An Overview of the Latest Developments and Potential Paths for Artificial Intelligence in Wastewater Treatment Systems

Abstract

As a rapidly developing and potent instrument for resolving practical issues, artificial intelligence (AI) has garnered considerable interest and has been widely used in many different domains. Diverse AI models have also been used in wastewater treatment (WWT) to optimize processes, forecast efficiency, and assess performance in order to explore high-efficiency and cost-effective solutions because of their remarkable learning and predictive capabilities. This review gathers the latest developments and applications of AI technologies in wastewater treatment plants and carefully examines the application and outcomes of various AI models, including artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), and deep learning (DL), in domains such as water quality monitoring, process optimization, fault diagnosis, membrane fouling prediction and control, and resource recovery. This study examines the benefits of these models in real-world engineering applications through a comparison with traditional approaches, as well as current issues like data collection and model generalization. Additionally, it looks to the future, where AI will be used in conjunction with emerging technologies like cloud computing, big data, and the Internet of Things (IoT) to drive the automated and intelligent advancement of wastewater treatment.

1. Introduction

As a vital component supporting life on Earth, water resources are under threat from both the sharp rise in pollutant types and the acceleration of industrialization [1]. Reports indicate that an estimated 300–400 billion tons of pollutants are released into water bodies worldwide annually, causing serious water pollution and placing a significant strain on water treatment systems [2]. A key indicator of water pollution is the biological oxygen demand (BOD), which is the quantity of oxygen needed by microorganisms to break down organic matter in water. Chemical oxygen demand (COD) is a measure of the level of organic matter pollution in water bodies. Furthermore, novel genetic pollutants, nanomaterials, and drug residues have been detected in water [3]. Sedimentation, clarification, and biological treatment are basic steps in typical wastewater treatment procedures. These techniques efficiently remove most organic matter and solid contaminants from water. Water quality reaches a certain level of purification after the initial treatments. However, more sophisticated treatment technologies like freshwater reverse osmosis (RO) and nanofiltration (NF) are still required to meet higher water quality standards [4]. These advanced treatment procedures can further eliminate dissolved materials, fine particles, and new contaminants (e.g., pharmacological residues and nanomaterials), guaranteeing adherence to usage guidelines. Many sewage treatment facilities have embraced conventional wastewater treatment techniques, such as biological and physicochemical processes. However, they have notable drawbacks in terms of energy usage, treatment effectiveness, and operating expenses. Innovative technologies are urgently needed in wastewater treatment facilities to increase productivity, reduce expenses, and use less energy [5].

Artificial Intelligence (AI), one of the 21st century’s most revolutionary technologies, has developed rapidly and is widely used in domains such as computer vision (CV), autonomous driving, and natural language processing (NLP) [6]. AI provides data-driven solutions for difficult problems and simulates human intelligence through independent reasoning and decision-making [7]. Through effective algorithms and big data analysis, AI can classify and regress large amounts of data in real time in the wastewater treatment industry, intelligently supporting the optimization of wastewater treatment procedures. AI technology has advanced significantly in data analysis, prediction, and optimization in recent years, opening up new wastewater treatment options. AI technologies are able to extract valuable patterns from large amounts of wastewater data by mimicking human learning and decision-making processes. This allows for precise control and dynamic process optimization [8]. Significant advancements in AI-driven data analysis, prediction, and optimization of wastewater treatment have been made in recent years [9]. Data-driven modeling and dynamic optimization capabilities offer new ways to upgrade critical stages of wastewater treatment plants. In water quality monitoring, AI allows for the accurate and real-time prediction of parameters like COD, BOD, and total nitrogen (TN), effectively replacing delayed manual testing. In process optimization, AI dynamically adjusts parameters like aeration and chemical dosing, to significantly reduce energy consumption. In fault diagnosis, anomaly early warning systems are established to improve operational reliability. In membrane fouling control, AI predicts fouling trends to prolong membrane lifespan and lower maintenance costs. In resource recovery, AI optimizes phosphorus and nitrogen recovery efficiency and sludge-to-energy conversion pathways, improving resource utilization and economic benefits.

Although the application of AI in wastewater treatment has attracted increasing attention [10], most of the current research focuses on a specific technology or process design, and there is a lack of a systematic and comprehensive review of AI models commonly used in wastewater treatment plants. This disparity makes it difficult for researchers and practitioners to assess the relative benefits and practical efficacy of different AI models in real-world engineering settings. This study aims to systematically combine popular AI models in wastewater treatment, such as Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), and Deep Learning (DL) architectures, in order to overcome this limitation. We will critically assess the state of research and application of these AI technologies in several important areas, including fault diagnosis, process optimization, and water quality monitoring, while weighing their advantages and disadvantages. A thorough assessment of the present and potential future of AI technologies is necessary due to their rapid development and broad use in wastewater treatment. Researchers and practitioners can gain a more thorough understanding of how AI can improve treatment effectiveness and promote sustainability through this consolidation, which offers an organized reference framework. This review provides guidance for future research directions and summarizes recent research. Finally, it examines the future development of AI in wastewater management, assuming that as data collection technologies and intelligent algorithms continue to advance, AI will be able to provide more accurate, effective, and perceptive water treatment solutions. This development promises strong support for water security and environmental preservation.

