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

WITHDRAWN: Effects of hydraulic retention time and cultivation on nutrients removal and biomass production in wastewater by membrane photobioreactor: Modeling and optimization by machine learning and response surface methodology

This article has been withdrawn at the request of the Editor-in-Chief.

Performance, community structure, metabolic pathway, and mechanism in a three-dimensional electrocatalytic biofilter (3DEBF) for the degradation of multiple concentrations of clofibric acid (CA)

A three-dimensional electrocatalytic biofilter (3DEBF) was constructed to remove clofibric acid (CA). This study compared the effectiveness of 3DEBF and biological aerated filter (BAF) in the removal of refractory CA, examined the effects of influent CA concentrations (0.1, 0.3, 0.5, 0.7, and 1.0 mg/L) on microbial community, and proposed a possible 3DEBF degradation mechanism. Results indicated that the average removal efficiency of 3DEBF reached a peak (76.09%) at 0.7 mg/L, which was 14.43% higher than that of BAF. Based on the microbial community analysis, the significant enrichment of Rhodobacter, Mycobacterium, and Sphingopyxis in 3DEBF was associated with the effect of the CA concentration and the electric field. The degradation pathway indicated that xenobiotics biodegradation and metabolism, membrane transport and replication and repair related genes were upregulated in 3DEBAF. Moreover, CA degradation is based on a combination of adsorption, electrochemical oxidation, and biodegradation.

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications

Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.

Microalgae-Enabled Wastewater Remediation and Nutrient Recovery through Membrane Photobioreactors: Recent Achievements and Future Perspective

The use of microalgae for wastewater remediation and nutrient recovery answers the call for a circular bioeconomy, which involves waste resource utilization and ecosystem protection. The integration of microalgae cultivation and wastewater treatment has been proposed as a promising strategy to tackle the issues of water and energy source depletions. Specifically, microalgae-enabled wastewater treatment offers an opportunity to simultaneously implement wastewater remediation and valuable biomass production. As a versatile technology, membrane-based processes have been increasingly explored for the integration of microalgae-based wastewater remediation. This review provides a literature survey and discussion of recent progressions and achievements made in the development of membrane photobioreactors (MPBRs) for wastewater treatment and nutrient recovery. The opportunities of using microalgae-based wastewater treatment as an interesting option to manage effluents that contain high levels of nutrients are explored. The innovations made in the design of membrane photobioreactors and their performances are evaluated. The achievements pave a way for the effective and practical implementation of membrane technology in large-scale microalgae-enabled wastewater remediation and nutrient recovery processes.

An overview of deploying membrane bioreactors in saline wastewater treatment from perspectives of microbial and treatment performance

The discharged saline wastewater has severely influenced the aquatic environment as the treatment performance of many wastewater treatment techniques is limited. In addition, the sources of saline wastewater are also plentiful from agricultural and various industrial fields such as food processing, tannery, pharmaceutical, etc. Although high salinity levels negatively impact the performance of both physicochemical and biological processes, membrane bioreactor (MBR) processes are considered as a potential technology to treat saline wastewater under different salinity levels depending on the adaption of the microbial community. Therefore, this study aims to systematically review the application of MBR widely used in the saline wastewater treatment from the perspectives of microbial structure and treatment efficiencies. At last, the concept of carbon dioxide capture and storage will be proposed for the MBR-treating saline wastewater technologies and considered toward the circular economy with the target of zero emission.

Effects of the Structure of Benzenesulfonate-Based Draw Solutes on the Forward Osmosis Process

A series of phosphonium-based ionic liquids (ILs) based on benzenesulfonate derivatives (tetrabutylphosphonium benzenesulfonate ([TBP][BS]), tetrabutylphosphonium 4-methylbenzenesulfonate ([TBP][MBS]), tetrabutylphosphonium 2,4-dimethylbenzenesulfonate ([TBP][DMBS]), and tetrabutylphosphonium 2,4,6-trimethylbenzenesulfonate ([TBP][TMBS])) were synthesized via anion exchange with tetrabutylphosphonium bromide ([TBP][Br]). Then, we characterized the ILs and investigated their suitability as draw solutes for forward osmosis (FO), focusing on their thermoresponsive properties, conductivities, and osmotic pressures. We found that aqueous [TBP][BS] was not thermoresponsive, but 20 wt% aqueous [TBP][MBS], [TBP][DMBS], and [TBP][TMBS] had lower critical solution temperatures (LCSTs) of approximately 41, 25, and 21 °C, respectively, enabling their easy recovery using waste heat. Based on these findings, 20 wt% aqueous [TBP][DMBS] was tested for its FO performance, and the water and reverse solute fluxes were found to be approximately 9.29 LMH and 1.37 gMH, respectively, in the active layer facing the draw solution (AL-DS) mode and 4.64 LMH and 0.37 gMH, respectively, in the active layer facing the feed solution (AL-FS) mode. Thus, these tetrabutylphosphonium benzenesulfonate-based LCST-type ILs are suitable for drawing solutes for FO process.

