Effluent Chemical Oxygen Demand Research Articles

Enhanced nitrogen removal for low C/N wastewater via preventing futile carbon oxidation and augmenting anammox

Efficient carbon use is crucial for biological nitrogen removal. Traditional aerobic processes can waste carbon sources, exacerbating carbon deficiency. This study explores an anaerobic/oxic/anoxic system with sludge double recirculation to improve nitrogen removal in low C/N wastewater. This system integrated aerobic nitrification after the carbon intracellular storage, separating carbon and nitrogen by denitrifying glycogen-accumulating organisms (DGAOs) with endogenous partial denitrification and Anammox within the anoxic units. A significant efficiency of 91.02±7.01% chemical oxygen demand (COD) was converted into intracellular carbon in anaerobic units, significantly reducing carbon futile oxidation in the aerobic units by effectively separating COD from ammonia. Intracellular storage of carbon sources and microbial adaptation to carbon scarcity prevent futile oxidation of COD in the aerobic units even with short-term high dissolved oxygen (DO), thereby enhancing nitrogen removal under anoxic conditions with sufficient intracellular carbon source. The microbial analysis identified Candidatus Brocadia as the dominant anammox bacteria, in combination with the activity of DGAOs and other related microbial communities, accounting for 37.0% of the TN removal. Consequently, the system demonstrated remarkable nitrogen removal efficiencies, achieving 81.3±3.3% for total nitrogen (TN) and 98.5±0.9% for ammonia nitrogen while maintaining an effluent COD concentration of 17.2±9.1 mg/L, treating the low C/N of 4.18 in the influent wastewater. The findings in this study provide a sustainable and energy-saving technique for conventional WWTPs to meet strict discharge standards by avoiding futile oxidation of COD and encouraging anammox contributions.

Biofilm characterization and dynamic simulation of advanced rope media reactor for the treatment of primary effluent.

Biofilm modeling is inherently complex, often requiring multiple assumptions and simplifications. In biofilm modeling, default or literature-based values in biofilm systems are usually used to estimate biofilm parameters, including boundary layer, biofilm density, thickness, attachment, and detachment rates. This study aimed to characterize and model the biofilm of a specific rope-type fixed media system, removing carbon and total inorganic nitrogen, coupled with sensitivity analysis. Among the five model parameters, the sensitivity analysis of this study showed that boundary layer thickness is the most influential parameter for predicting effluent ammonia and nitrate concentrations, and biofilm density is most sensitive with respect to effluent chemical oxygen demand (COD). The least sensitive parameter is the detachment rate. Based on the calculated mean absolute error (MAE) and root mean squared error (RMSE), the calibrated BioCord fixed-film reactor (BFFR) model accurately predicted effluent ammonium and dissolved oxygen (DO) in the continuously aerated bench-scale reactor (R1) and failed to predict well in the intermittently aerated bench-scale reactor (R2). RMSE values calculated for NH3-N and DO in R1 are 0.95 and 0.53 mg/L, respectively. In the BioCord pilot plant's case, ammonium-N predicted by the model fit the measured values well, while it overpredicted DO concentrations. PRACTITIONER POINTS: Fixed biofilm BioCord reactors were studied for primary effluent treatment. A methodology was developed to characterize biofilms. Boundary layer thickness is the most influential parameter for predicting effluent ammonia and nitrate concentrations. Biofilm density is the most sensitive parameter with respect to effluent COD. The calibrated BFFR model can predict effluent ammonium, nitrite, and nitrate-nitrogen.