2. Artificial Intelligence in Wastewater Treatment Plants (WWTPs)

2.1. Evolution of Artificial Intelligence in Wastewater Treatment Plants

AI technologies in wastewater treatment plants have gradually moved from laboratory research to real-world engineering applications due to advances in computational power during the big data era. Data-driven modeling techniques are progressively replacing traditional statistical methods, with machine learning emerging as a key subfield that offers new possibilities for accurate modeling and wastewater treatment process optimization. As shown in Figure 1, the development of wastewater treatment technology has undergone a transformative leap from conventional modeling approaches to highly intelligent systems. In the early stages, reliance was placed primarily on statistical and mathematical modeling methods—such as ARIMA, regression analysis, and Activated Sludge Models (ASM)—combined with expert systems and fuzzy logic. These enable trend prediction and basic early warning functions based on offline data analysis [11]. Entering the 2000s, advancements in computational power and sensing technologies facilitated the integration of methods like Principal Component Analysis (PCA) and Advanced Process Control (APC) [12]. This allows for finer online monitoring, fault detection, and process optimization. During the 2010s, machine learning algorithms—including Support Vector Machines (SVM), Random Forests (RF), XGBoost, and Long Short-Term Memory networks (LSTM)—were widely applied to real-time sensor data analysis. These not only achieved precise prediction of key effluent indicators (e.g., COD and NH₃-N concentrations) but also supported proactive maintenance and regulation. By the 2020s, cutting-edge technologies such as deep learning and graph neural networks had rapidly evolved. Convolutional Neural Networks (CNN) were adopted for image and video monitoring, while reinforcement learning (RL) enabled adaptive process control. Integrated with Digital Twin technology, these innovations empower wastewater treatment plants to achieve self-optimization and fully closed-loop intelligent operations, spanning from unit processes to entire systems [13].

The primary statistical methods for optimizing early multivariate process design are the response surface method (RSM) and its constituent parts, including the central composite design (CCD). RSM’s fundamental idea is to optimize system input parameters by developing mathematical models to approximate objective functions [14]. This method has been widely used in both scientific and industrial fields, especially in experimental processes that require multi-variable optimization. While these techniques have certain benefits in parameter screening, they are frequently overstretched when dealing with multivariate, nonlinear, and time-varying characteristics. AI-driven models—such as Deep Learning (DL), Support Vector Machines (SVM), and Artificial Neural Networks (ANN)—are technologies trained on large-scale datasets with adaptive learning capabilities, primarily designed to discover patterns for predictive analytics and optimization. Unlike the traditional statistical modeling approach of Response Surface Methodology (RSM), AI models leverage robust computational power and big data to address highly complex nonlinear relationships and dynamic process optimization challenges. Researchers can now create more intricate and flexible prediction models using algorithms that automatically discover the inherent laws of the data, owing to the development of artificial neural networks (ANN) and machine learning techniques. Specifically, the prediction of contaminant removal and optimization of certain process parameters have demonstrated high accuracy and robustness using decision tree (DT), support vector machine (SVM), random forest (RF), and integrated learning techniques [15]. The in-depth development of process intelligence is further supported by deep learning and GNN models, which further capture subtle features in vast amounts of data.

The use of AI has gradually progressed to deeper levels as data volume and computing power have increased [16]. Deep learning (DL), which uses multilayer neural network architectures, is particularly effective for modeling complex process dynamics and extracting high-level features from data. Models that closely resemble real reaction network structures can be created using graph neural networks (GNN), which provide new perspectives on the mechanisms underlying microbial community succession and pollutant migration pathways. The combination of artificial neural networks (ANN) and Response Surface Methodology (RSM) not only compensates for the nonlinear modeling shortcomings of traditional approaches but also improves model interpretability and practical control capabilities. At the same time, the integration of AI techniques with traditional statistical methods is continuously improving [17]. The role of AI in wastewater treatment has fundamentally changed as a result of these technological developments, moving from an auxiliary analytical tool to an increasingly important core decision-making engine [18,19].

2.2. Commonly Used Algorithms and Models in Wastewater Treatment Plants

Multi-variable coupling is a complex feature of wastewater treatment systems (e.g., the choice of suitable algorithms and models is essential for attaining precise control and effective treatment because of the nonlinear dynamic response (pH, dissolved oxygen, and sludge concentration), as well as other factors). Currently, machine learning algorithms, conventional statistical techniques, and artificial neural network (ANN)-based models are mostly employed; each has its own special benefits and works in tandem to advance process optimization. The categorization of popular AI models in intelligent modeling of wastewater treatment plants clearly defines their functions and roles, as shown in Figure 2. Performance metrics like adsorption capacity and decolorization rate are commonly predicted using artificial neural networks (ANN), which are well-known for simulating complex and nonlinear processes. Recurrent and feedforward networks are examples of typical architectures [20,21]. Researchers frequently combine ANN and Response Surface Methodology (RSM) to create hybrid models that improve model interpretability and robustness. This allows process optimization that combines data-driven high accuracy with the physical significance of the process parameters [22]. However, in order to identify important characteristics and patterns in wastewater treatment processes, machine learning techniques like decision trees (DT), support vector machines (SVM), random forests (RF), and gradient boosting machines (GBM) are trained on sizable historical datasets. Pollutant classification and performance prediction tasks are well-suited for decision trees and random forests because of their superiority in managing nonlinear relationships and variable interactions. Support vector machines (SVM) are frequently used to assess adsorbent performance and optimize parameter selection because of their advantages in small-sample learning and high-dimensional data processing. A few well-known AI models and algorithms are shown in Figure 2. Optimization algorithms like particle swarm optimization (PSO) and genetic algorithms (GA) are frequently used for parameter tuning, including dosing optimization and aeration strategy improvement. By combining several base models, ensemble learning techniques can increase prediction stability and lower the chance of overfitting, resulting in more accurate modeling of intricate multivariate systems.