Computational fluid dynamics simulation study of hypersaline water desalination via membrane distillation: Effect of membrane characteristics and operational parameters

In this study, a comprehensive model was developed using Computational Fluid Dynamics (CFD), and the behaviour of a direct contact membrane distillation (DCMD) system was investigated at hypersaline feedwater conditions. The effects of various operating parameters including feed and permeate velocities, temperatures and salinities, as well as different membrane characteristics like thickness, porosity, and thermal conductivity were studied. The developed simulation model was also validated using experimental data. The results showed that the membrane conductivity and thickness had a significant impact on the DCMD performance, and the optimum operational condition was necessary to be determined. The results showed that increasing the feedwater salinity from 50 to 200 g/l decreased the membrane flux by up to 33%, while a four times decrease in thermal conductivity of the membrane could lead to an increase in the membrane flux from 11.2 to 32.4 l/m²·h (LMH). In addition, the optimal membrane thickness was found to increase with salinity, reaching >120 μm for treatment of 22 wt% NaCl feedwater solution. However, the flux declined from >32 LMH to <13 LMH upon the increase in feedwater salinity (up to 22 wt% NaCl solution). It is also shown that a thinner membrane performed better for desalination of low salinity feedwater, while the thicker one produces higher separation performance and thermal efficiency for hypersaline brine desalination.

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.

Performance of pilot-scale constructed wetland osmotic microbial fuel cell under different gravel conditions

This paper explores the performance of pilot-scale constructed wetland osmotic microbial fuel cell (CW-OMFC) in different gravel conditions. The performance was measured in terms of power generation, water flux, chemical oxygen demand (COD) removal, and coulombic efficiency. The CW-OMFC was divided into four sections based on the porosity of the materials. The surface area of materials at Side A, Side B, Side C, and Side D were 2.717 m².g^-1, 0.228 m².g^-1, 0.095 m².g^-1, and 0.072 m².g^-1, respectively. The CW-OMFC achieved maximum water flux, minimum reverse salt flux, high power density, and COD removal efficiency of 6.66 ± 0.5 L.m^-2.h^-1, 3.33 ± 1.2 g.m^-2.h^-1, 59.53 ± 10 mW.m^-2 and 84.69%, respectively, by using high porous materials. The nutrients (nitrogen, phosphorus, and potassium) uptake by plants from wastewater were 12.17%, 12.01%, and 21.73%, respectively.

A Superimposed QD-Based Optical Antenna for VLC: White LED Source

Visible light communication (VLC) is a versatile enabling technology for following high-speed wireless communication because of its broad unlicensed spectrum. In this perspective, white light-emitting diodes (LED) provide both illumination and data transmission simultaneously. To accomplish a VLC system, receiver antennas play a crucial role in receiving light signals and guiding them toward a photodetector to be converted into electrical signals. This paper demonstrates an optical receiver antenna based on luminescent solar concentrator (LSC) technology to exceed the conservation of etendue and reach a high signal-to-noise ratio. This optical antenna is compatible with all colors of LEDs and achieves an optical efficiency of 3.75%, which is considerably higher than the similar reported antenna. This antenna is fast due to the small attached photodetector—small enough that it can be adapted for electronic devices—which does not need any tracking system. Moreover, numerical simulation is performed using a Monte Carlo ray-tracing model, and results are extracted in the spectral domain. Finally, the fate of each photon and the chromaticity diagram of the collected photons’ spectra are specified.

A Proposal for Optical Antenna in VLC Communication Receiver System

Visible Light Communication (VLC) is an important emerging choice for high-speed wireless communication. In this perspective, light-emitting diodes as illuminators will be modulated to transmit data simultaneously. However, the receivers bring severe difficulties due to cost, response time, and sensitivity with a wide Field Of View (FOV). To avoid these problems, one approach is to apply a large area photodetector; however, this solution is slow and costly. Another method is to focus light on a fast photodetector by optical components, but the photodetector’s FOV decreases, resulting from the conservation of etendue. Another option is Luminescent Solar Concentrators (LSCs). This paper demonstrates a novel shape of LSC with advantages such as inexpensive, fast response time, small antenna area for VLC purposes with significant geometrical gain, FOV, and ultra-broad bandwidth. It does not require any complex tracking system and active pointing but, due to its tiny size, it can also be adapted in integrating and mobile devices. Numerical simulation is done using Monte-Carlo raytracing, and the results are demonstrated in the spectral domain. The optical efficiency of the proposed antenna is obtained at 1.058%, which is about 0.4% better than the efficiency levels reported in other works, and the geometric gain of the antenna is reported to be 44, which is significant.

