Effluent Chemical Oxygen Demand Research Articles

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

AbstractThe 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.

Denitrification enhanced by composite carbon sources in AAO- biofilter: Efficiency and metagenomics research

Nitrogen removal from domestic sewage is usually limited by insufficient carbon source and electron donor. An economical solid carbon source was developed by composition of polyvinyl alcohol, sodium alginate, and corncob, which was utilized as external carbon source in the anaerobic anoxic oxic (AAO)-biofilter for the treatment of low carbon-to-nitrogen ratio domestic sewage, and the nitrogen removal was remarkably improved from 63.2% to 96.5%. Furthermore, the effluent chemical oxygen demand maintained at 35 mg/L or even lower, and the total nitrogen was reduced to less than 2 mg/L. Metagenomic analysis demonstrated that the microbial communities responsible for potential denitrification and organic matter degradation in both AAO and the biofilter reactors were mainly composed of Proteobacteria and Bacteroides, respectively. The solid carbon source addition resulted in relatively high abundance of functional enzymes responsible for NO3−-N to NO2−-N conversion in both AAO and the biofilter reactors, thus enabled stable reaction. The carbon source addition during glycolysis primarily led to the increase of genes associated with the metabolic conversion of fructose 1.6P2 to glycerol-3P The reactor maintained high abundance of genes related to the tricarboxylic acid cycle, and then guaranteed efficient carbon metabolism. The results indicate that the composite carbon source is feasible for denitrification enhancement of AAO-biofilter, which contribute to the theoretical foundation for practical nitrogen removal application.

Effective green treatment of sewage sludge from Fenton reactions: Utilizing MoS2 for sustainable resource recovery

Effectively managing sewage sludge from Fenton reactions in an eco-friendly way is vital for Fenton technology's viability in pollution treatment. This study focuses on sewage sludge across various treatment stages, including generation, concentration, dehydration, and landfill, and employs chemical composite MoS2 to facilitate green resource utilization of all types of sludge. MoS2, with exposed Mo4+ and low-coordination sulfur, enhances iron cycling and creates an acidic microenvironment on the sludge surface. The MoS2-modified iron sludge exhibits outstanding (>95%) phenol and pollutant degradation in hydrogen peroxide and peroxymonosulfate-based Fenton systems, unlike unmodified sludge. This modified sludge maintains excellent Fenton activity in various water conditions and with multiple anions, allowing extended phenol degradation for over 14 d. Notably, the generated chemical oxygen demand (COD) in sludge modification process can be efficiently eliminated through the Fenton reaction, ensuring effluent COD compliance and enabling eco-friendly sewage sludge resource utilization.

Study of BOD: COD Ratio as an Indicator for Wastewater of Rubber Industry Sector

When considering the rubber industry sector, the most significant challenge is the efficient wastewater treatment before discharging it into the natural environment. The rubber industry is considered to be highly chemical and water-intensive: and involves enormous quantitative s of waste production. The ratio between Biological Oxygen Demand (BOD5) and Chemical Oxygen Demand (COD) is a widely used parameter in characterising wastewater and then the same was used in this study as an indication to measure the efficiency of wastewater treatment plant operated in the rubber industry sector. Data for the study was extracted from samples collected from 10 wastewater treatment plants of rubber industries in the Sabaragamuwa Province; two samples from each representing before and after treatment. The samples were tested in two replicated for BOD5, COD, pH, and conductivity. These selected rubber factories contain lagoon/pond type wastewater treatment systems or activated sludge wastewater treatment systems. The results showed that the treated wastewater samples were within the range of 0.1–0.4 of BOD5: COD and were below the BOD5: COD permissible level as per CEA standards. (General standards for rubber in inland surface waters; Latex-BOD 60, COD 400; Lanka rubber/crepe rubber–BOD 50, COD 400) comparing the BOD, COD, pH, and conductivity influent and effluent of the treatment process proves the effectiveness of each treatment plant of the relevant industry. Tested samples show a high BOD5: COD ratio in influent and effluent of wastewater treatment system indicate that wastewater of the industry is easily biodegradable. However, more research comparing the characteristics of different rubber manufacturing processes is needed to develop this approach further. 
 Keywords: BOD, COD, BOD: COD ratio, Wastewater treatment plant

Electrochemical Degradation of Textile Effluent Using PbO2 Electrode in Tube Electrolyzer

