Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network

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.

Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

The intelligent prediction of membrane fouling during membrane filtration by mathematical models and artificial intelligence models

Recently, membrane separation technology has been widely utilized in filtration process intensification due to its efficient performance and unique advantages, but membrane fouling limits its development and application. Therefore, the research on membrane fouling prediction and control technology is crucial to effectively reduce membrane fouling and improve separation performance. This review first introduces the main factors (operating condition, material characteristics, and membrane structure properties) and the corresponding principles that affect membrane fouling. In addition, mathematical models (Hermia model and Tandem resistance model), artificial intelligence (AI) models (Artificial neural networks model and fuzzy control model), and AI optimization methods (genetic algorithm and particle swarm algorithm), which are widely used for the prediction of membrane fouling, are summarized and analyzed for comparison. The AI models are usually significantly better than the mathematical models in terms of prediction accuracy and applicability of membrane fouling and can monitor membrane fouling in real-time by working in concert with image processing technology, which is crucial for membrane fouling prediction and mechanism studies. Meanwhile, AI models for membrane fouling prediction in the separation process have shown good potential and are expected to be further applied in large-scale industrial applications for separation and filtration process intensification. This review will help researchers understand the challenges and future research directions in membrane fouling prediction, which is expected to provide an effective method to reduce or even solve the bottleneck problem of membrane fouling, and to promote the further application of AI modeling in environmental and food fields.

Living membrane bioreactor for highly effective and eco-friendly treatment of textile wastewater

The treatability of synthetic textile wastewater containing model dyes, such as reactive black and direct black dye (25.0 ± 2.6 mg_dye/L), with chemical oxygen demand (COD, 1000 ± 113 mg/L), ammonia‑nitrogen (NH₃-N, 140 ± 97 mg/L) and sulphate ions (SO₄^2-, 1357 ± 10.86 mg/L) was investigated in this study using an innovative living membrane bioreactor (LMBR) using an encapsulated self-forming dynamic membrane (ESFDM). The key advantage of ESFDMBR is the self-forming of the biological filtering layer protected between two meshes of inert robust and inexpensive material. A laboratory scale bioreactor (BR) equipped with a filtering unit mounting polyester meshes with a pore size of 30 μm, operated at an influent flux of 30 LMH was thus used. After the formation of the biological living membrane (LM), the treatment significantly reduced COD and DOC concentrations to the average values of 34 ± 10 mg/L and 32 ± 7 mg/L, corresponding to reduction efficiencies of 96.0 ± 1.1 % and 94 ± 1.05 %, respectively. Throughout the LMBR operation, the colours were successfully removed from synthetic textile wastewater with an overall removal efficiency of about 85.0 ± 1.8 and 86.0 ± 1.9 % for direct and reactive dyes, respectively. In addition, the proposed system was also found effective in affording removal efficiency of ammonia (NH₃) of 97 ± 0.5 %. Finally, this treatment afforded circa 40.7 ± 5.8 % sulphate removal, with a final concentration value of 805 ± 78.61 mg/L. The innovative living membrane, based on an encapsulated self-forming dynamic membrane allows a prolonged containment of the membrane fouling, confirmed by investigating the concentration of membrane fouling precursors and the time-course variations of turbidity and transmembrane pressure (TMP). Those final concentrations of wastewater pollutants were found to be below the limits for admission of the effluents in public sanitation networks in Italy and Tunisia, as representative countries for the regulation in force in Europe and North Africa. In conclusion, due to the low costs of plant and maintenance, the simple applicability, the rapid online implementation, the application of LMBR results in a promising method for the treatment of textile wastewater.

Predictive modelling and optimization of an airlift bioreactor for selenite removal from wastewater using artificial neural networks and particle swarm optimization