Experimental study on the treatment of high COD metal degreasing cleaning wastewater by coagulation and two-stage Fenton oxidation

ABSTRACT In this study, based on the water quality characteristics of metal degreasing cleaning wastewater , wastewater treatment by coagulation combined with two-stage Fenton oxidation was proposed. The results showed that when the dosage of PAC was 5 g/L and the dosage of CPAM was 8 mg/L, with an initial pH of 8, rapid stirring at 350 rpm for 3 min followed by slow stirring at 200 rpm for 15 min, the optimal removal rate of chemical oxygen demand (COD) reached 74.08%. Primary Fenton oxidation is mainly aimed at decomposing macromolecular long-chain pollutants and removing some pollutants. The highest COD removal rate was achieved at 95.07% when the dosage of H2O2 was 4 mol/L, the dosage of FeSO4 was 40 g/L, the oxidation lasted for 60 min, the initial pH was 2, the effluent pH was 8, and the settling time was 90 min. The two-stage Fenton oxidation process provided further advanced treatment. With a reagent ratio of 1:0.5, the effluent COD concentration was reduced to 457 mg/L, meeting the third class standard of the `Integrated Wastewater Discharge Standard' (GB 8978 1996).

Start-up phase performance evaluation of a pilot-scale sequential anaerobic-aerobic hybrid algal-bacterial suspended growth reactor for the domestic wastewater treatment

This study evaluated the start-up phase performance of an 80-cubic-meter pilot-scale anaerobic-aerobic hybrid algal-bacterial reactor treating real domestic wastewater operated at 24 h hydraulic retention time. The aerobic chambers of the reactor were inoculated with heterotrophic bacterial biomass and mixed microalgal culture. Blue light-emitting-diode (LED) lights were provided in the aerobic chambers to facilitate algal photosynthesis. The influent chemical oxygen demand (COD), total nitrogen (TN), and phosphorus (P) values were 241 ± 45 mg/L, 26.8 ± 4.2 mg/L, and 6.7 ± 0.9 mg/L, respectively. After thirty days of operation, the effluent COD, TN, and P values were observed to be 26.7 ± 6.5 mg/L, 15.6 ± 0.73 mg/L, and 4.45 ± 0.16 mg/L, respectively. The sludge volume index of the mixed liquor was 43 mL/g, indicating high settleability. During the operation, an increase in the chlorophyll to biomass ratio was observed, which implies that the algal-bacterial symbiosis might have played a role in simultaneous carbon and nitrogen removal from the domestic wastewater.

Continuous self-circulating up-flow granular sludge fluidized bed process treating low-strength real municipal wastewater at high hydraulic loads

Conditions conducive to aerobic granular sludge (AGS) growth and maintenance are very difficult to realize in continuous-flow biological treatment processes. This study conducted a continuous-flow self-circulating up-flow granular sludge fluidized bed (Zier process) treating real urban wastewater approximately one year. The substantial self-circulating multiple times (RSCMT, 8–15 times) and up-flow velocity (8–15 m/h) generated by aeration, the only power equipment in Zier process, facilitated pollutant removal, particle granulation and stabilization. With hydraulic retention time of 5 h, RSCMT of 9.3–14.4 times and chemical oxygen demand (COD)/total nitrogen (TN) ratio of 5.9 ± 1.0, the effluent COD, ammonia nitrogen and TN were 28.6 ± 7.7, 1.1 ± 1.2, and 13.3 ± 1.7 mg/L, respectively. The median particle size was 150–250 μm and effluent suspended solids concentration was 33.4 ± 14.5 mg/L. It is unnecessary to set up sludge reflux which simplifies the subsequent mud-water separation facilities. The Zier process provides a new process structure for implementation of continuous-flow AGS process.

The Valorization of Fruit and Vegetable Wastes Using an Anaerobic Fixed Biofilm Reactor: A Case of Discarded Tomatoes from a Traditional Market

