Wastewater Treatment Performance Research Articles

Co-based metal-organic frameworks for enhanced nickel adsorption and its impact on nitrifying microbial activity.

The release of nickel "Ni(II)" into aquatic environments is of great concern because of environmental and health issues. Metal-organic frameworks (MOFs) are one of the most promising technologies for removing heavy metals from water. In this work, an octahedral Co-based MOF (Co-MOF) was synthesized with a high Ni(II) removal capacity (qmax of 1534.09 ± 45.49 mg g-1) in aqueous media. For the first time, the effect of Co-MOF alone and in co-exposure with Ni(II) on nitrifying microbial consortium was assessed using dynamic microrespirometry. A single concentration of Co-MOF had no significant effects on nitrifying microbial consortium, while the concentration of Ni(II) exerted non-competitive inhibition on the nitrifying microbial consortium with an IC50 of 1.67 ± 0.03 mg L-1. In addition, the theoretical speciation analysis showed a decrease of 40% of IC50 when the free Ni(II) concentration was considered. Co-exposure of Co-MOF and Ni(II) during the nitrifying process allowed us to conclude that Co-MOF is an effective adsorbent for Ni(II) and can be used to mitigate the inhibitory effects of nickel on nitrifying microbial consortia, which is crucial for maintaining the good operation of wastewater treatment and balance of nitrogen cycle.

A novel approach to wastewater treatment control: A self-organizing fuzzy sliding mode controller

<p>The treatment of wastewater plays a crucial role in protecting the environment and ensuring the sustainable use of resources. This research paper presents a new methodology for managing wastewater treatment operations, utilising Self-Organizing Fuzzy Sliding Mode Controller (SOFSMC) to enhance the efficiency of treatment procedures. MATLAB Simulink functions as a simulation tool that facilitates meticulous analysis. SOFSMC presents a control strategy that is both adaptive and robust. This strategy effectively regulates crucial parameters, including dissolved oxygen levels, pH levels, and flow rates. It achieves this within the challenging and complex framework of wastewater treatment, which is characterised by dynamic and nonlinear dynamics. Using a SOFSMC for wastewater treatment control is novel approach. This novel technique creates a self-learning, dynamic system using fuzzy logic (FL) and sliding mode control (SMC). This unique approach can autonomously adapt to wastewater treatment processes' complex and nonlinear dynamics, improving efficiency, resource optimisation, and system dependability. The results emphasise the potential of SOFSMC as a revolutionary approach for wastewater treatment. This approach can improve treatment effectiveness, conserve resources, and protect the environment. The proposed method SOFSMC, exhibits commendable outcomes, with an integrated absolute error of 0.082 mg/L, an integrated square differential error of 0.091 mg/L, and a response time of 1.85 seconds This study offers a substantial advancement in the field of wastewater treatment regulation, highlighting its significance in the context of sustainable water management and environmental conservation.</p>

Multilayer polyelectrolyte and palladium nanoparticle decorated porous materials as fixed-bed reactors for efficient reduction of 4-NP

In this study, a continuous flow catalytic reactor based on three-dimensional porous sponge bed with the uniform distribution of catalyst to treat 4-NP wastewater efficiently. The porous sponge is used as a support to construct polyelectrolyte multilayers by loading dopamine. And the multilayer polyelectrolyte is closely bound to dopamine to form polyelectrolyte film, and the nanocatalysts are synthesized by electrostatic adsorption of precursors. The reactor realizes the uniform and stable distribution of the catalyst in the three-dimensional porous sponge, and the reactant transfers from bulk solution to catalyst surface by the way of convection and diffusion, which enhances mass transfer and further improves the reaction efficiency. Under the operating conditions of a catalytic bed as small as 3 cm, a 4-NP concentration up to 4.5 mM, and a flow rate of 300 μL min−1, the conversion rate remained above 96 % for up to 200 h, indicating that the reactor has good catalytic efficiency. The results showed that this continuous flow catalytic system structure has excellent performance in wastewater treatment.