Selenite (Se⁴⁺) is the most toxic of all the oxyanion forms of selenium. In this study, a feed forward back propagation (BP) based artificial neural network (ANN) model was developed for a fungal pelleted airlift bioreactor (ALR) system treating selenite-laden wastewater. The performance of the bioreactor, i.e., selenite removal efficiency (RE_selenite) (%) was predicted through two input parameters, namely, the influent selenite concentration (IC_selenite) (10 mg/L - 60 mg/L) and hydraulic retention time (HRT) (24 h - 72 h). After training and testing with 96 sets of data points using the Levenberg-Marquardt algorithm, a multi-layer perceptron model (2-10-1) was established. High values of the correlation coefficient (0.96 ≤ R ≤ 0.98), along with low root mean square error (1.72 ≤ RMSE ≤ 2.81) and mean absolute percentage error (1.67 ≤ MAPE ≤ 2.67), clearly demonstrate the accuracy of the ANN model (> 96%) when compared to the experimental data. To ensure an efficient and economically feasible operation of the ALR, the process parameters were optimized using the particle swarm optimization (PSO) algorithm coupled with the neural model. The RE_selenite was maximized while minimizing the HRT for a preferably higher range of IC_selenite. Thus, the most favourable optimum conditions were suggested as: IC_selenite - 50.45 mg/L and HRT - 24 h, resulting in RE_selenite of 69.4%. Overall, it can be inferred that ANN models can successfully substitute knowledge-based models to predict the RE_selenite in an ALR, and the process parameters can be effectively optimized using PSO.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

Artificial intelligence technologies in bioprocess: Opportunities and challenges

Bioprocess control and optimization are crucial for tapping the metabolic potential of microorganisms, and which have made great progress in the past decades. Combination of the current control and optimization technologies with the latest computer-based strategies will be a worth expecting way to improve bioprocess further. Recently, artificial intelligence (AI) emerged as a data-driven technique independent of the complex interactions used in mathematical models and has been gradually applied in bioprocess. In this review, firstly, AI-guided modeling approaches of bioprocess are discussed, which are widely applied to optimize critical process parameters (CPPs). Then, AI-assisted rapid detection and monitoring technologies employed in bioprocess are summarized. Next, control strategies according to the above two technologies in bioprocess are analyzed. Lastly, current research gaps and future perspectives on AI-guided optimization and control technologies are discussed. This review provides theoretical guidance for developing AI-guided bioprocess optimization and control technologies.

Enhanced understanding of osmotic membrane bioreactors through machine learning modeling of water flux and salinity

Mathematical modeling can be helpful to understand and optimize osmotic membrane bioreactors (OMBR), a promising technology for sustainable wastewater treatment with simultaneous water recovery. Herein, seven machine learning (ML) algorithms were employed to model both water flux and salinity of a lab-scale OMBR. Through the optimum hyperparameters tuning and 5-fold cross-validation, the ML models have achieved more accurate results without obvious overfitting and bias. The median R² scores of water flux modeling were all over the 0.95 and the most of median R² scores from total dissolved solids (TDS) modeling were higher than 0.90. During model testing, random forest (RF) algorithm presented the highest R² score of 0.987 with the lowest root mean square error (RMSE = 0.044) for the water flux modeling, and extreme gradient boosting (XGB) algorithm exhibited the best results (R² = 0.97; RMSE = 0.234) in the TDS modeling. The Shapley Additive exPlanations (SHAP) analysis found that the phosphorus concentration was a critical input feature for both water flux and TDS modeling. Finally, the selected ML models were used to predict water flux and salinity affected by two input features and the predication results confirmed the importance of the phosphate concentration. The results of this study have demonstrated the promise of ML modeling for investigating OMBR systems.

A novel cascaded fractional fuzzy approach for control of fermentation process

In this work, a cascaded control strategy based on fractional-order fuzzy PD/PI (FOFPD/PI) is proposed for temperature control of the bioreactor. The FOFPI is used to control the ethanol concentration in the inner loop, while the FOFPD is used for temperature control of the bioreactor in the outer loop. The integer order fuzzy PD/PI (IOFPD/PI), 2DOF FOPID, 2DOF PID, and PID are also designed for comparison purposes. The design parameter of FOFPD/PI and IOFPD/PI are estimated using non-dominated sorting genetic algorithm II (NSGA-II). Results revealed that the proposed cascaded control scheme reduced the IAE by 33.5 %, 40.5%, 47%, and 64% compared to IOFPD/PI, 2DOF FOPID, 2DOF PID, and PID, respectively. Hence, it can be concluded that the proposed FOFPD/PI controller provides precise control of reactor temperature in different operating conditions compared to other controllers.

Artificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review

Membrane fouling is one of major obstacles in the application of membrane technologies. Accurately predicting or simulating membrane fouling behaviours is of great significance to elucidate the fouling mechanisms and develop effective measures to control fouling. Although mechanistic/mathematical models have been widely used for predicting membrane fouling, they still suffer from low accuracy and poor sensitivity. To overcome the limitations of conventional mathematical models, artificial intelligence (AI)-based techniques have been proposed as powerful approaches to predict membrane filtration performance and fouling behaviour. This work aims to present a state-of-the-art review on the advances in AI algorithms (e.g., artificial neural networks, fuzzy logic, genetic programming, support vector machines and search algorithms) for prediction of membrane fouling. The working principles of different AI techniques and their applications for prediction of membrane fouling in different membrane-based processes are discussed in detail. Furthermore, comparisons of the inputs, outputs, and accuracy of different AI approaches for membrane fouling prediction have been conducted based on the literature database. Future research efforts are further highlighted for AI-based techniques aiming for a more accurate prediction of membrane fouling and the optimization of the operation in membrane-based processes.

Machine learning modeling and analysis of biohydrogen production from wastewater by dark fermentation process

Dark fermentation process for simultaneous wastewater treatment and H₂ production is gaining attention. This study aimed to use machine learning (ML) procedures to model and analyze H₂ production from wastewater during dark fermentation. Different ML procedures were assessed based on the mean squared error (MSE) and determination coefficient (R²) to select the most robust models for modeling the process. The research showed that gradient boosting machine (GBM), support vector machine (SVM), random forest (RF) and AdaBoost were the most appropriate models, which were optimized by grid search and deeply analyzed by permutation variable importance (PVI) to identify the relative importance of process variables. All four models demonstrated promising performances in predicting H₂ production with high R² values (0.893, 0.885, 0.902 and 0.889) and small MSE values (0.015, 0.015, 0.016 and 0.015). Moreover, RF-PVI demonstrated that acetate, butyrate, acetate/butyrate, ethanol, Fe and Ni were of high importance in decreasing order.

Surface Pretreatments of AA5083 Aluminum Alloy with Enhanced Corrosion Protection for Cerium-Based Conversion Coatings Application: Combined Experimental and Computational Analysis

The effects of surface pretreatments on the cerium-based conversion coating applied on an AA5083 aluminum alloy were investigated using a combination of scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), polarization testing, and electrochemical impedance spectroscopy. Two steps of pretreatments containing acidic or alkaline solutions were applied to the surface to study the effects of surface pretreatments. Among the pretreated samples, the sample prepared by the pretreatment of the alkaline solution then acid washing presented higher corrosion protection (~3 orders of magnitude higher than the sample without pretreatment). This pretreatment provided a more active surface for the deposition of the cerium layer and provided a more suitable substrate for film formation, and made a more uniform film. The surface morphology of samples confirmed that the best surface coverage was presented by alkaline solution then acid washing pretreatment. The presence of cerium in the (EDS) analysis demonstrated that pretreatment with the alkaline solution then acid washing resulted in a higher deposition of the cerium layer on the aluminum surface. After selecting the best surface pretreatment, various deposition times of cerium baths were investigated. The best deposition time was achieved at 10 min, and after this critical time, a cracked film formed on the surface that could not be protective. The corrosion resistance of cerium-based conversion coatings obtained by electrochemical tests were used for training three computational techniques (artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine regression (SVMR)) based on Pretreatment-1 (acidic or alkaline cleaning: pH (1)), Pretreatment-2 (acidic or alkaline cleaning: pH (2)), and deposition time in the cerium bath as an input. Various statistical criteria showed that the ANFIS model (R² = 0.99, MSE = 48.83, and MAE = 3.49) could forecast the corrosion behavior of a cerium-based conversion coating more accurately than other models. Finally, due to the robust performance of ANFIS in modeling, the effect of each parameter was studied.

Development of Machine Learning Models to Evaluate the Toughness of OPH Alloys

Oxide Precipitation-Hardened (OPH) alloys are a new generation of Oxide Dispersion-Strengthened (ODS) alloys recently developed by the authors. The mechanical properties of this group of alloys are significantly influenced by the chemical composition and appropriate heat treatment (HT). The main steps in producing OPH alloys consist of mechanical alloying (MA) and consolidation, followed by hot rolling. Toughness was obtained from standard tensile test results for different variants of OPH alloy to understand their mechanical properties. Three machine learning techniques were developed using experimental data to simulate different outcomes. The effectivity of the impact of each parameter on the toughness of OPH alloys is discussed. By using the experimental results performed by the authors, the composition of OPH alloys (Al, Mo, Fe, Cr, Ta, Y, and O), HT conditions, and mechanical alloying (MA) were used to train the models as inputs and toughness was set as the output. The results demonstrated that all three models are suitable for predicting the toughness of OPH alloys, and the models fulfilled all the desired requirements. However, several criteria validated the fact that the adaptive neuro-fuzzy inference systems (ANFIS) model results in better conditions and has a better ability to simulate. The mean square error (MSE) for artificial neural networks (ANN), ANFIS, and support vector regression (SVR) models was 459.22, 0.0418, and 651.68 respectively. After performing the sensitivity analysis (SA) an optimized ANFIS model was achieved with a MSE value of 0.003 and demonstrated that HT temperature is the most significant of these parameters, and this acts as a critical rule in training the data sets.

