Sewage Treatment Process Research Articles

Analysis of sewage treatment process and effect Take Maidao sewage plant in Qingdao as an example

The purpose of this paper is to analyze the current situation of urban sewage treatment, understand the process of sewage treatment, conduct in-depth analysis of it combined with actual cases, and put forward effective suggestions for improvement. Nowadays, with the development of science and technology, many cities have built advanced sewage plants, as an example as Qingdao, which is a serious water shortage city in Shandong Province of China. More specifically, a field interview and a field trip in Mai Dao sewage plant provide the actual treatment process and real data of the plant. It provides with a comprehensive process and actual removal rate of the plant, so we can have a deeper understanding of the current status of urban sewage treatment processes. Additionally, an analysis of the effect of every devices in the process supports the effectiveness of the plant. Furthermore, this paper gives information about current situations of reuse water process and offers some suggestions to the current treatment process and the use of outflow water.

Transport and risk of airborne pathogenic microorganisms in the process of decentralized sewage discharge and treatment

Sewage treatment processes are a critical anthropogenic source of bioaerosols and may present significant health risks to plant workers. Compared with the specialization and scale of urban sewage treatment, many decentralized treatment models are flexible and extensive. These treatment facilities are usually close to residential areas owing to the pipe network layout and other restrictions. Bioaerosols generated by these facilities may present a serious and widespread occupational and non-occupational exposure risk to nearby residents, particularly the elderly and children. An understanding of the characteristics and exposure risks of bioaerosols produced during decentralized sewage treatment is lacking. We compared bioaerosol emission characteristics and potential exposure risks under four decentralized sewage discharge methods and treatment models: small container collection (SCC), open-channel discharge (OCD), single household/combined treatment (SHCT), and centralized treatment (CT) in northwest China. The OCD mode had the highest bioaerosol production, whereas the CT mode had the lowest. The OCD model contained the most pathogenic bacterial species, up to 43 species, including Sphingomonas, Pseudomonas, Cladosporium, and Alternaria. Risk assessments indicated bioaerosol exposure was lower in the models with sewage treatment (SHCT and CT) than in those without (SCC and OCD). Different populations exhibited large variations in potential risks owing to differences in time spent indoors and outdoors. The highest risk was observed in males exposed to the SCC model. This study provides a theoretical basis and theories for the future joint prevention and control of the bioaerosol exposure risk from decentralized sewage treatment.

Research Status on Co-pyrolysis of Municipal Sludge and Biomass

Municipal sludge is a by-product in the sewage treatment process, and its main properties include high moisture content, containing organic toxic substances, large specific surface area and porosity, and easy to produce irritating gases. Biomass is a renewable energy source, including wood, straw, rice husks, sawdust, etc. This article mainly takes garden waste as an example. The co-pyrolysis of municipal sludge and garden waste is of practical significance for the reduction and resource utilization of both. This article summarizes the development and application of co-pyrolysis of municipal sludge and garden waste.

Designing the Spigot Structure of Hydrocyclones to Reduce Fine Particle Misplacement in Underflow

Hydrocyclones can be used to concentrate the entrained sands in sewage and alleviate the clogging and erosion of the drainage network, but in practical application, there are problems such as low concentrations of underflow and a high content of fine particles, which cause a significant load on the subsequent sand dewatering and recycling. This paper designs five spigot structures of hydrocyclones and investigates the separation performance by numerical simulation, aiming to improve the applicability of hydrocyclones in the sewage treatment process by optimizing the spigot structure. The research results show that a large cone spigot delays the external downward swirling flow and reduces fine particle content in the underflow, but its effective separation space is reduced, and the turbulence in the cone section area is more intensive, which influences the separation accuracy. An elongated spigot has a reduced underflow water distribution; fine particles are more enriched in the internal swirling flow, and the underflow recoveries of 1 μm and 5 μm particles drop by 2.34% and 2.31%. The spigot structure affects the downward fluid and air intake states; complicated spigot structures contribute to increasing the resistance of particle discharge through underflow, alleviating fine particle misplacement.