Continuous flow-through electro-Fenton membrane reactor with Fe−N4-doped carbon membrane as cathode for efficient removal of dimethylacetamide

Fe − N4-doped conductive carbon membranes (FeCMs) were fabricated through high-temperature carbonization using chitosan and phenolic resin as the raw materials and ammonium ferrous sulfate (NH4Fe(SO4)2) as the dopant. The impact of the NH4Fe(SO4)2 doping rate (0 − 15 wt%) on the structure and performance of the FeCM was investigated. Moreover, a continuous flow-through electro-Fenton membrane reactor (EFMR) was constructed using an FeCM cathode and a Ti plate anode for the efficient removal of dimethylacetamide (DMAC). The resultant carbon membrane with 10 wt% NH4Fe(SO4)2 (M10) exhibited a smaller pore size (40.1 nm), a higher porosity (48.3 %), a larger specific surface area (531.6 m2/g), and a higher mechanical strength (6.7 M Pa) than the other membranes. The removal rate of DMAC by EFMR with M10 as the cathode at 2 V reached 42.1 %, which was much greater than that without NH4Fe(SO4)2 (3.6 %). The electrosynthesis ·OH concentration by EFMR with M10 as the cathode was approximately 89.1 mg/L, which was much greater than that of most carbon-based materials. The intermediates of DMAC degradation (such as N-methylacetamide, acetamide, acetaldehyde, and acetic acid) were determined by gas chromatography–mass spectrometry (GC–MS), and the removal of total organic carbon (TOC) was approximately 3.4 %. Density functional theory (DFT) calculations revealed that the Fe atom served as the active site for dissolved oxygen adsorption and electrogeneration of ·OH. After 5 days of continuous EFMR operation, the DMAC removal rate remained at 88.8 % of the initial value (42.1 %). This study presents a novel approach for designing and fabricating conductive carbon membranes to improve industrial wastewater treatment performance.

In situ enhanced chlorine oxide radical generation on a novel space-confined photoanode and its application for ammonia oxidation: Structure, performance, and mechanism

Oxidizing ammonia to nitrogen is a challenge in wastewater treatment. For this challenge ammonia oxidation via chlorine oxide radicals has been proved to be a potential method. In this study, a space-confined photoanode was designed and prepared by loading RuO2 nanoparticles on TiO2/WO3 nanowires for in situ enhancing the generation of chlorine oxide radical. Scanning electron microscopy characterization confirmed that RuO2 nanoparticles were uniformly deposited on TiO2/WO3 nanowires. The generation of chlorine oxide radical was markedly boosted, and its steady-state concentration reached 2.76 × 10−12 M. This value was 3 times that of the traditional bifacial electrode (a type of electrode that has been widely used for chlorine oxide radical-related research). As a result, ammonia oxidation was greatly enhanced. In 90 min, 31.9 mg/L ammonia nitrogen could be degraded, yet only 0.94 mg/L nitrate was accumulated. In addition, the space-confined photoanode exhibited good performance for real wastewater treatment, and 41.0 mg/L ammonia nitrogen could be oxidized in 135 min. During photoelectrocatalysis, a synergistic index (47.7 %) was obtained. Besides the synergistic effect between photocatalysis and electrocatalysis, the unique morphology and structural design (TiO2/WO3 and RuO2 were mainly responsible to hydroxyl radical generation and free chlorine production, respectively) also synergistically promoted the generation of chlorine oxide radical, thereby improving ammonia oxidation. The mechanism for ammonia oxidation via chlorine oxide radicals over the space-confined photoanode was proposed based on experimental results and theoretical calculations.