Removal of pharmaceuticals from nitrified urine

In this study, granular activated carbon (GAC) was examined for the removal of five of the most commonly detected pharmaceuticals (naproxen, carbamazepine, acetaminophen, ibuprofen and metronidazole) from a nitrified urine to make the urine-derived fertiliser nutrient safe for food crops. Batch experiments were conducted to investigate the adsorption kinetics that described the removal of micropollutants (equal concentrations of 0.2 mM) from the synthetic nitrified urine at different GAC dosages (10-3000 mg/L). Artificial neural network modelling was also used to predict and simulate the removal of pharmaceuticals from nitrified urine. Langmuir and Freundlich isotherm models described the equilibrium data, with the Langmuir model providing slightly higher correlations. At the highest dose of 3000 mg/L GAC, all the pharmaceuticals showed a removal rates of over 90% after 1 h of adsorption time and 99% removal rates after 6 h of adsorption time. This study concludes that GAC is able to remove the targeted xenobiotics without affecting the concentration of N and P in the urine, suggesting that nitrified urine could be safely used as a nutrient product in future.

Integrated data-driven strategy to optimize the processes configuration for full-scale wastewater treatment plant predesign

Wastewater treatment plants (WWTPs) play an irreplaceable role in eliminating pollutants from domestic and industrial wastewater and contribute to water recycling. Nowadays, the selection of processes configuration of WWTPs mainly depends on the local wastewater treatment standards and the experience of wastewater engineers rather than an intelligent data-driven strategy. In this study, an integrated data-driven strategy consisting of t-distributed stochastic neighbor embedding (t-SNE) and deep neural networks (DNNs) is proposed for optimizing the processes configuration of full-scale WWTP predesign. A large dataset with 14,647 samples collected from 10 full-scale WWTPs with distinct treatment processes is clustered by the t-SNE method based on the influent characteristics, and four meaningful clusters (Clusters I-IV) are identified for the subsequent development of DNN classification models. All four DNN models achieve acceptable classification accuracy (>0.8975) and the maximal testing accuracy is 0.9505. The DNN models are capable of finding the optimized processes configuration of WWTPs under target scenarios. Our results highlight the strength of combining the t-SNE and the DNN models to utilize the relationships between key parameters and processes configuration of WWTPs, and help engineers predesign WWTPs with the optimal processes configuration.

Artificial intelligence modeling to predict transmembrane pressure in anaerobic membrane bioreactor-sequencing batch reactor during biohydrogen production

The complex nature of wastewater treatment has led to search for alternative strategies such as different artificial intelligence (AI) techniques to model the various operational parameters. The present work is aimed at predicting the transmembrane pressure (TMP) as a key operational parameter in the case of anaerobic membrane bioreactor-sequencing batch reactor (AnMBR-SBR) during biohydrogen production using the adaptive neuro-fuzzy inference systems (ANFIS) and artificial neural network (ANN). In both the models, organic loading rates (OLR) ranging from 0.5 to 8.0 g COD/L/d, effluent pH (3.6-6.9), mixed liquor suspended solid (4.6-21.5 g/L) and mixed liquor volatile suspended solid (3.7-15.5 g/L) were used as the input parameters to test TMP as an output parameter. The ANFIS model was trained using the hybrid algorithms for TMP prediction. The higher prediction performance was obtained by using the Gauss membership function with four membership numbers. A back-propagation algorithm was also employed for the feed forward training of ANN model; the best structure was a Levenberg-Marquardt training algorithm with nine neurons in the hidden layer. By employing ANFIS and ANN models, relatively a good prediction of TMP was obtained with the R² values of 0.93 and 0.88, respectively while the calculated mean square error for TMP in the ANFIS model (7.3 × 10^-3) was lower than that of ANN model (8.02 × 10^-3). The higher R² and lower MSE values for the ANFIS model exhibited a better TMP prediction performance than the ANN model. Finally, it was observed that in the sensitivity analysis of ANN model, OLR was the most important input parameter on the variation of TMP.