<p>A commercial PbO<sub>2</sub> mesh cylinder electrode was utilized as the anode for the electrochemical degradation of the textile effluent after the biological treatment with the titanium cylinder as the cathode in a self-made tube electrolyzer. The electrochemical performances of the PbO<sub>2</sub> electrode in tube electrolyzer under different initial pH, electrolyte flow rates, current densities and times of the electrochemical degradation were investigated. The experimental results illustrated that the PbO<sub>2</sub> electrode can reduce the chemical oxygen demand (COD) of the textile effluent from 94.0 mg L<sup>−1</sup> to 65.0 mg L<sup>−1</sup> with the current efficiency of 88.3%, the energy consumption of 27.7 kWh kg<sup>−1</sup> (per kilogram of degraded COD) and the carbon emissions of 18.0 kg CO<sub>2</sub> kg<sup>−1</sup> (per kilogram of degraded COD) under the optimal operating conditions. In addition, the COD of the textile effluent could be reduced from 94.0 mg L<sup>−1</sup> to 22.0 mg L<sup>−1</sup> after the fifth electrochemical degradation. Therefore, PbO<sub>2</sub> mesh cylinder electrode in the tube cylinder was promising for the electrochemical degradation of the textile effluent.</p>

Characteristics of Greenhouse Gas Emissions from Constructed Wetlands Vegetated with Myriophyllum aquatic: The Effects of Influent C/N Ratio and Microbial Responses

This study designed surface flow constructed wetlands (SFCWs) with Myriophyllum aquaticum (M. aquaticum) to evaluate how different influent C/N ratios (0:1 (C0N), 5:1 (C5N), 10:1 (C10N), and 15:1 (C15N)) affect pollutant removal, greenhouse gas (GHG) emissions, and microbial communities. The results showed that effluent ammonia nitrogen (NH4+-N), nitrate nitrogen (NO3−-N), and total nitrogen (TN) concentrations decreased, but effluent chemical oxygen demand (COD) concentration increased with increasing influent C/N ratios. The highest removal rates of TN (73.17%) and COD (74.56%) were observed with C5N. Regarding GHG emissions, a few changes in CO2 fluxes were caused by the influent C/N ratio, whereas CH4 fluxes obviously increased with the increasing influent C/N ratio. The highest N2O emission occurred with C0N (211.03 ± 44.38 mg-N·m−2·h−1), decreasing significantly with higher C/N ratios. High-throughput sequencing revealed that different influent C/N ratios directly influenced the microbial distribution and composition related to CH4 and N2O metabolism in SFCWs. The highest abundance (46.24%) of denitrifying bacteria (DNB) was observed with C5N, which helped to achieve efficient nitrogen removal with a simultaneous reduction in N2O emissions. Methanogen abundance rose with higher C/N ratios, whereas methanotrophs peaked under C5N and C10N conditions. Additionally, the random forest model identified influent C/N ratio and Rhodopseudomonas as primary factors influencing CH4 and N2O emissions, respectively. This highlights the importance of the influent C/N ratio in regulating both pollutant removal and GHG emissions in constructed wetlands.

Prediction of the effluent chemical oxygen demand and volatile fatty acids for anaerobic treatment based on different feature selections machine-learning methods from lab-scale to pilot-scale

The effluent chemical oxygen demand (COD) and volatile fatty acids (VFA) are important indicators for measuring wastewater anaerobic treatment systems. Through lab and pilot experiments on the anaerobic treatment process of high-salt wastewater, combined with different prediction requirements, the feature variables are classified. The effluent-COD and VFA prediction models of five different Machine-learning methods (i.e., back propagation neural network (BPNN), Genetic algorithm optimizes BPNN(GA-BP), support vector machine (SVM), Least squares support vector machine (LSSVM), and random forest (RF)) were established. The modeling results on different dataset (i.e., lab dataset, pilot dataset and mixed dataset) showed that the SVM and LSSVM methods based on statistical learning had the best model prediction performance in lab and pilot datasets, and the RF method performed well in mixed dataset. Furthermore, the genetic algorithm was adopted to optimize the RF model. After optimization of mixed dataset, the effluent-COD prediction (i.e., R2 = 0.966, RMSE/MAE are 61.227/48.584 mg/L) and VFA prediction (i.e., R2 = 0.639, RMSE/MAE are 26.838/21.831 mg/L) have been improved compared with the original RF model (i.e., R2 = 0.918, RMSE/MAE are 86.232/65.03 mg/L of effluent-COD, and R2 = 0.539, RMSE/MAE are 35.489/26.726 mg/L of VFA). This research can provide a reference for the prediction of the anaerobic treatment of actual large-scale industrial wastewater.