Stabilized oily-wastewater separation based on superhydrophilic and underwater superoleophobic ceramic membranes: Integrated experimental design and standalone machine learning algorithms

BackgroundReliable computational approaches to evaluate ceramic membrane performance in wastewater treatment mark a transformative step towards optimizing separation processes, ensuring environmental sustainability, and advancing water purification technologies. The current study explores the influential factors using artificial intelligence (AI) tools in the performance evaluation of superhydrophilic and underwater super-oleophobic ceramic membranes for the selective treatment of oily wastewater. MethodsThe chemometrics scenario of the research based on established experimental work employs advanced AI models viz: Random Forest (RF), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) to predict the efficacy of these membranes in terms of rejection and flux. The model predictions were evaluated using the Pearson Correlation Coefficient (PCC), Willmott Index (WI), mean absolute percentage error (MAPE), and mean absolute error (MAE). Significant findingsFrom the results, GPR had shown good agreement with correlations (WI=99.9) during the training and testing phases for flux prediction, indicating an exceptional model fit with negligible error (MAPE=0.001, MAE=0.000 in the testing phase). For rejection modelling, GPR and SVR exhibit similar levels of accuracy, with moderate PCC and WI values, while RF reveals significant limitations with the lowest scores across all statistical metrics. The findings highlight the potential of AI in optimizing wastewater treatment processes, with GPR identified as the most promising model for flux prediction. This study would provide insight into the modelling of the membrane separation process for oily wastewater and integrate AI in the performance evaluation of wastewater reclamation.

Poly(vinylidene fluoride) membrane design for efficient oil-in-water emulsion separation: Integrating superhydrophilicity, photocatalysis and self-cleaning

To solve the increasingly serious wastewater problems, developing membranes with excellent filtration performance and anti-fouling property has been a research focus. Herein, hydrophilic tannic acid/cysteine/tyrosine@titanium (TA/Cys/Tyr@Ti) double crosslinked coating was constructed on the poly (vinylidene fluoride) (PVDF) microfiltration membranes by two-step coating method, while the mineralized titanium dioxide (TiO2) nanoparticles were utilized to produce superhydrophilic membranes. The synergistic effect of the multilayered nanostructure and hydrophilic groups enables excellent underwater superoleophobicity and anti-fouling property of modified membranes. The addition of TiO2 provides photocatalytic degradation and self-cleaning properties. The optimized modified membrane (MF-4) showed outstanding oil–water separation potential (emulsion flux of 2760 ± 30 L·m−2·h−1, oil rejection rate of 99.5 ± 0.3 %) and stable recycling filter capacity. Moreover, MF-4 was able to efficiently degrade methylene blue under UV light, which was 99.1 % at 90 min. The flux recovery rate (FRR) was 97.4 % following simulated contamination and regeneration with bovine serum albumin. After UV irradiation, the FRR appeared to be significantly enhanced, proving its great anti-fouling and self-cleaning properties. This modified membranes with superhydrophilic and self-cleaning properties have great application potential in oily wastewater and achieving automatic operation of wastewater treatment.

Real-time monitoring of activated sludge flocs via enhanced mask region-based Convolutional Neural networks

The functionality of activated sludge in wastewater treatment processes depends largely on the structural and microbial composition of its flocs, which are complex assemblages of microorganisms and their secretions. However, monitoring these flocs in real-time and consistently has been challenging due to the lack of suitable technologies and analytical methods. Here we present a laboratory setup capable of capturing instantaneous microscopic images of activated sludge, along with algorithms to interpret these images. To improve floc identification, an advanced Mask R-CNN-based segmentation that integrates a Dual Attention Network (DANet) with an enhanced Feature Pyramid Network (FPN) was used to enhance feature extraction and segmentation accuracy. Additionally, our novel PointRend module meticulously refines the contours of boundaries, significantly minimising pixel inaccuracies. Impressively, our approach achieved a floc detection accuracy of >95%. This development marks a significant advancement in real-time sludge monitoring, offering essential insights for optimising wastewater treatment operations proactively.