Tomato waste, characterized by high organic matter and moisture content, offers a promising substrate for anaerobic digestion, though rapid acidification can inhibit methanogenic activity. This study investigated the performance of a 10.25 L anaerobic fixed biofilm reactor for biogas production using liquid tomato waste, processed through grinding and filtration, at high organic loading rates, without external pH control or co-digestion. Four scouring pads were vertically installed as a fixed bed within a fiberglass structure. Reactor performance and buffering capacity were assessed over three stages with progressively increasing organic loading rates (3.2, 4.35, and 6.26 gCOD/L·d). Methane yields of 0.419 LCH4/gCOD and 0.563 LCH4/g VS were achieved during the kinetic study following stabilization. Biogas production rates reached 1586 mL/h, 1804 mL/h, and 4117 mL/h across the three stages, with methane contents of 69%, 65%, and 72.3%, respectively. Partial alkalinity fluctuated, starting above 1500 mg CaCO3/L in Stage 1, dropping below 500 mg CaCO3/L in Stage 2, and surpassing 3000 mg CaCO3/L in Stage 3. Despite periods of forced acidification, the system demonstrated significant resilience and high buffering capacity, maintaining stability through hydraulic retention time adjustments without the need for external pH regulation. The key stability indicators identified include partial alkalinity, effluent chemical oxygen demand, pH, and one-day cumulative biogas. This study highlights the effectiveness of anaerobic fixed biofilm reactors in treating tomato waste and similar fruit and vegetable residues for sustainable biogas production.

Study of an electrochemical system with dual cathodes for the treatment of mariculture wastewater

The salinity properties of mariculture wastewater make it a suitable candidate for electrochemical treatment. In this study, an electro-Fenton synergistic electrocatalytic system was developed using a mixed metal oxide anode, often called Dimensionally Stable Anodes (DSA), and dual cathodes, an iron-carrying activated carbon sheet as the first cathode and a stainless-steel plate as the second cathode, to treat mariculture wastewater. The reaction conditions of the system were investigated and optimized. The chemical oxygen demand (COD) removal rate reached 100 % after 80 min under the optimal conditions: an applied voltage of 3.39 V, a plate spacing of 1.07 cm, and an aeration rate of 34.84 L·h−1. The system effectively removed COD and nitrogen from mariculture wastewater. Analyses using GC–MS, UV–visible spectroscopy, and Fourier-transform infrared spectroscopy showed that the system could degrade macromolecular organic pollutants in the wastewater. Mechanistic studies revealed that active chlorine and hydroxyl radicals play crucial roles in pollutant removal. The dynamic operation study indicated that the optimal operating conditions included a vertical water flow direction through the electrodes, and a flow rate of 10 mL·min−1. The oxidant reuse experiment determined a reflux ratio of 1:1 and a reaction time of 60 min, achieving effluent COD and NH3-N concentrations that met the primary discharge standards. The study on residual chlorine removal showed that the input amount of sodium thiosulfate was 0.36 kg, with a treatment cost of 0.32 yuan per ton of water, and the treated effluent met the discharge standards. These results provide a reference for the industrial application of electrochemical methods in treating mariculture wastewater.

Prediction and optimization of wastewater treatment process effluent chemical oxygen demand and energy consumption based on typical ensemble learning models

Pollution integration and carbon reduction has become a primary focus in wastewater treatment processes. In this study, water quality and control indicators were used as input features and the dataset was extended using the moving average method. Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine algorithms were used to predict the effluent chemical oxygen demand (COD) and total energy consumption (TEC). The results indicated that the model prediction performance could be effectively improved when the data were amplified by two times and that the XGBoost model exhibited the best prediction performance for effluent COD and TEC. The Non-dominated Sorting Genetic Algorithm II model was employed for the multi-objective optimization of effluent COD and TEC, resulting in reductions of 15% and 18%, respectively. The ensemble learning model proposed in this study to achieve synergy between water quality improvement and energy saving is practical.