Effect of pure oxygen aeration on soluble microbial products production in activated sludge system under the toxic condition of phenol

Soluble microbial products (SMP) contribute most of effluent organic matter (EfOM) in biological treatment processes. These products cause membrane fouling and generate disinfection by-products. This study investigated the effect of pure oxygen aeration on the production and characteristics of SMP under the toxic condition of phenol. Two sequencing batch reactors with pure oxygen (O-SBR) and air (A-SBR) aeration were used in the experiments. Results showed that the effluent chemical oxygen demand values of O- and A-SBR were 36.11 ± 1.54 and 42.44 ± 1.42 mg/L, respectively. SMP was the dominant component of EfOM. The various aeration types caused differences in the contents of extracellular polymeric substances (EPS) and SMP in the two SBRs. The content of loosely bound EPS (LB-EPS) in A-SBR was higher than that in O-SBR by 25%, whereas the tightly bound EPS content in A-SBR was lower than that in O-SBR by 19%. Pure oxygen aeration decreased 15% of humic acids, 42% of polysaccharides, and 29% of protein contents of SMP compared with air aeration, but it did not change the functional groups of SMP. SMP concentration was positively correlated with LB-EPS. Pure oxygen aeration caused the low contents of LB-EPS and SMP. This study showed the effect of pure oxygen aeration on SMP reduction, which can be useful for toxic industrial-wastewater biological treatment.

Optimizing waste molasses utilization to enhance electron transfer via micromagnetic carriers: Mechanisms and high-nitrate wastewater denitrification performance

The biological denitrification of high-nitrate wastewater (HNW) is primarily hindered by insufficient carbon sources and excessive nitrite accumulation. In this study, micromagnetic carriers with varying micromagnetic field (MMF) strengths (0.0, 0.3, 0.6, 0.9 mT) were employed to enhance the denitrification of HNW using waste molasses (WMs) as a carbon source. The results revealed that 0.6 mT MMF significantly improved the total nitrogen removal (TN) efficiency at 96.3%. A high nitrate (NO3−-N) removal efficiency at 99.3% with a low nitrite (NO2−-N) accumulation at 25.5 mg/L was achieved at 0.6 mT MMF. The application of MMF facilitated the synthesis of adenosine triphosphate (ATP) and stimulated denitrifying enzymes (e.g., nitrate reductase (NAR), nitrite reductase (NIR), and nitric oxide reductase (NOR)), which thereby promoting denitrification. Moreover, the effluent chemical oxygen demand (COD), tryptophan and fulvic-like substances exhibited their lowest levels at 0.6 mT MMF. Analysis through 16S ribosomal ribonucleic acid gene sequencing indicated a significant enrichment of denitrifying bacteria including Castellaniella Klebsiella under the influence of MMF. Besides, the proliferation of Acholeplasma, Klebsiella and Proteiniphilum at 0.6 mT MMF promoted the hydrolysis and acidification of WMs. This study offers new insights into the enhanced utilization of WMs and the denitrification of HNW through the application of MMF.

Simultaneous Phycoremediation and Lipid Production by Microalgae Grown in Non-Sterilized and Sterilized Anaerobically Digested Brewery Effluent

Microalgae have the ability to utilize nutrients present in wastewater and generate biomass that is abundant in carbohydrates, lipids, and proteins. The ability of microalgae to integrate wastewater management and biofuel production makes them a promising solution for enhancing environmental sustainability. The objective of this study was to assess the potential of local microalgae, Scenedesmus sp., to simultaneously remediate wastewater and produce lipids. The microalgae were cultivated in anaerobically digested brewery effluent, both sterilized and non-sterilized, to evaluate their phycoremediation and lipid production capabilities. The phycoremediation study was investigated by measuring chemical oxygen demand (COD), total nitrogen (TN), ammonium–nitrogen (NH4+-N), and total phosphorus (TP) removal from brewery effluent. Lipids were extracted from microalgal biomass without and with pretreatment methods, such as microwave, autoclave, osmotic stress, oven heating, and HCl digestion in a water bath, to enhance lipid extraction. Results indicate that Scenedesmus sp. achieves higher biomass production in non-sterilized brewery effluent compared to sterilized brewery effluent. Conversely, it attains higher lipid accumulation in sterilized brewery effluent compared to non-sterilized brewery effluent. Scenedesmus sp. also attained a higher removal of TP (69.32%) and COD (77.78%) in non-sterilized effluent, but TN (96.14%) in sterilized brewery effluent. The removal of NH4+-N was nearly 100% in both effluents. The maximum lipid content obtained was 14.79%, which was enhanced by 39.06%, 23.89%, 15.81%, 11.61%, and 4.78% after microwave, HCl digestion, autoclave, osmotic, and oven heating pretreatments, respectively. The findings of this study demonstrate that local microalgae have a great potential for wastewater remediation with lipid production using appropriate pretreatment methods.