Experimental Investigation for Enhancement of Solar Still Performance for Wastewater Treatment with the Influence of Encapsulated Phase Change Material

Experimental Investigation for Enhancement of Solar Still Performance for Wastewater Treatment with the Influence of Encapsulated Phase Change Material

Fast development of purple nonsulfur bacteria (PNSB) in a bubble column photobioreactor: Influence of carbon source and dissolved O2 availability on wastewater treatment performance

Photobioreactors with purple nonsulfur bacteria (PNSB) constitute a promising technology for wastewater treatment, gas purification, and high value-added bioproducts. In this work, different operational strategies were tested to investigate their impacts on photobioreactor performance, biomass properties, and microbial community. It showed that the operational conditions significantly influence the performance and microbial composition of PNSB photobioreactors. Anaerobic conditions favored the dominance of Rhodopseudomonas spp. and moderate COD removal efficiency (36.7 %), while aerobic conditions significantly enhanced COD removal efficiency (91.7 %) but altered the microbial community composition from PNSB to aerobic heterotrophic microorganisms, such as Delftia sp. and Microbacterium sp.. The removal ratio of chemical oxygen demand (COD):N:P was 100:10:2 under anaerobic conditions with continuous CO2 supply, while it was 100:1:1 under aerobic conditions with continuous supply of a CO2/O2 mixture. Net CO2 removal efficiencies of 2.8 % and 5.4 % were recorded under anaerobic conditions with and without the addition of organic carbon, respectively. The outlet CO2 concentration showed that CO2 uptake occurred under dark and anaerobic conditions without organic carbon. The reduced production of extracellular polymeric substances (EPS) was observed after COD was removed from the influent, preventing granule formation.

An intelligent predictive and optimized wastewater treatment plant.

The intelligent predictive and optimized wastewater treatment plant method represents a ground-breaking shift in how we manage wastewater. By capitalizing on data-driven predictive modeling, automation, and optimization strategies, it introduces a comprehensive framework designed to enhance the efficiency and sustainability of wastewater treatment operations. This methodology encompasses various essential phases, including data gathering and training, the integration of innovative computational models such as Chimp-based GoogLeNet (CbG), data processing, and performance prediction, all while fine-tuning operational parameters. The designed model is a hybrid of the Chimp optimization algorithm and GoogLeNet. The GoogLeNet is a type of deep convolutional architecture, and the Chimp optimization is one of the bio-inspired optimization models based on chimpanzee behavior. It optimizes the operational parameters, such as pH, dosage rate, effluent quality, and energy consumption, of the wastewater treatment plant, by fixing the optimal settings in the GoogLeNet. The designed model includes the process such as pre-processing and feature analysis for the effective prediction of the operation parameters and its optimization. Notably, this innovative approach provides several key advantages, including cost reduction in operations, improved environmental outcomes, and more effective resource management. Through continuous adaptation and refinement, this methodology not only optimizes wastewater treatment plant performance but also effectively tackles evolving environmental challenges while conserving resources. It represents a significant step forward in the quest for efficient and sustainable wastewater treatment practices. The RMSE, MAE, MAPE, and R2 scores for the suggested technique are 1.103, 0.233, 0.012, and 0.002. Also, the model has shown that power usage decreased to about 1.4%, while greenhouse gas emissions have significantly decreased to 0.12% than the existing techniques.

Unraveling the Potential of Microbial Flocculants: Preparation, Performance, and Applications in Wastewater Treatment