Efficient nitrogen removal of decentralized rural sewage: Pilot-scale study of a novel push-flow coupled bio-ecological system

The performance and mechanisms for pollutant removal were investigated in an extended pilot-scale operation. Results demonstrated that the PF-EFB system maintained stable and efficient performance after over 450 days of continuous operation. Under various operating conditions, the total nitrogen (TN) concentration was reduced to <2 mg/L, and the removal rate exceeded 90 %. The effluent chemical oxygen demand (COD) concentration remained below 40 mg/L. Additionally, a combination of nitrogen transformation and molecular biology analyses was conducted to explore the mechanisms. The key bacterial groups driving system performance were aerobic and anoxic nitrifying bacteria, which dominated the microbial population. Microbial community analysis showed that the abundance of aerobic denitrifying bacteria in the middle of the system was 40.5 %, while the abundance of anoxic denitrifying bacteria at the end was 19.0 %. This spatial variation in the distribution of aerobic and anoxic denitrifying bacteria along the flow direction inside the reactor, driven by environmental factors, is key to the system's efficient denitrification. Overall, the PF-EFB system effectively addressed challenges posed by water flow fluctuations and enhanced nitrogen removal efficiency. This innovative system offers significant benefits and serves as a valuable reference for rural sewage treatment.

Low-cost, reliable, and highly efficient removal of COD and total nitrogen from sewage using a sponge-filled trickling filter.

Development of low-cost and reliable reactors demanding minimal supervision is a need-of-the-hour for sewage treatment in rural areas. This study explores the performance of a multi-stage sponge-filled trickling filter (SPTF) for sewage treatment, employing polyethylene (PE) and polyurethane (PU) media. Chemical oxygen demand (COD) and nitrogen transformation were evaluated at hydraulic loading rates (HLRs) ranging from 2 to 6 m/d using synthetic sewage as influent. At influent COD of ∼350 mg/L, PU-SPTF and PE-SPTF achieved a COD removal of 97% across all HLRs with most of the removal occurring in the first segments. Operation of PE-SPTF at an HLR of 6 m/d caused substantial wash-out of biomass, while PU-SPTF retained biomass and achieved effluent COD < 10 mg/L even at HLR of 8-10 m/d. The maximum Total Nitrogen removal by PE-SPTF and PU-SPTF reactors was 93.56 ± 1.36 and 92.24 ± 0.66%, respectively, at an HLR of 6 m/d. Simultaneous removal of ammonia and nitrate was observed at all the HLRs in the first segment of both SPTFs indicating ANAMMOX activity. COD removal data, media depth, and HLRs were fitted (R2 > 0.99) to a first-order kinetic relationship. For a comparable COD removal, CO2 emission by PU-SPTF was 3.5% of that of an activated sludge system.

Novel membrane bioreactor with iron-tannin-framework based membrane for effluent quality improvement and fouling control

A 10 L membrane bioreactor (MBR) was constructed with novel iron-tannin-framework ultrafiltration membrane (ITUM) for membrane fouling mitigation and effluent quality improvement. Compared to commercial ultrafiltration membrane (CUM) in the same MBR, ITUM exhibited hydrophilicity with a contact angle of 64.18° (71.36° for CUM), resulting the permeance of ITUM (50.2 L m-2h−2 bar−1) 1.8 times higher than CUM. During 70-day operation, ITUM was consistently run at lower transmembrane pressure. After operation, the polyethersulfone (PES) peak intensity of ITUM reduced less than 1 % (85 % for CUM), which indicated a thinner sludge layer on ITUM. Meanwhile, the average effluent chemical oxygen demand concentration for ITUM and CUM achieved 16.69 and 17.60 mg L-1 in stable phase, respectively. For NH4+-N, it could reach 0 and 0.04 mg L-1. 16 s RNA results showed that Saccharibacteria accounted for 1.48 % and 1.20 % on ITUM and CUM, respectively. According to first-principles density functional theory simulations, simulated adsorption energies was −1.330 eV for gram-positive bacteria (such as Saccharibacteria). It was −0.867 eV for Fe(Ⅲ) to gram-negative bacteria. It suggests that ITUM could easily attract microorganisms with the ability of polysaccharide degradation, while reducing this type of pollutants deposition on membrane. Therefore, the ITUM received a more superior effluent quality and membrane fouling resistance.

Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater

To identify the complex time patterns in the process data and monitor the effect of wastewater treatment by predicting effluent chemical oxygen demand more accurately, a soft-sensor modeling method based on the multi-scale temporal convolutional network (MSTCN) was proposed in this paper. Data at different time scales are reconstructed according to the main frequencies determined by the Fourier transform approach, and the correlations between variables during that period are calculated and stored in the corresponding adjacency matrix. The specific temporal convolutional network (TCN) is built to learn the temporal dependencies within each sequence at the current scale, while the graph convolutional layer (GCN) captures the relationships among variables. Finally, predictions with less error can be obtained by integrating output features from GCN and TCN layers. The proposed model is validated on an annual dataset collected from a wastewater treatment plant employing biological processes for organic matter removal. The experimental results indicate that the proposed MSTCN reduces RMSE by 35.71% and 22.56% compared with the convolutional neural network and TCN, respectively. Moreover, MSCTN shortens the training period by 6.3 s and improves RMSE by 30.41% when compared to the long short-term memory network, which is outperformed in extracting temporal dynamic characteristics.

Model-based development of cell voltage control system for modified bio-electro-Fenton process

A modified bio-electro-Fenton (M−BEF) process with a cell voltage control system that improves the efficiency of organic removal and energy savings is demonstrated. The M−BEF process can accomplish bioelectricity generation, H2O2 production, and the Fenton reaction in a continuous-flow reactor. During synthetic wastewater treatment containing biodegradable (glucose) and recalcitrant (biphenyl) organic matter, the effluent chemical oxygen demand (COD) concentration was maintained between 2 and 6 mg L-1. To investigate the impact of different operating schemes on energy usage, model-based design (MBD) modeling and simulations were performed, which showed that COD removal efficiency without an external voltage supply was unstable at < 70 %. The automatic cell voltage control system saved 90 % of the power compared to the continuous cell voltage supply system. Further testing on more environmental samples and pollutants will enable real-time optimization of supplied power and wastewater treatment using the cell voltage control system.

Prediction of electrocoagulation treatment of tannery wastewater using multiple linear regression based ANN: Comparative study on plane and punched electrodes

This study investigated the electrocoagulation (EC) treatment of tannery wastewater using plane and punched aluminum and iron electrodes at the optimum condition of pH 9, voltage 20 V, electrode distance of 1 cm and 90 min electrolysis duration. The efficiency of the EC process was determined by evaluating the levels of biochemical oxygen demand (BOD), chemical oxygen demand (COD), and Chromium (Cr) in the treated effluents. The experiment utilized both linear regression and Artificial Neural Network (ANN) models for modeling, with the ANN model validating the predicted model from the experimental design with 95 % confidence. The use of plane aluminum electrodes resulted in an optimum removal efficiency of BOD (89.66 %), COD (96.21 %), Cr (96.05 %), and TDS (95.77 %). On the other hand, the punched electrodes achieved a removal efficiency of 90.86 % (BOD), 98.62 % (COD), 96.94 % (Cr), and 96.92 % (TDS). Similarly, when using plane iron electrodes, the removal efficiency of BOD, COD, Cr and TDS was 87.57 %, 94.77 % 93.42 % and 93.08 %, respectively, while punched iron electrodes removed 89.01 % of BOD, 96.59 % of COD, 94.66 % of Cr and 95 % of TDS. The results demonstrate that the proposed ANN effectively predicts effluent BOD, COD, Cr and TDS, addressing economic and environmental sustainability concerns.