The Domestic Wastewater Treatment Installation's Performance Study of Technical Aspects in Cahaya Abadi Housing, Palembang City

<p><em>The comprehensive treatment of rural water pollution was pioneered by developed countries, and many nations now have a variety of sewage treatment facilities and process technology that are suitable for their rural features. Domestic sewage is handled differently at sewage treatment facilities in cities than it is in rural areas. Its distributed drainage sites make it difficult to collect. The Indonesian government is implementing domestic wastewater treatment (DWWT) to reduce water pollution in areas without separate residential waste processing. The results of this analysis show that an extensive amount of maintenance is needed to ensure that the DWWT and its distribution system run as efficiently as possible. The Cahaya Abadi Housing in Sri Mulya Village, Sematang Borang District, Palembang City, is where the study is being carried out. DWWT Cahaya Abadi processes domestic liquid waste from household activities, although even though the facility has room for 169 dwellings, only 31 homes use it. As a result, capacity is not being used. The results of identifying the technical factors show that the use of pumps results in differences in flow discharge that affect the outcomes of the DWWT output. The extremely rapid pump that is being used has an impact on the processing process and the current detention time.</em></p>

Deciphering the removal of antibiotics and the antibiotic resistome from typical hospital wastewater treatment systems

Hospital wastewater treatment systems (HWTSs) are a significant source and reservoir of antibiotic resistance genes (ARGs) and a crucial hub for transmitting ARGs from clinical to natural environments. However, there is a lack of research on the antibiotic resistome of clinical wastewater in HWTSs. In this study, we used metagenomics to analyze the prevalence and abundance of ARGs in five typical HWTSs. A total of 17 antibiotics from six categories were detected in the five HWTSs; β-lactam antibiotics were found at the highest concentrations, with up to 4074.08 ng·L−1. We further found a total of 21 ARG types and 1106 subtypes of ARGs with the highest percentage of multi-drug resistance genes (evgS, msbA, arlS, and baeS). The most abundant last-resort ARGs were mcr, which were detected in 100 % of the samples. HWTSs effluent is a major pathway for the transmission of last-resort ARGs into urban wastewater networks. The removal of antibiotics, antibiotic-resistant bacteria, and ARGs from HWTSs was mainly achieved by tertiary treatment, i.e., chlorine disinfection, but antibiotics and ARGs were still present in the HWTSs effluent or even increased after treatment. Moreover, antibiotics and heavy metals (especially mercury) in hospital effluents can exert selective pressure for antibiotic resistance, even at low concentrations. Qualitative analyses based on metagenome-assembled genome analysis revealed that the putative hosts of the identified ARGs are widely distributed among Pseudomonas, Acidovorax, Flavobacterium, Polaromonas, and Arcobacter. Moreover, we further assessed the clinical availability of ARGs and found that multidrug ARGs had the highest clinical relevance values. This study provides new impulses for monitoring and removing antibiotics and ARGs in the hospital sewage treatment process.

Change characteristics, bacteria host, and spread risks of bioaerosol ARGs/MGEs from different stages in sewage and sludge treatment process

The spread of antibiotic resistance genes (ARGs) in the atmospheric environment has seriously threatened human health. Wastewater treatment plants (WWTPs) are an important source of aerosol ARGs. A large WWTP, including sewage treatment process (SWP) and sludge treatment process (SDP), was selected in North China for sampling in this study. The content of ARGs, mobile genetic elements (MGEs), and bacterial genera in sewage/sludge and aerosols from different process stages was detected. The possible correlation between ARGs/ MGEs and bacteria was analyzed. The risk of antibiotic-resistant bacteria was evaluated and the diffusion of ARGs/MGEs was simulated. The results showed that the concentration of ARGs/MGEs varied as the process progressed, and which in the aeration tank was relatively high. The ARGs/MGEs content in SWP aerosol (8.35–163.27 copies/m3) was higher than that in SDP (5.52–16.36 copies/m3). The main ARGs/MGEs detected in SWP aerosol were tnpA-05, tnpA-04, and ermF, while the main ARGs/MGEs detected in SDP aerosol were sul1, ermF, and blaPAO. ARGs were positively correlated with most bacteria and Escherichia coli with ARGs carries higher cytotoxicity. ARGs/MGEs mainly diffused towards the southeast, which may cause harm to urban residents with the diffusion of aerosols. This study provides clues and theoretical basis for preventing the hazards of ARGs from WWTP sources.