Estimation of in-situ biogas upgrading in microbial electrolysis cells via direct electron transfer: Two-stage machine learning modeling based on a NARX-BP hybrid neural network

With the increasing of data in wastewater treatment, data-driven machine learning models are useful for modeling biological processes and complex reactions. However, few data-driven models have been developed for simulating the microbial electrolysis cells (MECs) and traditional models are too ambiguous to comprehend the mechanisms. In this study, a new general data-driven two-stage model was firstly developed to predict CH₄ production from in-situ biogas upgrading in the biocathode MECs via direct electron transfer (DET), named NARX-BP hybrid neural networks. Compared with traditional one-stage model, the model could well predict methane production via DET with excellent performance (all R² and MES of 0.918 and 6.52 × 10^-2, respectively) and reveal the mechanisms of biogas upgrading, for the new systematical modeling approach could improve the versatility and applicability by inputting significant intermediate variables. In addition, the model is generally available to support long-term prediction and optimal operation for anaerobic digestion or complex MEC systems.

Progress in osmotic membrane bioreactors research: Contaminant removal, microbial community and bioenergy production in wastewater

Renewable energy, water conservation, and environmental protection are the most important challenges today. Osmotic membrane bioreactor (OMBR) is an innovative process showing superior performance in bioenergy production, eliminating contaminants, and low fouling tendency. However, salinity build-up is the main drawback of this process. Identifying the microbial community can improve the process in bioenergy production and contaminant treatment. This review aims to study the recent progress and challenges of OMBRs in contaminant removal, microbial communities and bioenergy production. OMBRs are widely reported to remove over 80% of total organic carbon, PO₄^3-, NH₄⁺ and emerging contaminants from wastewater. The most important microbial phyla for both hydrogen and methane production in OMBR are Firmicutes, Proteobacteria and Bacteroidetes. Firmicutes' dominance in anaerobic processes is considerably increased from usually 20% at the beginning to 80% under stable condition. Overall, OMBR process has great potential to be applied for simultaneous bioenergy production and wastewater treatment.

Recent Advances in the Prediction of Fouling in Membrane Bioreactors

Compared to the traditional activated sludge process, the membrane bioreactor (MBR) has several advantages such as the production of high-quality effluent, generation of low excess sludge, smaller footprint requirements, and ease of automatic control of processes. The MBR has a broader prospect of its applications in wastewater treatment and reuse. However, membrane fouling is the biggest obstacle for its wider application. This paper reviews the techniques available to predict fouling in MBR, discusses the problems associated with predicting fouling status using artificial neural networks and mathematical models, summarizes the current state of fouling prediction techniques, and looks into the trends in their development.

Separation, anti-fouling, and chlorine resistance of the polyamide reverse osmosis membrane: From mechanisms to mitigation strategies

Membrane technology has been widely used in the wastewater treatment and seawater desalination. In recent years, the reverse osmosis (RO) membrane represented by polyamide (PA) has made great progress because of its excellent properties. However, the conventional PA RO membranes still have some scientific problems, such as membrane fouling, easy degradation after chlorination, and unclear mechanisms of salt retention and water flux, which seriously impede the widespread use of RO membrane technology. This paper reviews the progress in the research and development of the RO membrane, with key focus on the mechanisms and strategies of the contemporary separation, anti-fouling and chlorine resistance of the PA RO membrane. This review seeks to provide state-of-the-art insights into the mitigation strategies and basic mechanisms for some of the key challenges. Under the guidance of the fundamental understanding of each mechanism, operation and modification strategies are discussed, and reasonable analysis is carried out, which can address some key technical challenges. The last section of the review focuses on the technical issues, challenges, and future perspective of these mechanisms and strategies. Advances in synergistic mechanisms and strategies of the PA RO membranes have been rarely reviewed; thus, this review can serve as a guide for new entrants to the field of membrane water treatment and established researchers.