Microbial flocculants (MBFs), a class of eco-friendly and biodegradable biopolymers produced by various microorganisms, have gained increasing attention as promising alternatives to conventional chemical flocculants in wastewater treatment and pollutant removal. This review presents a comprehensive overview of the current state of MBF research, encompassing their diverse sources (bacteria, fungi, and algae), major categories (polysaccharides, proteins, and glycoproteins), production processes, and flocculation performance and mechanisms. The wide-ranging applications of MBFs in removing suspended solids, heavy metals, dyes, and other pollutants from industrial and municipal wastewater are critically examined, highlighting their superior efficiency, selectivity, and environmental compatibility compared to traditional flocculants. Nonetheless, bioflocculants face significant challenges including high substrate costs, low production yields, and intricate purification methodologies, factors that impede their industrial scalability. Moreover, the risk of microbial contamination and the attendant health implications associated with the use of microbial flocculants (MBFs) necessitate thorough evaluation. To address the challenges of high production costs and variable product quality, strategies such as waste valorization, strain improvement, process optimization, and biosafety evaluation are discussed. Moreover, the development of multifunctional MBF-based flocculants and their synergistic use with other treatment technologies are identified as emerging trends for enhanced wastewater treatment and resource recovery. Future research directions are outlined, emphasizing the need for in-depth mechanistic studies, advanced characterization techniques, pilot-scale demonstrations to accelerate the industrial adoption of MBF, and moreover, integration with novel wastewater treatment processes, such as partial nitrification and the anammox process. This review is intended to inspire and guide further research and development efforts aimed at unlocking the full potential of MBFs as sustainable, high-performance, and cost-effective bioflocculants for addressing the escalating challenges in wastewater management and environmental conservation.

Deep learning-based image analysis for filamentous and floc-forming bacteria in wastewater treatment

In municipal wastewater treatment, effective secondary clarification relies on the balance between floc-forming bacteria and filamentous bacteria. Consequently, comprehensive and real-time monitoring of this balance will enable reliable operation of biological wastewater treatment. This research presents an artificial intelligence (AI)-based approach for the classification of filamentous and floc-forming bacteria in microscopic images using deep learning. To provide ground truth labeling, an automated rule-based segmentation algorithm was developed using color and morphology criteria along with supplementary filtration steps to enhance the precision of filamentous and floc-forming bacteria identification. The segmentation algorithm demonstrated reliable detection and categorization of bacteria across varying background intensities and effectively recognized intricate microbial configurations. Subsequently, the supervised deep learning model was trained on the segmented images and constructed with an encoder/decoder architecture. Machine training with only 68 microscopic images demonstrated successful classification of the filamentous and floc-forming bacteria with a 97.8 % accuracy. In addition, qualitative evaluation demonstrated that the deep learning model could generalize machine understanding across diverse scenarios and discern misclassified filamentous bacteria accurately. The proposed model stands as a promising automated tool for real-time quantification of filamentous and floc-forming bacteria in bioreactors and clarifiers, offering the potential for reliable operation as well as immediate actions for sludge bulking and membrane fouling problems.

Recent progress and perspectives of typical renewable bio-based flocculants: characteristics and application in wastewater treatment.

The research on bio-based flocculants for waste resource utilization and environmental protection has garnered significant attention. Bio-based flocculants encompass plant-based, animal-based, and microbial variants that are prepared and modified through biological, chemical, and physical methods. These flocculants possess abundant functional groups, unique structures, and distinctive characteristics. This review comprehensively discussed the removal rates of conventional pollutants and emerging pollutants by bio-based flocculants, the interaction between these flocculants and pollutants, their impact on flocculation performance in wastewater treatment, as well as their application cost. Furthermore, it described the common challenges faced by bio-based flocculants in practical applications along with various improvement strategies to address them. With their safety profile, environmental friendliness, efficiency, renewability, and wide availability from diverse sources, bio-based flocculants hold great potential for widespread use in wastewater treatment.

Characterization of Moroccan cactus juice toward its use as a green flocculant in wastewater treatment