Insight into microbial adaptability in continuous flow anaerobic ammonium oxidation process for low-strength sewage treatment

Organic matter concentration is a critical factor influencing the adaptability of anaerobic ammonium oxidation (anammox) bacteria to low-strength sewage treatment. To address this challenge and achieve stable anammox activity, a micro-aeration partial nitrification-anammox process was developed for continuous-flow municipal sewage treatment. Under limited ammonium conditions, the effective utilization of organics in denitrification promoted the stable accumulation of nitrite and enhanced anammox activity. This, in turn, led to enhanced nitrogen removal efficiency, reaching approximately 87.7%. During the start-up phase, the protein content of extracellular polymeric substances (EPS) increased. This enhanced EPS intensified the inhibitory effect of denitrifying bacteria (DNB) on nitrite-oxidizing bacteria through competition for nitrite, thereby facilitating the proliferation of anammox bacteria (AnAOB). Additionally, several types of DNB capable of utilizing slowly biodegradable organics contributed to the adaptability of AnAOB. These findings provide valuable insights for ensuring efficient anammox performance and robust nitrogen removal in the treatment of low-strength sewage.

Unlocking the paracetamol adsorption mechanism in graphene tridimensional-based materials: an experimental-theoretical approach

The omnipresence of emerging contaminants in the aquatic environment is indisputable. These contaminants include chemical substances not removed in traditional water and sewage treatment processes. To ensure the quality of water and healthy aquatic ecosystems, new treatment technologies and materials are essential to effectively control the presence of these contaminants in the aquatic environment. More than that, it is important to know how molecules interact with these new materials. A low-cost alternative currently available is adsorption. Despite this method being widely studied, describing the interaction mechanisms between the materials and the analytes is not usual, limiting the obtainment of more efficient materials. Thus, the objective of this work was to understand, in a theoretical-experimental way, the forms of interaction in the adsorption of the drug paracetamol, widely used worldwide, in materials based on graphene with different chemical and structural properties. For this, kinetic and isothermal experimental studies were carried out using four materials that contemplated different dimensions, pore sizes, and oxidation degrees. In theoretical studies, density functional theory (DFT) simulations were performed to cover quantum details, revealing how paracetamol interacts with different graphene structures. According to theoretical studies, binding energies, binding distances, and charge transfer between oxidized graphene and paracetamol drug are compatible with physical adsorption, strongly dependent on the type and number of functional groups on the graphene surface. These results agree with the experimental data where the highest adsorptions were observed precisely for materials containing a higher proportion of functional groups and where these groups are more available (more porous), with adsorptive capacities reaching 235.7 mg/g. Our findings contribute to scientific knowledge about using graphene structures as an adsorbent material, providing a solid basis for future studies and developing more efficient and advanced water treatment technologies.

Exploratory Research on the Use of Energy Recovery Technology in Sewage Treatment in Indonesia

This research delves into the integration of Energy Recovery Technology (ERT) within sewage treatment processes in Indonesia, addressing the imperative for sustainable wastewater management amid rapid population growth and urbanization. Through a comprehensive investigation encompassing technological, economic, and environmental dimensions, the study identifies challenges, evaluates the feasibility of ERT integration, and bridges critical gaps in the existing literature. The potential synergy between wastewater treatment and energy recovery aligns with Indonesia's commitment to sustainable development. The findings serve as a catalyst for innovative sewage treatment approaches, contributing to a paradigm shift in Indonesia's wastewater management practices. The accompanying bibliometric analysis provides a detailed overview of the academic landscape, key contributors, and impactful studies, enhancing the study's depth and practical implications.

Evaluation of Field Applicability of Sewage Treated with an Electrochemical Floatation System Coupled with a Separation Membrane Process

An electrochemical floatation system (EFS)-based sewage-treatment process that applied a dimensionally stable anode (DSA) was developed to secure treated sewage water from sewage-treatment plants for reuse. The DSA was fabricated at a temperature of 673–923 K by dispersing a low quantity of activated carbon on a titanium plate coated with mixed metal oxides (Pt, Re, Pd, Re, and Ir) to a thickness of 5 μm. The average size of the bubbles generated through the DSA of the EFS was 20–40 μm, thus confirming microbubble generation. An efficiency assessment of a titanium-based DSA confirmed metal oxide activity through the removal of Escherichia coli (100%) and total organic carbon (TOC; 48.83%) at a reaction time shorter than 10 min and a low current density (19.6 A/m2). During the long-term operation of the EFS with the separation membrane process, the average removal efficiency was 94.7%, 90.0%, 96.1%, 90.9%, and 98.0% for suspended solids, biochemical oxygen demand, TOC, total nitrogen, and total phosphorous, respectively. Coliforms were not detected. Overall, our EFS coupled with the separation membrane process produced high-quality recycled water that could be used for various purposes according to the water-quality standards for different applications.