Modeling and Sensitivity Analysis of the Forward Osmosis Process to Predict Membrane Flux Using a Novel Combination of Neural Network and Response Surface Methodology Techniques

The forward osmosis (FO) process is an emerging technology that has been considered as an alternative to desalination due to its low energy consumption and less severe reversible fouling. Artificial neural networks (ANNs) and response surface methodology (RSM) have become popular for the modeling and optimization of membrane processes. RSM requires the data on a specific experimental design whereas ANN does not. In this work, a combined ANN-RSM approach is presented to predict and optimize the membrane flux for the FO process. The ANN model, developed based on an experimental study, is used to predict the membrane flux for the experimental design in order to create the RSM model for optimization. A Box–Behnken design (BBD) is used to develop a response surface design where the ANN model evaluates the responses. The input variables were osmotic pressure difference, feed solution (FS) velocity, draw solution (DS) velocity, FS temperature, and DS temperature. The R2 obtained for the developed ANN and RSM model are 0.98036 and 0.9408, respectively. The weights of the ANN model and the response surface plots were used to optimize and study the influence of the operating conditions on the membrane flux.

A comprehensive review of nutrient-energy-water-solute recovery by hybrid osmotic membrane bioreactors

Hybrid osmotic membrane bioreactor (OMBR) takes advantage of the cooperation of varying biological or desalination processes and can achieve NEWS (nutrient-energy-water-solute) recovery from wastewater. However, a lack of universal parameters hinders our understanding. Herein, system configurations and new parameters are systematically investigated to help better evaluate recovery performance. High-quality water can be produced in reverse osmosis/membrane distillation-based OMBRs, but high operation cost limits their application. Although bioelectrochemical system (BES)/electrodialysis-based OMBRs can effectively achieve solute recovery, operation parameters should be optimized. Nutrients can be recovered from various wastewater by porous membrane-based OMBRs, but additional processes increase operation cost. Electricity recovery can be achieved in BES-based OMBRs, but energy balances are negative. Although anaerobic OMBRs are energy-efficient, salinity accumulation limits methane productions. Additional efforts must be made to alleviate membrane fouling, control salinity accumulation, optimize recovery efficiency, and reduce operation cost. This review will accelerate hybrid OMBR development for real-world applications.

Effective modelling of hydrogen and energy recovery in microbial electrolysis cell by artificial neural network and adaptive network-based fuzzy inference system

This study aims to analyze and model cathodic H₂ recovery (r_cat), coulombic efficiency (CE) with inputs of voltage, electrical conductivity (EC) and anode potential, and H₂ production rate and total energy recovery with inputs of r_cat and CE in a microbial electrolysis cell using artificial neural network (ANN) and adaptive network-based fuzzy inference system (ANFIS) procedures. Both ANN and ANFIS models demonstrated great goodness of fit for r_cat, CE, H₂ production rate and total energy recovery prediction with high R² values. The sum square error values for r_cat (0.0017), CE (0.0163), H₂ production rate (0.1062) and total energy recovery (0.0136) in ANN models were slightly higher than those in ANFIS models at 0.0005, 0.0091, 0.1247 and 0.0148 respectively. Sensitivity analysis by ANN models demonstrated that voltage, EC, r_cat and r_cat were the most effective factors for r_cat, CE, H₂ production rate and total energy recovery, respectively.

Progress on the Fabrication and Application of Electrospun Nanofiber Composites

Nanofibers are one of the most attractive materials in various applications due to their unique properties and promising characteristics for the next generation of materials in the fields of energy, environment, and health. Among the many fabrication methods, electrospinning is one of the most efficient technologies which has brought about remarkable progress in the fabrication of nanofibers with high surface area, high aspect ratio, and porosity features. However, neat nanofibers generally have low mechanical strength, thermal instability, and limited functionalities. Therefore, composite and modified structures of electrospun nanofibers have been developed to improve the advantages of nanofibers and overcome their drawbacks. The combination of electrospinning technology and high-quality nanomaterials via materials science advances as well as new modification techniques have led to the fabrication of composite and modified nanofibers with desired properties for different applications. In this review, we present the recent progress on the fabrication and applications of electrospun nanofiber composites to sketch a progress line for advancements in various categories. Firstly, the different methods for fabrication of composite and modified nanofibers have been investigated. Then, the current innovations of composite nanofibers in environmental, healthcare, and energy fields have been described, and the improvements in each field are explained in detail. The continued growth of composite and modified nanofiber technology reveals its versatile properties that offer alternatives for many of current industrial and domestic issues and applications.