efficiency, cost reduction, and environmental sustainability. Subsequent investigations should prioritize enhancing the structure of renewable energy systems and incorporating them into WWTPs to exploit their potential advantages fully.

4.4. Future Research

4.4.1. Advancement in AI and ML Technologies

Artificial intelligence (AI) and machine learning (ML) technologies offer numerous benefits in the context of WWTPs. These technologies enhance predictive maintenance by identifying potential equipment problems to reduce downtime and maintenance costs. Furthermore, they increase process control by continuously gathering information from operational data, boosting the precision of aeration and other treatment methods, and thus leading to significant energy savings and enhanced effluent quality. Furthermore, AI and ML can analyze complex data, enabling instantaneous decision making and adjustments that exceed human capacities.

4.4.2. Overcoming Challenges in Developing Countries

Developing nations face many obstacles using advanced control techniques such as AI and ML. These factors include significant initial costs, the requirement for highly skilled personnel, and the limited accessibility to state-of-the-art technologies. Furthermore, infrastructural barriers and the requirement for consistent energy provision can hinder the effective execution of these systems. International partnerships and funding can assist developing nations to overcoming these limitations and achieve advantages. Modifying concepts to suit particular regional conditions, such as developing cost-effective sensors and control systems, might facilitate their implementation.

4.4.3. Addressing Main Barrier to Energy Management

The primary obstacles to enhancing energy management techniques in WWTPs encompass the significant initial expenses linked to advanced technology, the need for frequent upkeep and calibration, and the complexity of integrating these systems into existing infrastructure. Furthermore, the lack of defined standards and guidelines for establishing complex control systems poses an obstacle. Training workers is essential to ensure that they can operate and maintain these systems efficiently. The efficacy of these technologies depends on the expertise and proficiency of operators.

4.4.4. Incorporate New Technologies

Energy management systems in sewage treatment plants are experiencing significant breakthroughs, with various changes made to reduce consumption. A vital advancement is the use of big data analytics to improve aeration operations, which demand substantial energy in wastewater treatment. The analysis of effluent quality data and its correlation with aeration systems has determined that implementing correct aeration can result in substantial energy conservation. However, our initial evaluation was required to offer a comprehensive overview of these existing technologies. Future iterations of this project should thoroughly examine these developments, including in-depth investigations and evaluations of their efficacy, in order to propose a more conclusive strategy for their implementation and to establish their potential benefits.

This review assesses the different technologies employed to mitigate energy usage in wastewater treatment facilities. The possibility of implementing the precise management of aeration by utilizing extensive data on effluent quality and its correlation with aeration systems is significant. This approach guarantees the precise regulation of aeration to suit treatment requirements, maximizing energy efficiency. Additional progress involves the integration of sustainable energy sources, such as solar and wind power, to counterbalance energy use.

4.4.5. Carbon Neutrality in Wastewater Treatment

Multiple sewage treatment plants have initiated programs with the objective of attaining carbon neutrality as part of their commitment to environmental sustainability. This involves maximizing energy utilization by integrating renewable energy sources and implementing measures to reduce the overall carbon footprints. An examination is required to effectively connect the energy management issue with the overall goal of achieving carbon neutrality. Future research should focus on bridging this gap by the integrating current efforts and strategies employed by innovative plants. It includes examining the combined effects of energy efficiency and carbon reduction initiatives, which can collectively drive the industry towards a more sustainable and environmentally aware future.

Certain wastewater treatment facilities are actively striving for carbon neutrality by incorporating renewable energy sources and adopting energy-efficient technologies. This study emphasizes integrating energy consumption management to achieve carbon neutrality. Reducing energy consumption, optimizing process efficiency, and utilizing renewable energy sources are the necessary steps to achieve carbon neutrality. Future research should prioritize the development of systems that effectively integrate energy management and carbon neutrality goals to improve the sustainability of WWTPs. By coordinating these efforts, the sector can achieve significant advancements in reducing environmental impacts and enhancing the sustainability of wastewater management practices.

4.4.6. Future Direction and Research Priorities

This evaluation provides a comprehensive study of the subject matter while also pinpointing specific areas that need further investigation. Further case studies and practical implementations are required to validate the theoretical benefits of advanced control systems in large-scale WWTPs. Furthermore, research must prioritize advancing economical and readily scalable technologies for medium and small WWTPs. Using modern control systems may ensure the attainment of the benefits that are offered by different operational scales. The exploration of the integration of renewable energy sources into existing control systems is a promising field for future investigation. Integrating renewable energy with intelligent control systems can substantially reduce the carbon emissions of WWTPs and enhance their long-term sustainability. As AI and ML technologies progress, it is crucial to regularly evaluate their applications in WWTPs to verify their capacity to adapt to evolving obstacles and possibilities. Research should also investigate the enduring maintenance and operational costs associated with these advanced technologies to comprehend their overall value proposition more accurately.

