Artificial intelligence in wastewater treatment: A data-driven analysis of status and trends

Innovative approaches to greywater micropollutant removal: AI-driven solutions and future outlook

Greywater constitutes a significant portion of urban wastewater and is laden with numerous emerging contaminants that have the potential to adversely impact public health and the ecosystem. Understanding greywater's characteristics and measuring the contamination levels is crucial for designing an effective recycling system. However, wastewater treatment is an intricate process involving significant uncertainties, leading to variations in effluent quality, costs, and environmental risks. This review addresses the existing knowledge gap in utilising artificial intelligence (AI) to enhance the laundry greywater recycling process and elucidates the optimal treatment technologies for the most prevalent micropollutants, including microplastics, nutrients, surfactants, synthetic dyes, pharmaceuticals, and organic matter. The development of laundry greywater treatment technologies is also highlighted with a critical discussion of physicochemical, biological, and advanced oxidation processes (AOPs) based on their functions, methods, associated limitations, and future trends. Artificial neural networks (ANN) stand out as the most prevalent and extensively applied AI model in the domain of wastewater treatment. Utilising ANN models mitigates certain limitations inherent in traditional adsorption models, particularly by offering enhanced predictive accuracy under varied operating conditions and multicomponent adsorption systems. Moreover, tremendous success has been recorded with the random forest (RF) model, exhibiting 100% prediction accuracy for both sessile and effluent microbial communities within a bioreactor. The precise prediction or simulation of membrane fouling behaviours using AI techniques is also of paramount importance for understanding fouling mechanisms and formulating efficient strategies to mitigate membrane fouling.

Machine learning algorithms for predicting membrane bioreactors performance: A review

Membrane bioreactors (MBR) are recognized as a sustainable technology for treating polluted effluents. Machine learning (ML) algorithms have emerged as a modeling option to predict pollutant removal and operational variables such as membrane fouling, permeability, and energy consumption, which are significant challenges for MBR application. This review examines the use of ML algorithms in MBR-based wastewater treatment, focusing on the prediction of nitrogen and organic matter removal, and operational parameters related to membrane fouling. It presents the structures and fit quality of each model, noting that artificial neural networks (ANNs) are the most commonly used algorithm, appearing in 88 % of the 57 analyzed articles. Additionally, the review identified studies using random forests, support vector machines, k-nearest neighbors and boosting techniques, among other ML algorithms, although these were less frequently encountered. The review suggests potential in exploring less-utilized models for MBR data and identifies a gap in predicting membrane lifespan and replacement with ML models. This study aims to guide the development of new models for optimizing MBR performance by highlighting effective variables and algorithms, enhancing process control, real-time data analysis, parameter adjustment, and operational efficiency.

Bioaerogels from biomass waste: An alternative sustainable approach for wastewater treatment

The generation of municipal solid waste is projected to increase from 2.1 billion tonnes in 2023 to 3.8 billion tonnes by 2050. In 2020, the direct global cost of managing this waste was approximately USD 252 billion. When considering additional hidden costs-such as those arising from pollution, adverse health effects, and climate change due to inadequate waste disposal-the total cost escalates to USD 361 billion. Without significant improvements in waste management practices, this figure could nearly double by 2050, reaching an estimated USD 640.3 billion annually. Among municipal solid waste, biowaste accounts for roughly 44 % of the global municipal solid waste, translating to about 840 million tonnes annually. They are widely accessible and economical, offering a cost-effective alternative to traditional treatment materials. Transforming biomass waste into carbon-based materials (e.g., bioaerogels) is a sustainable practice that reduces waste and repurposes it for environmental remediation. This approach not only decreases the volume of waste directed to landfills and mitigates harmful greenhouse gas emissions from decomposition but also aligns with the principles of a circular economy. Furthermore, it supports sustainable development goals by addressing issues such as water scarcity and pollution while promoting waste valorization and resource efficiency. The unique properties of bioaerogels-including their porosity, multi-layered structure, and chemical adaptability-make them highly effective for the remediation of different water pollutants from aquatic bodies. This review article comprehensively delves into multifaceted wastewater remediation strategies -based bioaerogels such as coagulation and flocculation, advanced oxidation processes, membrane filtration, catalytic processes, water disinfection, Oil-water separation, biodegradation, and adsorption. Additionally, it examines different mechanisms of interaction such as surface adsorption, electrostatic interaction, van der Waals forces, ion exchange, surface precipitation, complexation, pore-filling, hydrophobic interactions, and π-π stacking. Moreover, it conducts an integrated techno-economic evaluation to assess their feasibility in wastewater treatment. By valorizing biomass waste, a closed-loop system can be established, where waste is transformed into valuable bioaerogels. This approach not only addresses challenges related to effluent pollution but also generates economic, environmental, and social benefits. Ultimately, the review underscores the transformative potential of bioaerogels in wastewater treatment, emphasizing their crucial role in supporting long-term environmental goals and advancing the principles of resource circularity.