Even though a number of models have shown remarkable promise in wastewater treatment applications, they continue to have drawbacks like a strong dependence on training data and a lack of generalization ability. Researchers are actively investigating enhancement strategies, such as deep hybrid architectures, ensemble methods, and regularization techniques, to overcome these limitations. Furthermore, it is expected that the new deep learning and graph neural network models will further overcome the limitations of existing technologies by combining multi-scale information fusion and deeper feature extraction, providing better solutions for dynamic prediction of complex processes [23].

3. Artificial Intelligence in Wastewater Treatment Plants: State-of-the-Art and Progress

Intelligent decision-making systems in wastewater treatment plants have extensively incorporated AI models, showing notable benefits in a number of crucial domains. Process parameter prediction, abnormal operating condition detection, dynamic aeration and chemical dosing optimization, sludge bulking early warnings, and pollution source tracing in pipeline networks are some of these. These technologies have significantly improved wastewater treatment and resource utilization efficiency by combining multi-source sensor data with historical operational records.

As AI technology develops, more research is being conducted on the application of these models in real-world wastewater treatment engineering projects. The performance of various models in water quality prediction, anomaly detection, and process optimization is among the common AI models and their particular uses in wastewater treatment facilities, as shown in

Table 1. Representative applications of artificial intelligence models in WWTPs.

Application Scenario	AI Models/Algorithms	Research Parameters & Ranges	Key Function	Applied Results	Reference
Water quality parameter prediction	DA-LSTM neural network	COD: 394–3000 mg/L; TP: 0.09–1 mg/L; etc.	Predicting effluent COD, TP, TN	COD validation set R2 improved from 0.93 to 0.98	[24]
Anomaly detection and classification	Decision Tree (DT)	DO: 0–6 mg/L; NH3-N: 0–20 mg/L; etc.	Identify sensor faults and process anomalies	Achieved 92% recall and 61% precision	[25]
Aeration optimization control	Deep Reinforcement Learning (DRL)	Influent quality simulated from full-scale MBR plant; DO setpoint: 2–7 mg/L	Dynamically adjust aeration intensity	33.18% reduction in energy consumption while maintaining treatment efficiency	[26]
Process parameter prediction	ANN + ANFIS + SVR hybrid model	15 parameters (COD, TN, MLSS, etc.)	Predict 15 parameters, including MLSS, TN, and COD	5% average accuracy increase over individual base models	[27]
Treatment process optimization	Artificial Neural Network (ANN)	Aeration Energy (AE), Effluent Quality (EQ), Pumping Energy (PE)	Optimize control setpoints based on predictive results	Total energy consumption reduced by 6.85%	[28]
Membrane fouling prediction	Random Forest (RF)	COD: 340 mg/L–20 g/L; VSS: 1.10–37.00 g/L; Pore size: 0.08–0.5 μm; Packing density: 1.53–35.71 m2/m3	Predict the membrane fouling rate and quantify factors	Extended membrane lifespan by 30%, reduced maintenance costs by 25%	[29]

. For example, DA-LSTM neural networks have been used to predict trends in important indicators like COD, TP, and TN in the context of water quality parameter prediction. Artificial Neural Networks (ANN) have improved wastewater treatment plant operations through process optimization, resulting in a significant reduction in energy consumption. Random Forest (RF) models have effectively increased membrane service life in membrane fouling prediction.

Current research on AI models primarily focuses on laboratory-scale experiments and process design stages, often relying on simulated data or limited-scale datasets. However, practical engineering applications—particularly those involving process parameter optimization and performance prediction—require extensive, real-world historical data, especially water quality monitoring data. With the advancement of data acquisition technologies and continuous refinement of AI models, these techniques are progressively transitioning toward real-world engineering deployment.

3.1. Water Quality Monitoring

In order to meet the need for timely tracking of critical parameters during wastewater treatment processes, traditional water quality characterization is changing from laboratory analysis to online monitoring, owing to the development of real-time sensors, the Internet of Things (IoT), and data-driven models [30]. Because of their capacity to learn from and make predictions based on data from multiple sources, AI technologies have emerged as essential instruments for improving the effectiveness and precision of water quality monitoring. They have been extensively used in a variety of water environments, such as drinking water, groundwater, surface water, and wastewater (Figure 3). Artificial neural networks (ANN) are widely used for predicting disinfection byproducts and optimizing chemical dosing in drinking water; graph neural networks (GNN) are excellent at detecting pipeline network faults and anomalies; random forests (RF) and support vector machines (SVM) are commonly used for source identification and anomaly detection in groundwater and surface water pollution; and Long Short-Term Memory (LSTM) networks are well-suited for handling complex time-series data, such as effluent water quality prediction in wastewater treatment. In particular, AI has shown notable benefits in wastewater treatment facilities in recent years for the detection of traditional water quality parameters like total nitrogen (TN), chemical oxygen demand (COD), biological oxygen demand (BOD), and total dissolved solids (TDS).