Iron and Molybdenum Isotope Application for Tracing Industrial Contamination in a Highly Polluted River

Rivers adjacent to industrial zones usually suffer from severe pollution issues. Industrial wastewater that has undergone sewage treatment processes may be legally discharged into rivers under water quality permits. Previous studies have frequently employed isotopic tracers to identify potential contaminants for pollution control. Conventional radiogenic isotopes utilized in tracing studies cannot discern whether the source is untreated (primary) industrial wastewater, which can have serious impact to the environment. By analyzing the iron (Fe) and molybdenum (Mo) isotopic compositions in original industrial wastewater and treated effluent, this study aims to investigate whether the heavily polluted Agongdian River is contaminated by the untreated wastewater. Based on the results from this study, the original industrial wastewater exhibits higher concentrations of metallic elements and heavier Fe and lighter Mo isotopic compositions, compared to the treated effluent. Consequently, it appears that Agongdian River water indeed exhibits evidence of untreated industrial wastewater. Furthermore, the volume of original industrial wastewater entering the river can be estimated from these results. This research offers a more precise and accurate approach to monitor potential industrial wastewater pollution in natural water bodies, contributing to the goal of environmental protection and sustainable development.

Degradation kinetics of organic matter in domestic sewage by sequencing batch reactor

The sequencing batch reactor (SBR) has become a popular sewage treatment process in various industries due to its strong impact load resistance and numerous other advantages. This study examines the variations of chemical oxygen demand chromium (CODcr), ammoniacal nitrogen (NH3-N), total bound nitrogen (TN) and total phosphorus (TP) with aeration time to investigate the degradation patterns of different pollutants during the SBR’s reaction stage. Additionally, an organic matter degradation kinetic model was developed. The experimental results showed that the CODcr and NH3-N in the reactor met the national discharge standards after two hours of influent. Furthermore, the removal rates of CODcr and NH3-N exceeded 85% and 99%, respectively, at the end of the final aeration. At the same time, the TN and TP removal rates remained stable at approximately 45% and 68%. The analysis of the kinetic model showed that the degradation process of organic matter followed a first-order kinetic equation: S=S0e−0.00012Xt. The regression model's fitting value exhibited a high degree of conformity with CODcr removal’s experimental results, with the average relative error of 5.52% falling within an acceptable range. Furthermore, the correlation coefficient (0.9966) indicated high accuracy and effectively represented the changes in organic matter throughout the SBR reaction stage. Consequently, this model can serve as a theoretical foundation for determining CODcr’s maximum treatment capacity in experimental wastewater.

Current Situation and Prospect in Urban Sewage Treatment Plants

Sewage is the carrier of resources and energy, which contains great chemical energy and heat energy. The traditional sewage treatment process consumes energy, and the energy consumption is high. Under the background of carbon neutrality, China’s urban sewage treatment plants have achieved carbon neutrality operation in terms of energy self-sufficiency and reduction of greenhouse gas emissions. To explore the feasibility of carbon neutrality in sewage treatment plants, the cases of carbon neutrality in sewage treatment plants at domestic and abroad were analyzed. And the implementation approaches and main emission reduction processes to achieve carbon neutrality were summarized. The technical challenges and research directions of future sewage treatment plants in China from the perspective of carbon neutrality are proposed.

Enhancing sewage flow prediction using an integrated improved SSA-CNN-Transformer-BiLSTM model