ABSTRACT The present work is dedicated to the characterization of Moroccan cactus juice, which shows encouraging performance in wastewater treatment. Various techniques were used to highlight the chemical and physical properties of this natural product. Elemental analysis of cactus juice using inductively coupled plasma emission spectroscopy revealed the presence of numerous minerals. Fourier Transform Infrared, ultraviolet, and nuclear magnetic spectroscopies demonstrate the presence of many carbohydrate components. Identification of volatile compounds by gas chromatography reveals the presence of Gibb-3-ene-1,10-dicarboxylic acid, ethyl gallate, 3TMS derivative, ethyl ester and 1-(2-Isopropyl-phenyl)-3,6,6-trimethyl-1,5,6,7-tetrahydro–indazol. Thermogravimetric analysis indicates three peaks at 100, 200 and 600 °C, corresponding to the mass loss of water, polysaccharides, and organic matter, respectively. The rheological characterization of cactus juice suggests that this liquid exhibits shear-thinning behavior, which is governed by the Casson model. The results of the Zeta potential measurement demonstrate the anionic character of cactus juice as a flocculant, with −25 mV as a value of zeta potential. Its use as a flocculant in organic matter removal shows that the chemical Oxygen demand decreases significantly with the increase of the added cactus juice dose, confirming, therefore, its flocculating behavior.

Biostimulation of styrene-containing wastewater in carrier-supported anaerobic bioreactor towards enhancing biogas production and styrene degradation

Biological treatment of styrene-containing petrochemical wastewater usually suffers from styrene volatility and its toxicity to microorganisms. The main aim of the present study was to enhance the treatment performance of synthetic petrochemical wastewater using carrier-supported anaerobic bioreactor, in terms of biogas production and styrene degradation. For this purpose, three types of carrier, including magnetic granular activated carbon (MGAC), cuttlefish bone (CFB) and pumice, were used. A control reactor and the three carrier-supported reactors were operated under three styrene concentrations of 20 (cycle 1), 50 (cycle 2), and 100 (cycle 3) mg/L. Ethanol was used as a co-solvent and the total chemical oxygen demand (COD) was 500 mg/L in each cycle. According to the results, under all examined styrene concentrations, all the carrier-supported reactors exhibited higher styrene removal efficiency and gas production than the control reactor. Among carrier-containing rectors, the reactor supplemented with MGAC showed the best performance with styrene removal of 85.4 %–91.4 % and gas production of 368–890 mL/g COD added. The corresponding values for the control reactor were 72.3–79.2 % and 174–358 mL/g COD added. In addition, Carrier-containing reactors showed 31.2–67.2 % less styrene volatilization than the control reactor. The process performance was also affected by the styrene concentration so that the highest and the lowest gas productions were achieved for styrene contents of 50 and 100 mg/L, orderly. Using modified Gompertz model, it was found that the carriers shortened the lag phase and increased the maximum methane production rate, proving the establishment of stable conditions in carrier-supported reactors for the treatment of styrene-containing wastewater.

Evaluation of the effluent concentrations of Karbala wastewater treatment plant using reliability analysis

Abstract The effective and consistent operation of wastewater treatment plant systems (WWTPs) is crucial for the sustainability of the environment and public health protection. The main objective of the present study is concentrated on assessing the reliability of the Karbala wastewater treatment plant’s (WWTP’s) performance. It investigates the plant’s efficiency through the weekly concentration values of three key water quality indicators, which are biochemical oxygen demand (BOD), total suspended solids (TSS), and chemical oxygen demand (COD) collected over 4 years of operation from 2020 to 2023. The methods employed were the coefficient of reliability (COR) method for plant performance in removing the effluent concentrations of 5-day biochemical oxygen demand, COD, and TSS. The analysis found that the COR values were generally close to 1 for all years, with the lowest value recorded at 0.71 in 2020, during the initial stabilization phase of the WWTP. The main finding was that the Karbala WWTP has been effective in pollutant removal. The present study is important because it supplies dependable data that wastewater treatment operators can use to assess their daily operations and gauge the success of biological treatment methods. It is worth noting that no study has been done on the reliability model for examining the quality of wastewater of the Karbala WWTP, and such a method of analysis is considered a new improvement for the evaluation of the plant to meet the Iraqi standards.