Table 2. Research on the Application of Artificial Intelligence in Water Quality Monitoring.

Model	Input Variables	Target Parameter	Results	Advantages/Limitations	Reference
Long Short-Term Memory (LSTM)	Inflow/outflow water parameters from a WWTP	COD	COD prediction error rate: 7%	Captures temporal features; requires long training time	[35]
TL-LSTM	Inflow indicators, operational parameters, and effluent data from WWTPs	NH3-N	Prediction performance: R2 = 0.811, RMSE = 0.627 mg/L	Transfer learning improves generalization; data-intensive	[36]
Artificial Neural Network (ANN)	Current/voltage in microbial fuel cell (MFC) biodegradation	BOD5	Average error: 7%	Simple structure; moderate accuracy	[34]
Extreme Gradient Boosting (XGBoost)	234 PFAS compounds from 64 studies	PFAS micropollutants	pH identified as the most critical predictor for PFAS removal	High accuracy; limited interpretability	[32]
Hybrid dynamic model	Two-year operational data from A2O + AO processes	TN	TN prediction error range: 9.4–15.5%	Integrates multiple model strengths; high	[31]
Backpropagation Neural Network (FBPNN)	Two-year water quality data from municipal WWTPs	NO3−-N	Prediction performance: R2 = 99.38%, RMSE = 0.12 mg/L	High precision; prone to overfitting	[33]

summarizes the performance of various artificial intelligence (AI) models in predicting target parameters within wastewater treatment and water quality forecasting in recent years, with each model demonstrating unique advantages under diverse operating conditions and data scenarios [31,32,33,34,35,36]. For example, Long Short-Term Memory (LSTM) networks effectively capture temporal dependencies in influent/effluent process data, achieving a 7% error rate in Chemical Oxygen Demand (COD) prediction and proving suitable for scenarios requiring precise dynamic responses; Transfer Learning combined with LSTM (TL-LSTM) outperforms traditional methods in cross-scenario generalization for ammonia nitrogen (NH₃-N) forecasting (R² = 0.811, RMSE = 0.627 mg/L); Artificial Neural Networks (ANN), known for structural simplicity, maintain moderate accuracy with ~7% average error in BOD₅ prediction for biodegradation processes like Microbial Fuel Cells (MFC). Integrated learning approaches, such as Extreme Gradient Boosting (XGBoost), achieve high precision in PFAS micropollutant modeling (R² up to 0.92) by identifying pH as a key predictive factor, although with limited interpretability. Hybrid dynamic models integrating multiple approaches attain 9.4–15.5% error in Total Nitrogen (TN) prediction, particularly suited for long-term analysis of complex coupled processes. Backpropagation Neural Networks (FBPNN) deliver exceptional precision in nitrate nitrogen (NO₃⁻-N) forecasting (R² = 99.38%, RMSE = 0.12 mg/L), yet require careful overfitting mitigation.

The development of single-parameter prediction models was the main focus of previous research. For example, Singh et al. [37] compared and discovered that the LSTM model was able to predict COD dynamically (R² = 0.92). This innovation paved the way for the creation of soft sensors by combining time series data like DO and pH, which had a higher prediction accuracy than the conventional multivariate statistical methods. Haimi et al. [38] integrated electrochemical sensor data with neuro-fuzzy systems to achieve dynamic BOD estimation (R² = 0.89) in wastewater treatment plants. Using a novel strategy, Carreres-Prieto et al. [39] established a new paradigm for spectroscopy-machine learning hybrid systems by showcasing the potential of visible spectroscopy (497–570 nm green band) in conjunction with genetic algorithm (GA)-optimized models for precise multi-parameter prediction (COD, BOD5, TSS, TN, and TP).

Advancements in hardware platforms have fueled upgrades to multi-parameter detection technologies. By incorporating inexpensive sensors, Minchala et al.’s Industrial Internet of Things (IIoT) system [40] made it possible to acquire six parameters in parallel, including pH, DO, and COD. Its enhanced FGS-PID algorithm also drastically reduces the overshooting and stabilization time for dissolved oxygen control. However, problems with sensor stability continue to limit the reliability of long-term monitoring. The accuracy of measurements can be greatly impacted by unmaintained DO probes, which can develop a baseline drift of 0.2–0.5 mg/L per month. Nair et al. [41] suggested a dynamic calibration model that successfully suppressed the sensor drift phenomenon by combining the process parameters (e.g., sludge age and reflux ratio) with extended Kalman filtering (EKF), which effectively mitigates sensor drift through recurring calibration and offers assurance of the reliability of the data in intricate operational scenarios.

Single-parameter detection is giving way to multi-dimensional intelligent analysis as the current technological frontier develops. Regarding the precision of detection, CNN and deep-ultraviolet laser-induced Raman spectroscopy were combined to detect micropollutants with high sensitivity (detection limit up to μg/L level) [42]. At the system integration level, Dai et al. [43] created a dynamic control model to optimize dosing and aeration strategies by establishing real-time correlations between COD, TN, TP, and other parameters, resulting in an approximate 24 percent reduction in energy consumption. 3% in an actual wastewater treatment facility while ensuring that the effluent water quality satisfies regulations. These developments demonstrate that AI has not only found significant optimization potential through thorough inter-parameter correlation analysis but also resolves the lag issue of conventional water quality testing. To unlock the greater value of intelligent water quality monitoring throughout the entire region, we must further develop the model’s generalization ability under the influence of complex matrices and create a standardized data governance framework across all plants.