<p>Accurate prediction of sewage flow is crucial for optimizing sewage treatment processes, cutting down energy consumption, and reducing pollution incidents. Current prediction models, including traditional statistical models and machine learning models, have limited performance when handling nonlinear and high-noise data. Although deep learning models excel in time series prediction, they still face challenges such as computational complexity, overfitting, and poor performance in practical applications. Accordingly, this study proposed a combined prediction model based on an improved sparrow search algorithm (SSA), convolutional neural network (CNN), transformer, and bidirectional long short-term memory network (BiLSTM) for sewage flow prediction. Specifically, the CNN part was responsible for extracting local features from the time series, the Transformer part captured global dependencies using the attention mechanism, and the BiLSTM part performed deep temporal processing of the features. The improved SSA algorithm optimized the model's hyperparameters to improve prediction accuracy and generalization capability. The proposed model was validated on a sewage flow dataset from an actual sewage treatment plant. Experimental results showed that the introduced Transformer mechanism significantly enhanced the ability to handle long time series data, and an improved SSA algorithm effectively optimized the hyperparameter selection, improving the model's prediction accuracy and training efficiency. After introducing an improved SSA, CNN, and Transformer modules, the prediction model's $ {R^{\text{2}}} $ increased by 0.18744, $ RMSE $ (root mean square error) decreased by 114.93, and $ MAE $ (mean absolute error) decreased by 86.67. The difference between the predicted peak/trough flow and monitored peak/trough flow was within 3.6% and the predicted peak/trough flow appearance time was within 2.5 minutes away from the monitored peak/trough flow time. By employing a multi-model fusion approach, this study achieved efficient and accurate sewage flow prediction, highlighting the potential and application prospects of the model in the field of sewage treatment.</p>

Enhancing sewage flow prediction using an integrated improved SSA-CNN-Transformer-BiLSTM model

<p>Accurate prediction of sewage flow is crucial for optimizing sewage treatment processes, cutting down energy consumption, and reducing pollution incidents. Current prediction models, including traditional statistical models and machine learning models, have limited performance when handling nonlinear and high-noise data. Although deep learning models excel in time series prediction, they still face challenges such as computational complexity, overfitting, and poor performance in practical applications. Accordingly, this study proposed a combined prediction model based on an improved sparrow search algorithm (SSA), convolutional neural network (CNN), transformer, and bidirectional long short-term memory network (BiLSTM) for sewage flow prediction. Specifically, the CNN part was responsible for extracting local features from the time series, the Transformer part captured global dependencies using the attention mechanism, and the BiLSTM part performed deep temporal processing of the features. The improved SSA algorithm optimized the model's hyperparameters to improve prediction accuracy and generalization capability. The proposed model was validated on a sewage flow dataset from an actual sewage treatment plant. Experimental results showed that the introduced Transformer mechanism significantly enhanced the ability to handle long time series data, and an improved SSA algorithm effectively optimized the hyperparameter selection, improving the model's prediction accuracy and training efficiency. After introducing an improved SSA, CNN, and Transformer modules, the prediction model's $ {R^{\text{2}}} $ increased by 0.18744, $ RMSE $ (root mean square error) decreased by 114.93, and $ MAE $ (mean absolute error) decreased by 86.67. The difference between the predicted peak/trough flow and monitored peak/trough flow was within 3.6% and the predicted peak/trough flow appearance time was within 2.5 minutes away from the monitored peak/trough flow time. By employing a multi-model fusion approach, this study achieved efficient and accurate sewage flow prediction, highlighting the potential and application prospects of the model in the field of sewage treatment.</p>

Ofloxacin weakened treatment performance of rural domestic sewage in an aerobic biofilm system by affecting biofilm resistance, bacterial community, and functional genes

Ofloxacin (OFL) is a typical fluoroquinolone antibiotic widely detected in rural domestic sewage, however, its effects on the performance of aerobic biofilm systems during sewage treatment process remain poorly understood. We carried out an aerobic biofilm experiment to explore how the OFL with different concentrations affects the pollutant removal efficiency of rural domestic sewage. Results demonstrated that the OFL negatively affected pollutant removal in aerobic biofilm systems. High OFL levels resulted in a decrease in removal efficiency: 9.33% for chemical oxygen demand (COD), 18.57% for ammonium (NH4+-N), and 8.49% for total phosphorus (TP) after 35 days. The findings related to the chemical and biological properties of the biofilm revealed that the OFL exposure triggered oxidative stress and SOS responses, decreased the live cell number and extracellular polymeric substance content of biofilm, and altered bacterial community composition. More specifically, the relative abundance of key genera linked to COD (e.g., Rhodobacter), NH4+-N (e.g., Nitrosomonas), and TP (e.g., Dechlorimonas) removal was decreased. Such the OFL-induced decrease of these genera might result in the down-regulation of carbon degradation (amyA), ammonia oxidation (hao), and phosphorus adsorption (ppx) functional genes. The conventional pollutants (COD, NH4+-N, and TP) removal was directly affected by biofilm resistance, functional genes, and bacterial community under OFL exposure, and the bacterial community played a more dominant role based on partial least-squares path model analysis. These findings will provide valuable insights into understanding how antibiotics impact the performance of aerobic biofilm systems during rural domestic sewage treatment.