Designing Multi-Stage 2 A/O-MBR Processes for a Higher Removal Rate of Pollution in Wastewater

Multi-stage A/O-MBR processes were designed to improve wastewater treatment efficiency; three different designs were carried out and compared in this study. The 2(A/O)-MBR process, i.e., with two sets of anoxic/oxic tanks in series, showed better effluent quality than A/O-MBR and 3(A/O)-MBR processes. The removal rates of COD, NH4+-N, TP and TN were 95.29%, 89.47%, 83.55% and 78.58%, respectively, complying satisfactorily with China’s urban sewage treatment plant pollutant discharge standards. In terms of membrane fouling, the 3(A/O)-MBR process demonstrated the lowest fouling propensity. The microbial community structure in each bioreaction tank was analyzed, the results from which matched with the process efficiency and fouling behavior.

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.

Removal of Emerging Contaminants from Wastewater Streams Using Membrane Bioreactors: A Review

Water is a very valuable natural resource. As the demand for water increases the presence of emerging contaminants in wastewater has become a growing concern. This is particularly true when one considers direct reuse of wastewater. Obtaining sufficient removal of emerging contaminants will require determining the level of removal for the various unit operations in the wastewater treatment process. Membrane bioreactors are attractive as they combine an activated sludge process with a membrane separation step. They are frequently used in a wastewater treatment process and can operate at higher solid loadings than conventional activated sludge processes. Determining the level of removal of emerging contaminants in the membrane bioreactor step is, therefore, of great interest. Removal of emerging contaminants could be by adsorption onto the biomass or membrane surface, biotransformation, size exclusion by the membrane, or volatilization. Given the fact that most emerging contaminants are low molecule weight non-volatile compounds, the latter two methods of removal are usually unimportant. However, biotransformation and adsorption onto the biomass are important mechanisms of removal. It will be important to determine if the microorganisms present at given treatment facility are able to remove ECs present in the wastewater.

A breakthrough dynamic-osmotic membrane bioreactor/nanofiltration hybrid system for real municipal wastewater treatment and reuse

This study designed a Dynamic-Osmotic membrane bioreactor/nanofiltration (OsMBR/NF) system for municipal wastewater treatment and reuse. Results indicated that a continuously rotating FO module with 60 RPM in Dynamic-OsMBR system could enhance shear stress and reduce cake layer of foulants, leading to higher flux (50%) compared to Traditional-OsMBR during a 40-operation day. A negligible specific reverse salt flux (0.059 G/L) and a water flux of 2.86 LMH were recorded when a mixture of 0.1 M EDTA-2Na/0.1 M Na₂CO₃/0.9 mM Triton114 functioned as draw solution (DS). It was found that the Dynamic-OsMBR/NF hybrid system could effectively remove pollutants (∼98% COD, ∼99% PO₄^3-P, ∼93% NH₄⁺-N, > 99% suspended solids) from wastewater. In short, this developed system can be considered a breakthrough technology as it successfully minimizes membrane fouling by shear force, and achieves high water quality for reuse by two membrane- barriers.

Improving Formaldehyde Removal from Water and Wastewater by Fenton, Photo-Fenton and Ozonation/Fenton Processes through Optimization and Modeling

This study aimed to assess, optimize and model the efficiencies of Fenton, photo-Fenton and ozonation/Fenton processes in formaldehyde elimination from water and wastewater using the response surface methodology (RSM) and artificial neural network (ANN). A sensitivity analysis was used to determine the importance of the independent variables. The influences of different variables, including H2O2 concentration, initial formaldehyde concentration, Fe dosage, pH, contact time, UV and ozonation, on formaldehyde removal efficiency were studied. The optimized Fenton process demonstrated 75% formaldehyde removal from water. The best performance with 80% formaldehyde removal from wastewater was achieved using the combined ozonation/Fenton process. The developed ANN model demonstrated better adequacy and goodness of fit with a R2 of 0.9454 than the RSM model with a R2 of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H2O2 concentration (23%), Fe dosage (21%), contact time (14%) and formaldehyde concentration (12%). The findings demonstrated that these treatment processes and models are important tools for formaldehyde elimination from wastewater.