3.2. Process Optimization and Energy Saving

The use of AI technology in wastewater treatment has changed from empirical regulation to dynamic optimization based on precise data. Key parameters in the wastewater treatment process can be precisely predicted, and process setpoints can be changed in real time by utilizing AI models, particularly machine learning algorithms like artificial neural networks (ANNs) and support vector machines (SVMs). This significantly increases the stability and accuracy of the process.

Table 3. Case studies of artificial intelligence for process optimization in WWTPs.

Model/Method	Application Scenario	Input Variables	Objective	Results/Performance	Reference
ANN-GA Hybrid Model	Dye Wastewater Treatment	Reaction time, flow rate, current density, pH, initial dye concentration	Optimize electro-oxidation parameters for enhanced decolorization efficiency	Achieved 88.8% decolorization (close to model-predicted 95.5%)	[44]
SOM + K-means Clustering	Activated Sludge Wastewater Treatment	Dissolved oxygen (DO), oxidation-reduction potential (ORP)	Identify operational modes for aeration control optimization	Extracted key parameters (ORAS, ORP, OUR)	[45]
ANN-ANFIS-RSM Hybrid Model	Textile Wastewater Biosorption	Temperature, pH, biosorbent dosage, dye concentration	Predict methylene blue (MB) adsorption performance and optimize conditions	Optimal MB removal: 74.49% (R2 > 0.9)	[46]
GRU Neural Network	Industrial Wastewater	Flow rate, pH, temperature, DO, real-time COD	Predict COD concentration for process stability	Superior accuracy over LSTM and SVR	[47]
CLSTMA Deep Learning Model	Papermaking Wastewater	Influent/effluent COD, suspended solids (SS), flow rate, pH, temperature, DO	High-precision BOD/COD prediction for reuse cost optimization	Prediction improvements: SS (8.29–11.86%), COD (15.13–37.21%)	[48]
IFFNN-LSSVM Hybrid Model	Effluent Quality Prediction in WWTPs	Temperature, conductivity, turbidity, total dissolved solids (TDS), other physical parameters	High-accuracy effluent prediction with low computational cost	Lower error vs. GWO/FFNN benchmarks	[49]

provides examples of how AI models have been used to optimize wastewater treatment processes. AI models effectively optimize every step of the wastewater treatment process, which not only increases process efficiency but also enables energy use that makes sense. When applied to the electro-oxidation process of dye wastewater, for instance, the artificial neural network-genetic algorithm (ANN-GA) can optimize the process parameters, increase the decolorization efficiency to 88%, and significantly reduce energy consumption, reducing electrical energy consumption by roughly 20% when compared to traditional methods [44]. Furthermore, the use of AI technology in wastewater treatment has demonstrated significant benefits in real-world engineering and is not restricted to laboratory testing. For instance, the K-means clustering algorithm and self-organizing mapping neural network (SOM) were utilized to extract important parameters like ORP and OUR, and optimize aeration control in activated sludge sewage treatment, effectively increasing the effectiveness of the treatment. Optimization of aeration control also significantly reduces energy consumption [45]. With a removal rate of 74.49% and an R² > 0.9, the ANN-ANFIS-RSM hybrid model significantly improved the adsorption performance of MB dye and had a significant impact on the bioadsorption process of textile wastewater. In addition to improving treatment outcomes, this optimization process lowers operating expenses and the quantity of chemical agents used, which saves energy and resources during the treatment process [46].

The precise regulation of biometabolic processes in biochemical treatment units is driven by intelligent algorithms. For example, Jin et al. [50] used machine learning to reconstruct the microalgal metabolic network, which greatly increased biomass yield and CO₂ fixation efficiency, opening up a new avenue for carbon capture and resourcing. This optimization not only improves the biomass yield of wastewater treatment but also achieves effective CO₂ capture, achieving the dual goals of environmental protection and energy efficiency. Seshan et al. [51] integrated the LSTM predictor into the Model Predictive Control (MPC) framework, utilizing dynamic dissolved oxygen (DO) concentration adjustment to achieve the best possible balance between energy consumption and nitrogen removal efficiency. The ability of this system to predict 0.5–6 h in advance offers crucial operational support for process stability. When dealing with variations in influent quality, these dynamic regulation techniques successfully reduce the intrinsic response delays of traditional activated sludge systems.

Simultaneously, synergistic improvement of material properties and process parameters is the main focus of physicochemical treatment unit optimization. Qi et al. [52] created nanohybrid materials that, when optimized using the ANN-PSO model, demonstrated outstanding dye adsorption performance. Additionally, their reusability significantly decreases the operating costs. In order to achieve high accuracy and efficiency for decentralized wastewater treatment, Aghilesh et al. [46] used agricultural waste to build an inexpensive adsorption system. This was accomplished using the ANFIS model. Process optimization offers a cost-effective method for decentralized wastewater treatment. Furthermore, Picos-Benítez et al. [44] verified the special value of intelligent algorithms in multi-objective optimization by combining ANN-GA with an electrochemical process to drastically lower energy consumption while maintaining treatment efficiency.

