SARS-CoV-2 removal by mix matrix membrane: A novel application of artificial neural network based simulation in MATLAB for evaluating wastewater reuse risks

Integrating artificial intelligence modeling and membrane technologies for advanced wastewater treatment: Research progress and future perspectives

Membrane technologies have become proficient alternatives for advanced wastewater treatment, ensuring high contaminant removal and sustainable resource recovery. Despite significant progress, ongoing research efforts aim to further optimize treatment performance. Among the challenges faced, membrane fouling persists as a relevant obstacle in membrane technologies, necessitating the development of more effective mitigation strategies. Mathematical models, widely employed for predicting treatment performance, generally exhibit low accuracy and suffer from uncertainties due to the complex and variable nature of wastewater. To overcome these limitations, numerous studies have proposed artificial intelligence (AI) modeling to accurately predict membrane technologies' performance and fouling mechanisms. This approach aims to provide advanced simulations and predictions, thereby enhancing process control, optimization, and intensification. This literature review explores recent advancements in modeling membrane-based wastewater treatment processes through AI models. The analysis highlights the enormous potential of this research field in enhancing the efficiency of membrane technologies. The role of AI modeling in defining optimal operating conditions, developing effective strategies for membrane fouling mitigation, enhancing the performance of novel membrane-based technologies, and improving membrane fabrication techniques is discussed. These enhanced process optimization and control strategies driven by AI modeling ensure improved effluent quality, optimized resource consumption, and minimized operating costs. The potential contribution of this cutting-edge approach to a paradigm shift toward sustainable wastewater treatment is examined. Finally, this review outlines future perspectives, emphasizing the research challenges that require attention to overcome the current limitations hindering the integration of AI modeling in wastewater treatment plants.

Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks

Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.

Generation of bio-energy after optimization and controlling fluctuations using various sludge activated microbial fuel cell

An aerobic microbial fuel cell (MFC) was designed to produce bio-electricity using cow manure-pretreated slurry (CM) and sewage sludge (SS). A comparative study of parametric effects on power generation for various parameters like feed ratio of wastes, pH of anode media, and electrode depth was conducted. This experiment aimed to identify the most important system parameters and optimize them to develop a suitable controller for a stable output. Power production reached its maximum at an electrode depth of 7 cm, a pH of 6, and a feed ratio of 2:1 in the CM + SS system before applying the controller. Response surface methodology (RSM) was practiced to explore the relationships between various parameters and the response using MINITAB software. The regression equation of the most productive system deduced from the RSM result has an R2 value of 85.3%. The results show that an ON/OFF controller works satisfactorily in this study. The highest energy-generating setup has a chemical oxygen demand (COD) removal efficiency of 45%. The morphology and content of the used wastes indicate that they can be recycled in other applications.

Sustainable wastewater treatment by RO and hybrid organic polyamide membrane nanofiltration system for clean environment

One of the environmental pollution is happened by the discharge of industrial wastewater that needs to be adequately filtered. Given that the effluent from the leather industry contains high levels of chromium, heavy metals, lipids, and Sulphur, it is one of the wastewater disposals that are most damaging. This experimental study focuses on reverse osmosis and hybrid organic polyimide membrane for nanofiltration for sustainable wastewater treatment. In the RO and organic polyamide Nano-porous membranes, a thin film of polyamide membrane was used for efficient filtration. Taguchi analysis optimized process parameters such as pressure, temperature, pH, and volume reduction factor. The outcome shows an 89% reduction in total wastewater hardness, an 88% reduction in sulfate, and an 89% efficiency reduction in COD. As a result, the proposed technology significantly increased filtration efficiency.

Curve fitting model of Polycarbonate Al2O3-nanoparticle membranes for removing emerging contaminants from wastewater: Effect of temperature and nanoparticles