Modeling Method for Aerobic Zone of A2O Based on KPCA-PSO-SCN

Sewage treatment plants face significant problems as a result of the annual growth in urban sewage discharge. Substandard sewage discharge can also be caused by rising sewage treatment expenses and unpredictable procedures. The most widely used sewage treatment process in urban areas is the Anaerobic–Anoxic–Oxic (A2O) sewage treatment process. Therefore, modeling the sewage treatment process and predicting the effluent quality are of great significance. A process modeling method based on Kernel Principal Component Analysis–Particle Swarm Optimization–Stochastic Configuration Network (KPCA-PSO-SCN) is proposed for the A2O aerobic wastewater treatment process. Firstly, eight auxiliary variables were determined through mechanism analysis, including Chemical Oxygen Demand (COD) and ammonia nitrogen (NH4+) and nitrate nitrogen (NO3−) of influent water, pH, temperature (T), Mixed Liquor Suspended Solid (MLSS), Dissolved Oxygen (DO) and hydraulic residence time (HRT) in the aerobic zone. Dimensionality reduction was carried out using the kernel principal component analysis method based on the Gaussian function, and the eight-dimensional data were changed to five-dimensional data, which improved the running speed and efficiency of subsequent models. Then, according to the advantages of the particle swarm optimization algorithm, such as low calculation cost and fast convergence, combined with the advantages of stochastic configuration network general approximation performance, the PSO-SCN model was established to predict the three water quality indexes of effluent COD, NH4+, and NO3− for the aerobic zone. The experimental results proved the effectiveness of the model. Compared with classic water quality prediction algorithm models such as SCN, PSO-BP, RBF, PSO-RBF, etc., the superiority of the PSO-SCN algorithm model was demonstrated.

Multiple-layer statistical methodology for developing data-driven models of anaerobic digestion process

When modelling anaerobic digestion, ineffective data handling and inadequate designation of modelling parameters can undermine the model reliability. In this study, a multilayer statistical technique, which employed a machine learning technique using regression models, was introduced to systematically support the development of anaerobic digestion models. Layer-by-layer statistical techniques including cubic smoothing splines (missing data reconstruction), principal component analysis (identifying correlated parameters), analysis of variance (analysing differences among datasets), and linear regression (developing data-driven models) were used to develop and validate anaerobic digestion models. Experimental data collected from the long-term operation of lab-scale (operated for 350 days), pilot-scale (operated for 150 days), and full-scale reactors (operated for 750 days) were used to demonstrate the modelling process. The multivariate models based on a data-driven modelling technique were developed by subjecting the experimental and monitored data to a modelling process. The developed models could predict the biogas production and effluent chemical oxygen demand during anaerobic digestion. Statistical analyses verified the modelling hypotheses, evaded invalid model development, and ensured data integrity and parameter validity. Multiple linear regression of principal components demonstrated that the performance of biogas production using food waste was influenced by the variances of the nitrogen and organic concentrations, but not by the chemical oxygen demand to total nitrogen (C/N) ratio. In the validation process, the model developed with lab-scale reactor data showed relatively high accuracy with R2, SSE, and RMSE values of 0.86, 34.45, and 0.72.