AI technology not only achieves energy savings by optimizing process parameters, but also improves the stability and accuracy of the processing process. Li et al. [53] created the 1D-CNN model, which uses multilevel data fusion to identify microplastics with high precision, and its built-in spectral database offers a fresh approach to pollutant traceability. Miao et al.’s GRU-IoT early warning system complements this technology [47], where the latter uses real-time data collection to respond quickly to process anomalies and the former guarantees precise control of water quality. Together, these two components create an intelligent optimization network that spans the entire process.

3.3. Fault and Abnormality Diagnosis

Wastewater treatment systems are extremely vulnerable to sensor failures, process fluctuations, and external disturbances, which can result in operational abnormalities and treatment failures. These systems often operate in complex and volatile environments. Conventional fault detection techniques based on empirical rules or physical models are insufficient for processing high-dimensional, multi-source, and nonlinear data. AI technology, particularly data-driven intelligent diagnosis techniques, has been progressively used in sewage treatment processes in recent years to identify faults and detect anomalies [54]. Principal component analysis (PCA), for instance, is frequently employed in multivariate process monitoring and is appropriate for detecting system anomalies in steady-state or slowly changing environments [54,55]. A number of enhanced techniques, including incremental PCA (IPCA), recurrent PCA (RPCA), and sliding window PCA, have been proposed to improve diagnostic robustness and adjust in real time to changes in working conditions because traditional PCA frequently produces false positives when faced with parameter changes or dynamic perturbations [56,57].

Independent component analysis (ICA) and its nonlinear extended kernel ICA have been introduced by some scholars to further improve the fault identification ability of nonlinear and higher-order coupling processes. They extracted independent features from mixed signals and combined the statistics SPE and I² to discriminate anomalies, achieving more accurate and efficient monitoring capabilities than traditional ICAs [58]. Meanwhile, when combined with ARMA models, neural networks—particularly auto-associative neural networks, or AANNs—have demonstrated robust learning and generalization abilities in handling sensor failures and missing data, and they have accomplished multi-step prediction and early warning [59]. By creating a working condition library to accomplish automatic fault identification and localization, hybrid models—such as first-principles models coupled with neural networks—further integrate the benefits of mechanism simulation and historical data fitting and produce good application results in the industrial wastewater treatment process [60].

Advanced techniques like the sparse Bayesian transfer learning framework (EAdspB-TLM) and variational Bayesian canonical correlation analysis (VBMCCA), have also been introduced for fault diagnosis in the face of high uncertainty and data scarcity. These techniques not only enhance quality-related variable diagnosis and prediction but also enable model migration and generalization across various operating environments, greatly enhancing model adaptability in small-sample settings [61,62]. Furthermore, real-time anomaly detection and sensor fault isolation have been effectively accomplished in practical projects like sewage plants using techniques like sliding window PCA [46], while DIYBOT platforms for small or distributed systems have accomplished remote control monitoring and emergency response capabilities through phase space reconstruction and other techniques [63]. Wastewater treatment facilities are also moving toward intelligent process monitoring and virtual and real integration, owing to the idea of digital twins; however, they still have many obstacles to overcome in the areas of model building, data integration, and lifecycle management [64].

The digital twin (DT) architecture, which has gradually emerged in recent years, offers a “virtual and real integration” solution for the predictive diagnosis and emergency response of sewage treatment systems, further overcoming the limitations of current AI methods in terms of timeliness, responsiveness, and global optimization. The architecture couples the virtual model and the real world in real time using the closed-loop mechanism of “monitoring-modeling-decision-execution” to achieve intelligent management of the entire failure process. The typical application procedure for this architecture is depicted in Figure 4 and is based on the sensor data in the actual process unit (e.g., key operating parameters (e.g., aeration tank and secondary sedimentation tank), the mechanism model (e.g., activated sludge dynamics model), and AI algorithm (e.g., LSTM timing prediction) are combined to perform fault identification and trend deduction, and the optimization instructions are then fed back to the physical system to achieve dynamic regulation and intervention optimization.

3.4. Membrane Contamination Prediction and Control

Membrane fouling is the main operational challenge in membrane separation technologies (such as MBR, RO, and NF) and is primarily a result of increased TMP, lower flux, and higher cleaning frequency. Several factors affect this process, including complex coupling mechanisms (such as SMPs, extracellular polymers (EPS), particle deposition, biological contamination, and inorganic fouling), as well as complex coupling mechanisms (such as soluble microbial products (SMPs), extracellular polymers (EPS), particle deposition, biological contamination, and inorganic fouling) [65]. The contamination characteristics of the membrane surface in RO and NF systems can be considerably altered by variations in operating conditions and influent water quality, exhibiting greater nonlinearity and uncertainty [66]. In addition to impairing membrane performance, fouling increases energy consumption and cleaning chemical usage. In contrast to conventional activated sludge systems that prioritize the effectiveness of pollutant removal (e.g., COD and TN), membrane systems focus more on the declining trend of the operating state. Therefore, rather than increasing the efficiency of biological processing itself, the aim of AI models in this field is to forecast the dynamic changes in membrane operation indicators and optimize the operation strategy accordingly. However, because conventional experimental methods struggle to capture contamination dynamics in real time, researchers are now turning to AI-assisted prediction and control techniques.