The purpose of this paper is to demonstrate the use of the phase separation procedure in order to synthesize ultrafiltration polycarbonate containing aluminum oxide (Al₂O₃) nanoparticles (NPs) to remove emerging contaminants from wastewater at varying temperatures and nanoparticle contents. In the membrane structure, Al₂O₃-NPs are loaded at rates of 0≤φ≤1% volume. Fourier transform infrared (FTIR), atomic force microscopy (AFM), and scanning electron microscopy (SEM) were used to characterize the fabricated membrane containing Al₂O₃-NPs. Nevertheless, volume fractions ranged from 0 to 1% during the experiment, which was conducted between 15 and 55 °C. An analysis of the ultrafiltration results was conducted by using a curve-fitting model to determine the interaction between these parameters and the effect of all independent factors on the emerging containment removal. Shear stress and shear rate for this nanofluid are nonlinear at different temperatures and volume fractions. Viscosity decreases with increasing temperature at a specific volume fraction. In order to remove emerging contaminants, a decrease in viscosity at a relative level fluctuates, resulting in more porosity in the membrane. NPs become more viscous with an increasing volume fraction at any given temperature on the membrane. For example, a maximum relative viscosity increases of 34.97% is observed for a 1% volume fraction at 55 °C. A novel model is then used to measure the viscosity of nanofluid. This indicates that the results and experimental data are in very close agreement, as the maximum deviation is 2.6%.

Occurrences of similar viral diversity in campus wastewater and reclaimed water of a university dormitory

Water reuse from wastewater sources still remain some critical safety concerns associated with treacherous contaminants like pathogenic viruses. In this study, viral diversities in campus wastewater (CWW) and its reclaimed water (RCW) recycled for toilet flushing and garden irrigation of a university dormitory were assessed using metagenomic sequencing for acquisition of more background data. Results suggested majority (>80%) of gene sequences within assembled contigs predicted by open reading frame (ORF) finder were no-hit yet believed to be novel/unrevealed viral genomic information whereas hits matched bacteriophages (i.e., mainly Myoviridae, Podoviridae, and Siphoviridae families) were predominant in both CWW and RCW samples. Moreover, few pathogenic viruses (<1%) related to infections of human skin (e.g., Molluscum contagiosum virus, MCV), digestion system (e.g., hepatitis C virus, HCV), and gastrointestinal tract (e.g., human norovirus, HuNoV) were also noticed raising safety concerns about application of reclaimed waters. Low-affinity interactions of particular viral exterior proteins (e.g., envelope glycoproteins or spike proteins) for disinfectant ligand (e.g., chlorite) elucidated treatment limitations of current sewage processing systems even with membrane bioreactor and disinfectant contactor. Revolutionary disinfection approaches together with routine monitoring and new regulations are prerequisite to secure pathogen-correlated water quality for safer reuse of reclaimed waters.

Determining reclaimed water quality thresholds and farming practices to improve food crop yield: A meta-analysis combined with random forest model

Irrigated agricultural systems with reclaimed water (RW) play a crucial role in alleviating global water scarcity and increased food demand. However, appropriate reclaimed water quality thresholds and farming practices to improve food crop yield is virtually unclear. Therefore, for the first time, this study made a large compilation of previous studies using meta-analysis combined with a random forest (RF) model and analyzed the impact of RW versus freshwater (FW) on the yield of food crops (cereals, vegetables, and fruits). It was found that magnesium ion (Mg²⁺), calcium ion (Ca²⁺), electrical conductivity (EC), total nitrogen (TN), and potential of hydrogen (pH) were the most important factors for RW quality indicators. Based on the results, water managers should establish more conservative RW quality thresholds to promote food crop production, especially for salts and pollutants in RW. Compared to international water quality standards, it could be slightly relaxed the restrictions of TN in RW. The optimal farming practices obtained that irrigation amount of the mixed RW and FW (RW + FW) was from 1000 m³ ha^-1 to 5000 m³ ha^-1, and the cultivation period was no more than three years. Flood irrigation (FI) and drip irrigation (DI) for cereals were also recommended. Finally, a comparison of the determined results from this method with other scenarios published, finding a good agreement.