A hybrid deep learning approach to improve real-time effluent quality prediction in wastewater treatment plant

Wastewater treatment plant (WWTP) operation is usually intricate due to large variations in influent characteristics and nonlinear sewage treatment processes. Effective modeling of WWTP effluent water quality can provide valuable decision-making support to facilitate their operations and management. In this study, we developed a novel hybrid deep learning model by combining the temporal convolutional network (TCN) model with the long short-term memory (LSTM) network model to improve the simulation of hourly total nitrogen (TN) concentration in WWTP effluent. The developed model was tested in a WWTP in Jiangsu Province, China, where the prediction results of the hybrid TCN-LSTM model were compared with those of single deep learning models (TCN and LSTM) and traditional machine learning model (feedforward neural network, FFNN). The hybrid TCN-LSTM model could achieve 33.1 % higher accuracy as compared to the single TCN or LSTM model, and its performance could improve by 63.6 % comparing to the traditional FFNN model. The developed hybrid model also exhibited a higher power prediction of WWTP effluent TN for the next multiple time steps within eight hours, as compared to the standalone TCN, LSTM, and FFNN models. Finally, employing model interpretation approach of Shapley additive explanation to identify the key parameters influencing the behavior of WWTP effluent water quality, it was found that removing variables that did not contribute to the model output could further improve modeling efficiency while optimizing monitoring and management strategies.

Effluent quality prediction of the sewage treatment based on a hybrid neural network model: Comparison and application

The accurate prediction and assessment of effluent quality in wastewater treatment plants (WWTPs) are paramount for the efficacy of sewage treatment processes. Neural network models have exhibited promise in enhancing prediction accuracy by simulating and analyzing diverse influent parameters. In this study, a back propagation neural network hybrid model based on a tent chaotic map and sparrow search algorithm (Tent_BP_SSA) was developed to predict the effluent quality of sewage treatment processes. The prediction performance of the propose hybrid model was compared with traditional neural network models using five performance indicators (MAE, RMSE, SSE, MAPE and R2). Specifically, in comparison with the prior Tent_BP_SSA, Tent_BP_SSA2 demonstrated notable enhancements, with the R2 increasing from 0.9512 to 0.9672, while MAE, RMSE, SSE, and MAPE decreased by 9.62%, 18.84%, 24.80%, and 47.10%, respectively. These indicators collectively affirm that the utilization of higher-order input parameters ensures improved accuracy of the Tent_BP_SSA2 hybrid model in predicting effluent quality. Moreover, the Tent_BP_SSA2 model exhibited robust prediction ability (R2 of 0.9246) when applied to assess the effluent quality of an actual sewage treatment plant. The incorporation of integrated models comprising the sparrow search optimizing algorithm, tent chaotic mapping, and higher-order magnitude decomposition of input parameters has demonstrated the capacity to enhance the accuracy of effluent quality prediction. This study illuminates novel perspectives on the prediction of effluent quality and the assessment of effluent warnings in WWTPs.

A research on the strengthening effect of sludge charcoal on activated sludge process in sewage treatment.

With the rapid development of urbanization, the number of urban sewage treatment plants is increasing, wastewater treatment volume is gradually becoming large, and correspondingly, the sludge production capacity has a rapid growth. As a new method of sludge disposal, sludge carbonization is characterized by low energy consumption, simple products, and wide resource utilization prospects, which is of great help to solve problems of current sludge disposal in China. The residual sludge from sewage plant was used as raw material in this study in order to investigate the physical and chemical properties of sludge charcoal after high temperature carbonization and explore the enhancement in the removal of pollutants including CODcr, NH3-N, TN, and TP during sewage treatment with the used sludge charcoal. The results show that the optimal dosing amount of sludge charcoal was 2g.L-1 when it was added into SBR equipment at one time, while the optimal dosing amount is 0.06g.L-1 when it was added into SBR equipment with each influent process. The enhanced removal effect of pollutants in sewage treatment process mainly depended on the physical adsorption and intensified bio-degradation of sludge charcoal, and activated sludge and sludge charcoal were synergistic in water treatment. The removal effect of pollutants is strengthened in the physical adsorption-bio-degradation-sludge charcoal reproduction-re-adsorption system. These suggested that sludge charcoal could be promising for the enhancement of pollutant removal in sewage through activated sludge process.