Enhancing wastewater treatment efficiency through hydrodynamic cavitation and advanced oxidation processes: Experimental insights and comparative analysis

BackgroundThe escalating industrial wastewater production mandates effective treatment methods to mitigate environmental and health risks. Hydrodynamic cavitation (HC) and its combination with advanced oxidation processes (AOPs) offer promising approaches for wastewater pollution reduction. MethodsThis study investigates the enhancement of HC efficiency for Chemical Oxygen Demand (COD) reduction by integrating hydrogen peroxide (H2O2) and air injection. Synthetic wastewater resembling industrial effluent COD levels serves as the experimental medium. Varied inlet pressures, H2O2 concentrations, and air injection rates were examined to gauge their impact on COD reduction. Significant FindingsHigher inlet pressures directly correlate with increased COD reduction, highlighting intensified cavitation effects. Combining HC with H2O2 or air, especially when directly injected into the venturi throat, showcased substantial synergy, significantly reducing COD levels. Notably, the maximum extent of COD reduction, achieved at 51.85 %, was with the combination of (HC+H2O2+Air). The COD reduction percentages for other processes, including HC alone, (HC+Air tank injection), (HC+Air throat injection), (HC+H2O2 tank injection), and (HC+H2O2 throat injection), were 4.21 %, 19.7 %, 20.9 %, 37.8 %, and 39.4 %, respectively.

Comparative study for the performance of pure artificial intelligence software sensor and self-organizing map assisted software sensor in predicting 5-day biochemical oxygen demand for Kauma Sewage Treatment Plant effluent in Malawi

Introduction: Modeling plays a crucial role in understanding wastewater treatment processes, yet conventional deterministic models face challenges due to complexity and uncertainty. Artificial intelligence offers an alternative, requiring no prior system knowledge. This study tested the reliability of the Adaptive Fuzzy Inference System (ANFIS), an artificial intelligence algorithm that integrates both neural networks and fuzzy logic principles, to predict effluent Biochemical Oxygen Demand. An important indicator of organic pollution in wastewater.Materials and Methods: The ANFIS models were developed and validated with historical wastewater quality data for the Kauma Sewage Treatment Plant located in Lilongwe City, Malawi. A Self Organizing Map (SOM) was applied to extract features of the raw data to enhance the performance of ANFIS. Cost-effective, quicker, and easier-to-measure variables were selected as possible predictors while using their respective correlations with effluent. Influents’ temperature, pH, dissolved oxygen, and effluent chemical oxygen demand were among the model predictors.Results and Discussions: The comparative results demonstrated that for the same model structure, the ANFIS model achieved correlation coefficients (R) of 0.92, 0.90, and 0.81 during training, testing, and validation respectively, whereas the SOM-assisted ANFIS Model achieved R Values of 0.99, 0.87 and 0.94. Overall, despite the slight decrease in R-value during the testing stage, the SOM- assisted ANFIS model outperformed the traditional ANFIS model in terms of predictive capability. A graphic user interface was developed to improve user interaction and friendliness of the developed model. Integration of the developed model with supervisory control and data acquisition system is recommended. The study also recommends widening the application of the developed model, by retraining it with data from other wastewater treatment facilities and rivers in Malawi.

Enhancing the effluent prediction accuracy with insufficient data based on transfer learning and LSTM algorithm in WWTPs

Recently, there has been rapid advancement in artificial intelligence (AI), revolutionizing numerous industries. But the application of AI in the wastewater treatment plants (WWTPs) is still in infancy with limited intelligence capabilities, due to insufficient data. In this study, a transfer learning algorithm based on a long and short-term memory neural network (TL-LSTM) was proposed for predicting effluent chemical oxygen demand (COD) in WWTP. The TL-LSTM approach involves the transfer of neural network parameters from a source domain with amounts of data to a target domain with insufficient data to improve prediction accuracy. Results indicate that TL-LSTM yielded a root mean square error (RMSE) of 0.627 mg/L and a determination coefficient R2 score of 0.811, outperforming a long and short-term memory neural network (LSTM) with RMSE of 0.754 mg/L and R2 of 0.727. Moreover, the effectiveness of TL-LSTM was further confirmed by other effluent indicators (NH3-N, TP and pH). Compared to LSTM, TL-LSTM achieves reduced RMSE by 16.8 %, 15.2 %, 7 % and 14.3 % for predicting effluent COD, NH3-N, TP and pH, respectively. Thus, this study underscores the relevance of TL-LSTM as a valuable tool for early warning, energy-saving and consumption reduction in WWTPs with insufficient data.