Enhancement the wastewater treatment performance of multistage living machine by underwater lamp

Source separation and decentralized domestic wastewater treatment represent effective strategies to enhance sewage treatment performance and facilitate water reuse economically. The Living Machine (LM) system has gained widespread adoption for decentralized sewage treatment. While underwater light source has been demonstrated to enhance the treatment performance of open aerobic reactors in LM systems, its influence on the treatment efficiency of a fully multistage LM system remains underreported. In this study, an underwater lamp-added LM system (ULLM) with eight reactors was constructed and investigated. The introduction of underwater light source obviously improved the removal capacity of chemical oxygen demand (COD) and NH4+-N, which was 96.1% and 61.6%, respectively. The diversity of algae, zooplankton, and aquatic animals was notably higher in the light-treated reactors than in the control group (CK) without underwater light source, and substantial alteration in the microbial community of the light-treated reactors was observed compared with CK reactors. At the phylum level, Proteobacteria and Nitrospirae enriched in the underwater light-treated reactors, while Bacteroidetes and Actinobacteria exhibited a decrease after light exposure. At the genus level, Nitrospira and Rhodanobacter were enriched in the ULLM system. Importantly, the prevalence of these two dominant genera was sustained until the final operational stage, indicating their potential key roles in enhancing wastewater treatment performance. The addition of underwater light source proves to be an effective strategy for augmenting the treatment efficiency of the multistage living machine systems, resulting in substantial improvements in pollutant removal. These findings contribute valuable insights into optimizing LM systems for decentralized wastewater treatment.

Impact of truncated modified basalt fibers on aerobic granular sludge stability and pollutant removal in piggery wastewater treatment

Modified basalt fibers (MBF) and carbon fibers (CF) are effective bio-carrier materials that enhance wastewater treatment performance. This study explored the use of two lengths of truncated MBF and CF (0.1 and 0.4 mm) in a sequencing batch reactor to support activated sludge treatment, facilitating the formation of aerobic granular sludge (AGS). This approach aimed to enhance the stability and pollutant removal efficiency of AGS, particularly for treating piggery wastewater (PWW). The study further assessed the impact of adding truncated fibers on AGS formation, stability, and its chemical oxygen demand (COD) and total nitrogen (TN) removal capabilities. Results indicate that truncated MBF notably enhanced sludge activity, promoting the secretion of extracellular polymeric substances (EPS) within AGS. This process further increased the protein/polysaccharide ratio and bolstered AGS stability. Specifically, the addition of 0.1 mm MBF boosted PWW treatment efficiency. Fluorescence in situ hybridization (FISH) revealed that the addition of fibers had a minimal impact on the distribution of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) within AGS, although truncated MBF enriched the population of AOB. Microbial community analysis showed that the addition of MBF favored the enrichment of bacteria associated with nitrogen removal, upregulating the tricarboxylic acid cycle, nitrogen and amino acid metabolism. Overall, truncated MBF significantly improved AGS stability and its effectiveness in PWW treatment.

Distinct microbial communities enriched in water-saturated and unsaturated reactors influence performance of integrated hydroponics-microbial electrochemical technology

This study aimed to understand the wastewater treatment and electricity generation performance besides the microbial communities of the integrated Hydroponics-Microbial Electrochemical Technology (iHydroMET) systems operated with water-saturated and water-unsaturated reactors. The organics removal was slightly higher in the water-unsaturated system (93 ± 4 %) than in the water-saturated system (87 ± 2 %). The total nitrogen removal and electric voltage were considerably higher in the water-saturated system (42 ± 5 %; 111 ± 8 V per reactor) than in the water-unsaturated system (18 ± 3 %; 95 ± 9 V per reactor). The enhanced organics and nitrogen removal and high voltage output in respective conditions were due to the dominance of polysaccharide-degrading aerobes (e.g., Pirellula), anammox bacteria (e.g., Anammoximicrobium), denitrifiers (e.g., Thauera and Rheinheimera), and electroactive microorganisms (e.g., Geobacter). The differential performance governed by distinct microbial communities under the tested conditions indicates that an appropriate balancing of water saturation and unsaturation in reactors is crucial to achieving optimum iHydroMET performance.