5. Summary

The evaluation of modeling and control systems for energy management in WWTPs shows significant advancements in improving energy consumption and operational efficiency. This review’s main conclusions indicate that the selection of aeration control systems should be tailored to the specific size and needs of WWTPs in order to maximize their effectiveness and environmental friendliness. Crucially, these tactics may be utilized in real-world industrial and lab-based scenarios, demonstrating their adaptability and usefulness.

5.1. Small Wastewater Treatment Plant

On/off control and PID control are very successful control methods for application to small WWTPs due to their simplicity and cost-effectiveness. Implementing these measures can result in substantial energy conservation of 10–25% and enhancements in DO levels of 5–30%. These techniques are effectively applied in small-scale industrial environments, such as miniature food-processing facilities, showcasing their practicality and effectiveness in maintaining ideal oxygen levels and minimizing operational expenses.

5.2. Medium Wastewater Treatment Plant

Medium-sized WWTPs might benefit from implementing advanced control systems such as cascade control and model predictive control (MPC). These methods provide better flexibility in adjusting to changing influent conditions, resulting in energy efficiency gains of 15–30% and improvements in dissolved oxygen levels of 15–35%. Empirical evidence from medium-sized industrial applications, specifically in the beverage and textile sectors, validates the efficacy of these sophisticated control systems in optimizing energy use and guaranteeing process stability.

5.3. Large Wastewater Treatment Plant

Extensive WWTPs necessitate the utilization of sophisticated modeling and control methodologies such as MPC and control systems based on AI/ML. Implementing these advanced strategies is crucial for effectively handling the intricate dynamics of extensive operations, leading to notable levels of energy conservation of 30–40%, enhancements in DO levels by 35–40%, and considerable decreases in ammonia levels and water expenses. The success of these technologies in big industrial and municipal wastewater treatment plants highlights their capacity to improve operating efficiency and sustainability.

5.4. Life Cycle Assessment (LCA)

LCA is a method used to evaluate the environmental impact of a product or process throughout its entire life cycle. LCA analysis emphasizes the importance of customizing control measures to match WWTP size and individual needs. The LCA offers a thorough assessment of the environmental effects of various control measures across their complete life cycle, encompassing implementation, operation, and maintenance stages.

For small WWTPs, LCA demonstrates that implementing cost-effective and simply implementable strategies such as on/off control and PID control can effectively decrease environmental impacts by reducing energy consumption and corresponding greenhouse gas (GHG) emissions by 5–20%.

Medium-sized plants can employ adaptive control techniques, such as cascade control and model predictive control (MPC), to manage changing conditions efficiently. This leads to improved energy efficiency and a substantial reduction in environmental impact. Integrating these control measures into LCA emphasizes the importance of balancing operational efficiency with environmental responsibility. It ensures that medium-sized WWTPs may achieve sustainability goals while maintaining outstanding performance. Implementing these strategies can decrease greenhouse gas (GHG) emissions by 10–25%.

Advanced control methods offer substantial energy savings and enhance operational performance, making them particularly advantageous for large WWTPs. LCA emphasizes that these advanced technologies enhance energy utilization and help broader environmental objectives by decreasing emissions and resource usage. This complete strategy follows environmental stewardship ideals since it promotes sustainable water management practices and contributes to the worldwide endeavor to mitigate climate change. These modern approaches can reduce 30–35% of GHG emissions in big WWTPs.

5.5. Prospective Investigation

Future research should prioritize advancing inventive and adaptable methods for wastewater treatment plants with different capacities. This encompasses incorporating sustainable energy sources, creating effective control systems for small and medium-sized facilities, and assessing artificial intelligence and machine learning applications in wastewater treatment plants. Furthermore, it is crucial to prioritize the resolution of obstacles related to implementing sophisticated control systems in developing nations and finding solutions to enhance energy management practices. By matching control strategies with the specific requirements of WWTPs, substantial enhancements in energy efficiency and environmental sustainability can be attained, hence promoting the overall objectives of operational efficiency and environmental stewardship. Future studies should validate findings in both laboratory and real-world contexts to ensure their practical application and usefulness.

This paper’s originality lies in the analysis and proposal of tailored control techniques for WWTPs based on their scale. In addition, this research integrates cutting-edge technology, such as AI and ML, to improve energy efficiency. The observed benefits in energy preservation and operational improvements in all scales of wastewater treatment plants highlight the significant potential for enhancing sustainability in wastewater treatment operations. These findings’ practical relevance and scalability are demonstrated by their successful applications in lab-based and real-world situations.