Applications of artificial intelligence for chemical analysis and monitoring of pharmaceutical and personal care products in water and wastewater: A review

Specifying and interpreting the occurrence of emerging pollutants is essential for assessing treatment processes and plants, conducting wastewater-based epidemiology, and advancing environmental toxicology research. In recent years, artificial intelligence (AI) has been increasingly applied to enhance chemical analysis and monitoring of contaminants in environmental water and wastewater. However, their specific roles targeting pharmaceuticals and personal care products (PPCPs) have not been reviewed sufficiently. This review aims to narrow the gap by highlighting, scoping, and discussing the incorporation of AI during the detection and quantification of PPCPs when utilising chemical analysis equipment and interpreting their monitoring data for the first time. In the chemical analysis of PPCPs, AI-assisted prediction of chromatographic retention times and collision cross-sections (CCS) in suspect and non-target screenings using high-resolution mass spectrometry (HRMS) enhances detection confidence, reduces analysis time, and lowers costs. AI also aids in interpreting spectroscopic analysis results. However, this approach still cannot be applied in all matrices, as it offers lower sensitivity than liquid chromatography coupled with tandem or HRMS. For the interpretation of monitoring of PPCPs, unsupervised AI methods have recently presented the capacity to survey regional or national community health and socioeconomic factors. Nevertheless, as a challenge, long-term monitoring data sources are not given in the literature, and more comparative AI studies are needed for both chemical analysis and monitoring. Finally, AI assistance anticipates more frequent applications of CCS prediction to enhance detection confidence and the use of AI methods in data processing for wastewater-based epidemiology and community health surveillance.

Transition from sulfur autotrophic to mixotrophic denitrification: Performance with different carbon sources, microbial community and artificial neural network modeling

To address the limitations inherent in both sulfur autotrophic denitrification (SAD) and heterotrophic denitrification (HD) processes, this study introduces a novel approach. Three carbon sources (glucose, methanol, and sodium acetate) were fed into the SAD system to facilitate the transition towards mixotrophic denitrification. Batch experiments were conducted to explore the effects of influencing factors (pH, HRT) on the denitrification performance of the mixotrophic system. Carbon source dosages were varied at 12.5%, 25%, and 50% of the theoretical amounts required for HD (18, 36, and 72 mg/L, respectively). The results showed distinct optimal dosages for each of the three organic carbon sources. The mixotrophic system, initiated with sodium acetate at 25% of the theoretical value, demonstrated the highest denitrification performance, achieving NO3--N removal efficiency of 99.8% and the NRR of 6.25 mg/(L·h). In contrast, the corresponding systems utilizing glucose (at 25% of the theoretical value) and methanol (at 50% of the theoretical value) achieved lower removal efficiency of 77.0% and 88.4%, respectively. The corresponding NRRs were 4.85 mg/(L·h) and 5.65 mg/(L·h). Following the transition from SAD to a mixotrophic system, the abundance of Thiobacillus decreased from 78.5% to 34.4% at the genus level, and the mixotrophic system cultivated a variety of other denitrifying bacteria (Thauera, Aquimonas, Azoarcus, and Pseudomonas), indicating an enhanced microbial community structure diversity. The established artificial neural network (ANN) model accurately predicted the effluent quality of mixotrophic denitrification, which predicted values closely aligning with experimental results (R2 > 0.9). Furthermore, initial pH exerted greater relative importance for COD removal and sulfur conversion, while the relative importance of HRT was more pronounced for NO3--N removal.