Oily wastewater treatment by a continuous flow electrocoagulation reactor with polarity switch: Assessment of the relation between process variables and the aluminum released to the environment

Electrocoagulation with electrical polarity inversion was used to treat oil in water emulsions (145 ± 5 mg dm−3) using a cylindrical 4.8 dm3 reactor in continuous mode. The effects of spatial time and time between polarity inversion were explored using a three-level full factorial design (32), followed by Spearman correlation (ps), which has shown that the aluminum concentration in the treated effluent is not directly dependent on the mass of aluminum released by the electrodes. Nonetheless, the loss of mass of the electrodes is correlated (ps = 0.6970) to oil removal and to less electric power consumption (ps = −0.6909). Surface response analysis revealed that increasing the number of inversion cycles reduces electrode degradation. The treatment reduced the effluent's chemical oxygen demand by over 92.8%. Regarding environmental impact, there is an inverse statistical correlation between aluminum in the treated effluent and oil removal (ps = −0.7426), indicating that removing more oil with less environmental impact is possible. The better condition, considering oil removal and lower electrode consumption, was obtained with a spatial time of 36 min and a polarity inversion time of 10 s; for this condition, oil removal reached 87.0% with an energy expenditure of about 7.21 kW h.m−3.

Experimental study of microwave-catalytic oxidative degradation of COD in livestock farming effluent by copper-loaded activated carbon

ABSTRACT The problem of massive discharge of livestock wastewater is becoming more and more severe, causing irreversible damage to the ecological environment, and how to treat livestock wastewater efficiently and rapidly deserves to be studied in depth. In this work, CuO/granular activated carbon (GAC) loaded catalysts were prepared and characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), nitrogen adsorption/desorption techniques, and X-ray energy spectroscopy (EDS). The results showed that CuO was successfully attached to the GAC surface with good adsorption performance. The effects of catalyst dosage, H2O2 dosage, initial pH, microwave power and microwave irradiation time in different reaction systems on the degradation efficiency of chemical oxygen demand (COD) in wastewater were investigated, and the orthogonal experiments were used to explore the importance ranking of these factors. The highest degradation rate of COD was found to be enhanced by 12.1% in the reaction system of CuO/GAC, and the initial pH had the greatest effect on the COD removal rate. The combined MW/catalyst/H2O2 method used in this work provided a rapid and effective degradation of COD in wastewater, which can be helpful for reference in other microwave catalytic oxidation studies.

Reduction of soluble microbial products in activated sludge system with pure oxygen aeration under toxic stress condition of phenol

<p>This study aimed to explore the effect of pure oxygen aeration on the generation of soluble microbial products (SMP) when activated sludge was under toxic stress condition of phenol. Two parallel sequencing batch reactors, one with air and the other with pure oxygen aeration, were used to investigate the relationships among effluent chemical oxygen demand (COD), N-acyl-homoserine lactones (AHLs), microbial community, extracellular polymeric substances (EPS), and SMP. The results showed that pure oxygen aeration caused rapid recovery of the activated sludge and low effluent COD concentration in steady condition when the two reactors were fed with phenol. In contrast, these results were not observed with sodium acetate as substrate. A strong correlation was found among C4-HSL, loosely bound EPS (LB-EPS), and SMP. The genera Zoogloea, Bacteroides, and Flavobacterium were also related to effluent COD, SMP, and LB-EPS production under toxic stress condition of phenol. Pure oxygen aeration reduced AHL production and decreased the relative abundance of specific genera (Zoogloea and Flavobacterium). These changes caused the reduction of SMP generation in the pure oxygen-aerated activated sludge systems. These results provide insights into the effect of pure oxygen aeration on SMP generation of activated sludge with toxic substance existence.</p>

Integration of Membrane Bioreactor and Reverse Osmosis for Textile Wastewater Treatment and Reclamation: A Pilot-Scale Study

Membrane bioreactor (MBR) technology, a combination of traditional activated sludge and membrane filtration, has been widely used for industrial wastewater treatment and reclamation. This paper highlights a pilot-scale MBR system treating textile wastewater from a textile factory in Taiwan. Over 7 months of continuous operation, the average MBR influent chemical oxygen demand (COD) is 332 mg/L, and the average effluent COD is 38 mg/L, which results in approximately 88% COD removal. A reverse osmosis (RO) module is installed after 2 months of MBR operation and uses the MBR permeate as its influent. The RO produces pure water with average COD, conductivity, and color of 7 mg/L, 16 μS/cm, and 7 Pt-Co, respectively. The RO permeate is suitable for reuse in manufacturing processes, and the RO membrane shows stable performance with TMP, which is less than or equal to 0.5 kg/cm2 during the test. The study demonstrates the great feasibility of MBR combined with RO for treating and reclaiming textile wastewater.