Machine learning modeling of polycarbonate ultrafiltration membranes at different temperatures, Al2O3 nanoparticle volumes, and water ratios

The efficacy of novel polycarbonate ultrafiltration, aluminum oxide nanoparticle (Al₂O₃-NPs) volume fraction, temperature, and water/ethylene glycol (EG) ratio were evaluated to determine the thermophysical properties of the membrane. 5%-10% of Al₂O₃-NPs have been added to the PC. A machine learning approach was used to compare the volume fraction of Al₂O₃-NPs, the temperature, and the water-to-ethylene glycol (EG) ratio. To determine the impact of Al2O3-NPs loading on the Response Surface Method (RSM), DOE, ANOVA, ANN, MLP, and NSGA-II, the number of aluminum oxide nanoparticles (Al₂O₃-NPs), temperature, and water/ethylene glycol (EG) on membranes in PC ultrafiltration are evaluated. Based on the Relative Thermal Conductivity Model (RSM), the regression coefficient of Al₂O₃ in water and EG was 0.9244 and 0.9170 with adjusted regression coefficients. A higher concentration of EG enhances the thermal conductivity of the membrane when the effective parameters are considered. The effect of temperature on the relative viscosity of the membrane led to the conclusion that Al₂O₃ water/EG can cool at high temperatures while providing no viscosity change. When Al₂O₃ is dissolved in water and EG, more EG is necessary to optimize the mode of reactivity. Using the MLP model, the calculated R-value is 0.9468, the MSE is 0.001752989 (mean square error), and the MAE is 0.01768558 (mean absolute error). RSM predicted the average thermal conductivity behavior of nanofluid better. The ANN model, however, has proven to be more effective than the RSM in simulating the relative viscosity of nanofluids. The NSGA-II optimized results showed that the minimum relative viscosity and maximum coefficient of thermal conductivity occurred at the lowest water ratio and maximum temperature.

Various advanced wastewater treatment methods to remove microplastics and prevent transmission of SARS-CoV-2 to airborne microplastics

Microplastics (MPs) and SARS-CoV-2 interact due to their widespread presence in our environment and affect the virus' behaviour indoors and outdoors. Therefore, it is necessary to study the interaction between MPs and SARS-CoV-2. The environmental damage caused by MPs is increasing globally. Emerging pollutants may adversely affect organisms, especially sewage, posing a threat to human health, animal health, and the ecological system. A significant concern with MPs in the air is that they are a vital component of MPs in the other environmental compartments, such as water and soil, which may affect human health through ingesting or inhaling. This work introduces the fundamental knowledge of various methods in advanced water treatment, including membrane bioreactors, advanced oxidation processes, adsorption, etc., are highly effective in removing MPs; they can still serve as an entrance route due to their constantly being discharged into aquatic environments. Following that, an analysis of each process for MPs' removal and mitigation or prevention of SARS-CoV-2 contamination is discussed. Next, an airborne microplastic has been reported in urban areas, raising health concerns since aerosols are considered a possible route of SARS-CoV-2 disease transmission and bind to airborne MP surfaces. The MPs can be removed from wastewater through conventional treatment processes with physical processes such as screening, grit chambers, and pre-sedimentation.

Mega-scale desalination Efficacy (Reverse Osmosis, Electrodialysis, Membrane Distillation, MED, MSF) during COVID-19: Evidence from salinity, pretreatment methods, temperature of operation

The unprecedented situation of the COVID-19 pandemic heavily polluted water bodies whereas the presence of SARS-CoV-2, even in treated wastewater in every corner of the world is reported. The main aim of the present study is to show the effectiveness and feasibility of some well-known desalination technologies which are reverse osmosis (RO), Electrodialysis (ED), Membrane Distillation (MD), multi effect distillation (MED), and multi stage flashing (MSF) during the COVID-19 pandemic. Systems’ effectiveness against the novel coronavirus based on three parameters of nasopharynx/nasal saline-irrigation, temperature of operation and pretreatment methods are evaluated. First, based on previous clinical studies, it showed that using saline solution (hypertonic saline >0.9% concentration) for gargling/irrigating of nasal/nasopharynx/throat results in reducing and replication of the viral in patients, subsequently the feed water of desalination plants which has concentration higher than 3.5% (35000ppm) is preventive against the SARS-CoV-2 virus. Second, the temperature operation of thermally-driven desalination; MSF and MED (70-120°C) and MD (55-85°C) is high enough to inhibit the contamination of plant structure and viral survival in feed water. The third factor is utilizing various pretreatment process such as chlorination, filtration, thermal/precipitation softening, ultrafiltration (mostly for RO, but also for MD, MED and MSF), which are powerful treatment methods against biologically-contaminated feed water particularly the SARS-CoV-2. Eventually, it can be concluded that large-scale desalination plants during COVID-19 and similar situation are completely reliable for providing safe drinking water.