Optimizing wastewater treatment through artificial intelligence: recent advances and future prospects

Artificial intelligence (AI) is increasingly being applied to wastewater treatment to enhance efficiency, improve processes, and optimize resource utilization. This review focuses on objectives, advantages, outputs, and major findings of various AI models in the three key aspects: the prediction of removal efficiency for both organic and inorganic pollutants, real-time monitoring of essential water quality parameters (such as pH, COD, BOD, turbidity, TDS, and conductivity), and fault detection in the processes and equipment integral to wastewater treatment. The prediction accuracy (R2 value) of AI technologies for pollutant removal has been reported to vary between 0.64 and 1.00. A critical aspect explored in this review is the cost-effectiveness of implementing AI systems in wastewater treatment. Numerous countries and municipalities are actively engaging in pilot projects and demonstrations to assess the feasibility and effectiveness of AI applications in wastewater treatment. Notably, the review highlights successful outcomes from these initiatives across diverse geographical contexts, showcasing the adaptability and positive impact of AI in revolutionizing wastewater treatment on a global scale. Further, insights on the ethical considerations and potential future directions for the use of AI in wastewater treatment plants have also been provided.

Modelling and optimization of membrane process for removal of biologics (pathogens) from water and wastewater: Current perspectives and challenges

As one of the 17 sustainable development goals, the United Nations (UN) has prioritized "clean water and sanitation" (Goal 6) to reduce the discharge of emerging pollutants and disease-causing agents into the environment. Contamination of water by pathogenic microorganisms and their existence in treated water is a global public health concern. Under natural conditions, water is frequently prone to contamination by invasive microorganisms, such as bacteria, viruses, and protozoa. This circumstance has therefore highlighted the critical need for research techniques to prevent, treat, and get rid of pathogens in wastewater. Membrane systems have emerged as one of the effective ways of removing contaminants from water and wastewater However, few research studies have examined the synergistic or conflicting effects of operating conditions on newly developing contaminants found in wastewater. Therefore, the efficient, dependable, and expeditious examination of the pathogens in the intricate wastewater matrix remains a significant obstacle. As far as it can be ascertained, much attention has not recently been given to optimizing membrane processes to develop optimal operation design as related to pathogen removal from water and wastewater. Therefore, this state-of-the-art review aims to discuss the current trends in removing pathogens from wastewater by membrane techniques. In addition, conventional techniques of treating pathogenic-containing water and wastewater and their shortcomings were briefly discussed. Furthermore, derived mathematical models suitable for modelling, simulation, and control of membrane technologies for pathogens removal are highlighted. In conclusion, the challenges facing membrane technologies for removing pathogens were extensively discussed, and future outlooks/perspectives on optimizing and modelling membrane processes are recommended.

Machine learning facilitated the conceptual design of an alum dosing system for phosphorus removal in a wastewater treatment plant

Wastewater treatment plants (WWTPs) face challenges in controlling total phosphorus (TP), given more stringent regulations on TP discharging. In particular, WWTPs that operate at a small scale lack resources for real-time monitoring of effluent quality. This study aimed to develop a conceptual alum dosing system for reducing TP concentration, leveraging machine learning (ML) techniques and data from a full-scale WWTP containing incomplete TP information. The proposed system comprises two ML models in series: an Alert model based on LightGBM with an accuracy of 0.92, and a Dosage model employing a voting algorithm through combining three ML algorithms (LightGBM, SGD, and SVC) with an accuracy of 0.76. The proposed system has demonstrated the potential to ensure that 88.1% of the effluent remains below the TP discharge limit, which outperforms traditional dosing methods and could reduce overdosing from 61.3 to 12.1%. Furthermore, the SHapley Additive exPlanations (SHAP) analysis revealed that incorporating the output features from the previous cycle and utilizing the results of the Alert model as the input features for dosage prediction could be an effective method for data with limited information. The findings of this study have practical applications in improving the efficiency and effectiveness of TP control in small-scale WWTPs, providing a valuable solution for complying with stringent regulations and enhancing environmental sustainability.

Artificial neural networks for performance prediction of full-scale wastewater treatment plants: a systematic review

Wastewater treatment plants (WWTPs) are complex systems that must maintain high levels of performance to achieve adequate effluent quality to protect the environment and public health. Artificial intelligence and machine learning methods have gained attention in recent years for modeling complex problems, such as wastewater treatment. Although artificial neural networks (ANNs) have been identified as the most common of these methods, no study has investigated the development and configuration of these models. We conducted a systematic literature review on the use of ANNs to predict the effluent quality and removal efficiencies of full-scale WWTPs. Three databases were searched, and 44 records of the 667 identified were selected based on the eligibility criteria. The data extracted from the papers showed that the majority of studies used the feedforward neural network model with a backpropagation training algorithm to predict the effluent quality of plants, particularly in terms of organic matter indicators. The findings of this research may help in the search for an optimum design modeling process for future studies of similar prediction problems.