It is challenging to achieve continuous and accurate process perception and fault prediction using traditional methods that rely on manual experience or univariate monitoring because of the concealment and hysteresis of membrane fouling. In order to achieve this, scientists have built a membrane fouling prediction model by progressively introducing AI. Early predictive models were based on conventional statistical techniques. For example, Han et al. [67] developed a correlation model between membrane permeability and sensor data using partial least squares (PLS) in conjunction with recursive fuzzy neural networks, and they partially achieved the short-term prediction of TMP changes. However, this approach lacks robustness and is heavily reliant on linear assumptions. The stacked noise reduction autoencoder (SDAE) developed by Shi et al. [68] indirectly improves the prediction accuracy of membrane fouling trends (e.g., TMP growth) by 15–20% through deep feature extraction and uses water quality parameters such as NH₄⁺-N and TN as input variables. However, it faces the problem of easy overfitting in long-term predictions and relies heavily on high-frequency data.

To increase the model’s stability and capacity for generalization, Wang et al. [69] combined the SHAP interpretability framework with the Optuna automatic hyperparameter optimization algorithm, and optimized the multilayer perceptron (MLP) model to achieve a TMP prediction R² of 0.9183 (RMSE = 2.408 kPa). This demonstrates a strong nonlinear relationship between aeration intensity and TMP. Meanwhile, group learning techniques (e.g., due to their feature selection capabilities [70,71,72,73]), such as the ranking evaluation of the impact of COD, MLSS, and other operating parameters on membrane fouling (

Table 4. Research on Artificial Intelligence Methods for Membrane Fouling Prediction and Control.

Model/Method	Study Object	Input Variables	Objective	Results/Performance	Reference
Feedforward Neural Network (FFNN)	Membrane Bioreactor (MBR)	MLSS, HRT, time	Predict COD removal efficiency and transmembrane pressure (TMP)	HRT reduction led to smaller sludge particles; MLSS showed the strongest correlation with TMP	[70]
Feedback Neural Network (FNN)	Desalination/WWTP filtration modules	Hydrodynamic parameters of the filtration process	Predict pressure drop and (bio)fouling growth on ultrafiltration membranes	Quantified biofilm thickness on membrane surface; correlated biofilm development with hydrodynamic parameters	[71]
Artificial Neural Network (ANN)	AO + MBR system	pH, alkalinity, DO, COD, TN, TP, nitrate	Identify the most relevant variables for TMP prediction	TN-TP-nitrate combination showed the strongest predictive power for TMP	[72]
Machine Learning (ML) + NMR spectroscopy	Aquaculture water membrane filters	NMR spectra of foulant components	Predict maximum TMP as a fouling indicator	Polysaccharides identified as primary foulants contributing to membrane clogging	[73]
MLP & RBF ANN	Submerged MBR (SMBR)	Time, COD, TSS, SRT, MLSS	Predict TMP and membrane permeability (Perm) under alternating aeration	TMP increased while Perm decreased with operation time; GA-optimized ANN showed higher accuracy	[74]
Decision Tree (DT)	Tertiary wastewater NF membrane	Pressure, TOC, pH, conductivity	Predict permeate flux decline	High TOC (>9.38 mg/L), high conductivity (>1564 mg/L), and high pressure caused significant flux decline	[75]
PCA + Fuzzy Clustering	MBR fouling assessment	TMP datasets from lab-scale MBR	Monitor and control membrane fouling	Successfully extracted fouling control parameters (filtration cycle status, aeration rate) from TMP data alone.	[76]

). For example, XGB and random forest are frequently used for key variable identification. However, the model must be repeatedly modified for various scenarios due to the intricate relationship between the membrane type, process parameters, and pollution mechanism, which severely limits the effectiveness of engineering promotion. Furthermore, deep architectures like Feedforward/Feedback Neural Networks (FFNN, FNN) excel in transmembrane pressure (TMP) prediction and identifying patterns in transmembrane pressure differential changes by capturing nonlinear relationships. Methods such as Principal Component Analysis (PCA) and Fuzzy Clustering (FC) have advantages in multivariate dimensionality reduction, pattern mining, and operational state classification.

In terms of membrane fouling control optimization, AI technology is still mainly limited to laboratory research, and its engineering applications face significant bottlenecks. Mirbagheri et al. [74] quantified the effect of aeration mode on transmembrane pressure (TMP) for the first time by combining genetic algorithms with neural networks, but their model was only validated in a small pilot device and did not solve the problem of parameter drift at the engineering scale. Although the intelligent decision-making framework proposed by Jafari and Sim [66,77] can theoretically optimize membrane cleaning efficiency, its core relies on high-precision sensor continuous data, and most sewage plants find it difficult to meet their needs in terms of hardware deployment and data continuity. At present, two core obstacles hinder its engineering application: first, the scale mismatch between the response of the minute-level algorithm and the hourly fouling process leads to a control lag; second, there is a lack of a universal scheme for the dynamic trade-off of multi-objective optimization of membrane life, energy consumption, and other parameters. Although digital twin technology has high hopes, its demand for high-cost sensors and real-time computing power is currently difficult to match the actual conditions of the wastewater treatment industry.