Cultivation of Microalgae in Liquid Digestate to Remove Nutrients and Organic Contaminants

The goal of this research was to assess the efficiency of the liquid digestate treatment conducted with algal, environmental isolates illuminated entirely with sunlight. The photobioreactor was exposed to natural conditions and evaluated based on the reduction of chemical oxygen demand (COD), nitrogen compounds, and soluble phosphates. Microalgal and bacterial communities growing during the treatment process were studied. A high removal rate of soluble COD (= 91%) and nutrients (= 86%) was achieved. The average concentrations of nitrogen, phosphates, and COD in the reactor effluent were 95 mgN/L, 49 mg/L, and 735 mg O2/L, respectively. The overall algae-bacteria biomass productivity of 22 mg/L/d, calculated on the total suspended solids (TSS) basis, was recorded. The microbial analysis revealed the dominance of Tetradesmus obliquus followed by Microglena sp. in the first 14 weeks of the experiment. At the end of the experimental run, Chlorella sorokiniana cells appeared as a result of illumination intensity changes. The dominating bacteria belonged to Firmicutes (26.31%), Patescibacteria (17.38%), and Actinobacteriota (14.86%) and could have been responsible for the transformation of nitrogen and oxidation of organic contaminants. The research demonstrated that natural sunlight can successfully be used for efficient liquid digestate treatment with the algae-bacterial community.

Denitrification reaction performance in three-dimensional biofilm electrode reactors using self-releasing carbon biofilm electrodes

Agricultural waste, corncobs, and sponge iron were used to develop a self-releasing carbon biofilm electrode (SRC-BE) and utilized for denitrification experiments in three-dimensional biofilm electrode reactors (3D-BERs). Fe2+ was released to enhance the extracellular electron transfer. Following an alkali treatment, SRC-BE were introduced into 3D-BERs of the electrode group (EG), with iron serving as the anode-cathode plate in the EG. Under a current density of 0.1 mA/cm2, hydraulic retention time of 2 h, and dissolved oxygen control at 0.5–1.0 mg/L, the removal rates were as follows: the highest removal rates were 71.09 % for total nitrogen, 40.26 % for NH4+-N, and 84.34 % for NO3−-N, with a stable 96.87 % removal rate for total phosphate. The effluent chemical oxygen demand in the EG remained stable at approximately 12 mg/L during the stable phase, thus reducing the risk of secondary pollution. The enrichment of electroactive microorganisms and functional genes related to C and N also verified their denitrification efficacy.

Bio-degumming of ramie fiber using an integrated spiral symmetry stream anaerobic bioreactor

The bio-degumming method focused research interest on green degumming technology in the textile sector, addressing environmental pollution, water usage and energy consumption caused by chemical degumming methods while enhancing fiber quality. The application of recent developments in the chemical degumming method of fiber remains a challenge for environmental protection. The novel spiral symmetry stream anaerobic bioreactor was employed in a system that integrates the ramie degumming process and reduces wastewater generation. This study analyzed the effect of liquor ratios on the water quality indicators, fiber quality and microbial community composition. After this study, the effluent chemical oxygen demand concentration, pH and ammonia nitrogen were 264.2 ± 114.2, 7.2 ± 0.1 and 7.4 ± 4.8 mg/L, respectively. This shows a mean chemical oxygen demand removal rate of 55.7 ± 11.8%, which is over 79% higher than previous bio-degumming methods. The fiber quality met the ramie fine dry hemp standard requirements. The high-throughput sequencing results revealed that the microbial community adapted to this process. The relative abundance of dominant microbes ( Bacteroidetes and Methanosaeta), which perform biodegradation and methane production, was present. This study demonstrates a promising green degumming technology and eco-friendly concept for the textile industry.