3.5. Resource Recovery and Utilization

One of the main tactics to address the world’s water shortage and accomplish sustainable development goals is the creative use of wastewater [78]. Wastewater treatment facilities have made great strides in accurately monitoring and predicting the energy, chemicals, and nutrients in wastewater using AI, machine learning, and Internet of Things (IoT) technologies. For instance, dynamic control of important wastewater parameters is made possible by real-time data collection and prediction models (e.g., pH, dissolved oxygen, and suspended solids) to improve resource recovery efficiency and streamline procedures [79]. Furthermore, related research has demonstrated that implementing a circular economy model can lower operating costs while allowing high-value resources to be reused in industrial production and irrigated agriculture [80]. Fisher et al. [81] established a theoretical framework for the creation of intelligent management platforms by methodically demonstrating the crucial role that cross-industry data mining plays in wastewater resource management. Additionally, AI-assisted optimization techniques offer theoretical and practical underpinnings for the effective recovery of nutrients like nitrogen and phosphorus. For example, a number of studies have shown that the accurate recovery of important nutrients, such as phosphorus, from wastewater not only reduces pollution in the environment but also makes it possible to turn them into valuable agricultural resources [82].Additionally, membrane separation technology has gained significant attention as a wastewater resource recovery method, particularly for the graded utilization of wastewater and the recovery of high-value metals. On the one hand, reverse osmosis membrane systems and selective nanofiltration have been used to recover vital metal resources, such as lithium-magnesium separation from extremely salted wastewater or lithium battery industrial wastewater. Their energy consumption control and separation selectivity have emerged as research hotspots. However, multistage membrane-based wastewater grading is also essential for separating dyes, organic materials, and salts, making it easier to extract desired components from intricate mixed water streams [83].

Activated sludge has the potential to produce high-value biological products like organic acids, biofuels, manure, and pesticides in addition to directly recovering dissolved components from wastewater (Figure 5). In order to achieve the closed-loop utilization of the complete wastewater treatment process, AI is also frequently utilized in the resource utilization modeling of sludge and its derivatives. Applications in methane production, volatile fatty acid (VFA) recovery, and biohydrogen production, in particular, exhibit great promise. For instance, some researchers have suggested that by training ANN models, the effect of organic matter degradation in wastewater on hydrogen production can be predicted, improving the fermentation process and increasing the efficiency of hydrogen production when using organic matter-rich wastewater for anaerobic fermentation [84,85]. This procedure offers significant support for clean energy, in addition to aiding sludge energy recovery. Furthermore, Xu et al. promoted resource recovery and utilization in wastewater treatment by investigating the use of artificial neural networks (ANNs) and deep neural networks (DNNs) in the synthesis of volatile fatty acids (VFAs), which can be utilized for bioplastic synthesis or biodenitrification.

Therefore, AI technology has also been applied to the multi-objective optimization of thermochemical processes, such as sludge pyrolysis. The pyrolysis path can convert the remaining sludge into high-value-added products, such as bio-oil, biochar, and syngas, improving the overall level of resource recovery. For example, Naqvi et al. [86] pointed out that by combining thermogravimetric analysis (TGA) data with machine learning algorithms, reaction kinetic parameters (such as activation energy and maximum weight loss rate) can be accurately predicted, and the pyrolysis temperature and residence time can be optimized, improving product selectivity and energy efficiency, and providing a new direction for closed-loop energy management in wastewater treatment plants.

4. Conclusions

This study offers a methodical examination of the state of research and applications of AI in wastewater treatment facilities, highlighting noteworthy developments made in important fields such as resource recovery, process optimization, fault diagnosis, and water quality monitoring. AI has made it possible to predict crucial parameters online in real time (e.g., real-time water quality monitoring), thus circumventing the temporal and spatial constraints of conventional laboratory analysis (e.g., COD, BOD, TN) while achieving multi-parameter correlation using integrated spectroscopic and Internet of Things technologies. In terms of process optimization and energy conservation, a variety of algorithms, such as ANN-GA, SOM-K-means, and hybrid models, have significantly improved the processing efficiency of biochemical and physical units and reduced energy consumption. In order to diagnose faults and abnormalities, a closed loop of “monitoring, modelling, decision-making, and execution” has been established through the combined use of PCA, SVM, and digital twin technology. This has enabled the simultaneous diagnosis of several faults, ranging from local detection to system-level. The prediction and control of membrane contamination using a range of methods, such as PLS, SDAE, Optuna-optimized MLP, and integrated learning models, have greatly improved with flux and TMP predictions. Even though cost and response lag remain problems in engineering applications, the development of inexpensive soft sensors and real-time optimization algorithms will promote the enhancement of online control capability. AI-assisted optimization techniques have enabled the recovery and use of nitrogen, phosphorus, and high-value metals in wastewater treatment plants (WWTPs), aiding the shift to a circular economy model. However, issues with data collection and quality, a lack of model generalization capabilities, and insufficient tight integration with existing procedures have limited the widespread promotion and all-encompassing use of AI technology. Future research must focus on three key areas: cross-platform data standardization, seamless integration with edge computing technologies, and lightweight model architecture design in order to facilitate the practical application of AI in complex industrial wastewater treatment systems.