Effect of surfactant frequently used in soil flushing on oxygen mass transfer in micro-nano-bubble aeration system

In-site soil flushing and aeration are the typical synergetic remediation technology for contaminated sites.The surfactant present in flushing solutions is bound to affect the aeration efficiency.The purpose of this study is to evaluate the effect of surfactant frequently used in soil flushing on the oxygen mass transfer in micro-nano-bubble(MNB)aeration system.Firstly,bio-surfactants and chemical surfactants were used to investigate their effects on Sauter mean diameter of bubble(dBS),gas holdup(ε),volumetric mass-transfer coefficient(kLa)and liquid-side mass-transfer coefficient(kL)in the MNB aeration system.Then,based upon the experimental results,the Sardeing's and Frossling's models were modified to describe the effect of surfactant on kL in the MNB aeration.The results showed that,for the twenty aqueous surfactant solutions,with the increase in surfactant concentration,the value of dBS,kLa and kL decreased,while the value ofεand gas-liquid interfacial area(a)increased.These phenomena were mainly attributed to the synergistic effects of immobile bubble surface and the suppression of coalescence in the surfactant solutions.In addition,with the presence of electric charge,MNBs in anionic surfactant solutions were smaller and higher in number than in non-ionic surfactant solutions.Furthermore,the accumulation of surfactant on the gas-liquid interface was more conspicuous for small MNB,so the reduction of kL in anionic surfactant solutions was larger than that in non-ionic surfactant solutions.Besides,the modified Frossling's model predicted the effect of surfactant on kL in MNB aeration system with reasonable accuracy.

Effect of bubble morphology and behavior on power consumption in non-Newtonian fluids’aeration process

Due to a prolonged operation time and low mass transfer efficiency, the primary challenge in the aeration process of non-Newtonian fluids is the high energy consumption, which is closely related to the form and rate of impeller, ventilation, rheological properties and bubble morphology in the reactor. In this perspective, through optimal computational fluid dynamics models and experiments, the relationship between power consumption, volumetric mass transfer rate(kLa) and initial bubble size(d0) was constructed to establish an efficient operation mode for the aeration process of non-Newtonian fluids. It was found that reducing the d0could significantly increase the oxygen mass transfer rate, resulting in an obvious decrease in the ventilation volume and impeller speed. When d0was regulated within 2-5 mm,an optimal kLa could be achieved, and 21% of power consumption could be saved, compared to the case of bubbles with a diameter of 10 mm.

Influence of Stand-Alone Vertical Gas Vents on Aeration and Denitrification of Organic Municipal Waste Assessed by Two-Dimensional (2D) Lysimeters

Landfilled organic waste, in the presence of oxygen, can undergo aerobic decomposition facilitated by heterotrophic microorganisms. Aerobic degradation of solid waste can quickly consume available oxygen thus curtailing further degradation. The aim of this study was the investigation of a low-cost method of replenishing oxygen consumed in landfilled waste. Three 2D lysimeters were established to investigate the effectiveness of stand-alone, vertical ventilation pipes inserted into waste masses. Two different configurations of ventilation were tested with the third lysimeter acting as an unventilated control. Lysimeters were left uninsulated and observed over the course of 6 months with regular collection of gas and leachate samples. Lysimeters were then simulated for Oxygen (O2) and Nitrous oxide (N2O) to analyze the denitrification contributions of each. The experiment revealed that a single ventilation pipe can increase the mean oxygen level of a 1.7 m × 1.0 m area by up to 13.5%. It also identified that while increasing the density of ventilation pipes led to increased O2 levels, this increase was not significant at the 0.05 probability level. A single vent averaged 13.67% O2 while inclusion of an additional vent in the same area only increased the average to 14.59%, a 6.7% increase. Simulation helped to verify that lower ventilation pipe placement density may be more efficient as in addition to the effect on oxygenation, denitrification efficiency may increase. Simulations of N2O production estimated between 8% - 20% more N2O being generated with lower venting density configurations.

Operational guidance for aeration and flow augmentation for the Chicago Area Waterway System—A case study

The Chicago Area Waterway System(CAWS)is a 133.9 km branching network of navigable waterways controlled by hydraulic structures,in which the majority of the flow is treated wastewater effluent and there are periods of substantial combined sewer overflows.The CAWS comprises a network of effluent dominated streams.More stringent dissolved oxygen(DO)standards and a reduced flow augmentation allowance have been recently applied to the CAWS.Therefore,a carefully calibrated and verified one-dimensional flow and water quality model was applied to the CAWS to determine emission-based real-time control guidelines for the operation of flow augmentation and aeration stations.The goal of these guidelines was to attain DO standards at least 95%of the time.The“optimal”guidelines were tested for representative normal,dry,and wet years.The finally proposed guidelines were found in the simulations to attain the 95%target for nearly all locations in the CAWS for the three test years.The developed operational guidelines have been applied since 2018 and have shown improved attainment of the DO standards throughout the CAWS while at the same time achieving similar energy use at the aeration stations on the Calumet River system,greatly lowered energy use on the Chicago River system,and greatly lowered discretionary diversion from Lake Michigan,meeting the recently enacted lower amount of allowed annual discretionary diversion.This case study indicates that emission-based real-time control developed from a well calibrated model holds potential to help many receiving water bodies achieve high attainment of water quality standards.

Short-Term Household Load Forecasting Based on Attention Mechanism and CNN-ICPSO-LSTM

Accurate load forecasting forms a crucial foundation for implementing household demand response plans andoptimizing load scheduling. When dealing with short-term load data characterized by substantial fluctuations,a single prediction model is hard to capture temporal features effectively, resulting in diminished predictionaccuracy. In this study, a hybrid deep learning framework that integrates attention mechanism, convolution neuralnetwork (CNN), improved chaotic particle swarm optimization (ICPSO), and long short-term memory (LSTM), isproposed for short-term household load forecasting. Firstly, the CNN model is employed to extract features fromthe original data, enhancing the quality of data features. Subsequently, the moving average method is used for datapreprocessing, followed by the application of the LSTM network to predict the processed data. Moreover, the ICPSOalgorithm is introduced to optimize the parameters of LSTM, aimed at boosting the model’s running speed andaccuracy. Finally, the attention mechanism is employed to optimize the output value of LSTM, effectively addressinginformation loss in LSTM induced by lengthy sequences and further elevating prediction accuracy. According tothe numerical analysis, the accuracy and effectiveness of the proposed hybrid model have been verified. It canexplore data features adeptly, achieving superior prediction accuracy compared to other forecasting methods forthe household load exhibiting significant fluctuations across different seasons.

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Accurate forecasting of time series is crucial across various domains.Many prediction tasks rely on effectively segmenting,matching,and time series data alignment.For instance,regardless of time series with the same granularity,segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy.However,these events of varying granularity frequently intersect with each other,which may possess unequal durations.Even minor differences can result in significant errors when matching time series with future trends.Besides,directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy.Therefore,this paper proposes a short-term forecasting method for time series based on a multi-granularity event,MGE-SP(multi-granularity event-based short-termprediction).First,amethodological framework for MGE-SP established guides the implementation steps.The framework consists of three key steps,including multi-granularity event matching based on the LTF(latest time first)strategy,multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio,and a short-term prediction model based on XGBoost.The data from a nationwide online car-hailing service in China ensures the method’s reliability.The average RMSE(root mean square error)and MAE(mean absolute error)of the proposed method are 3.204 and 2.360,lower than the respective values of 4.056 and 3.101 obtained using theARIMA(autoregressive integratedmoving average)method,as well as the values of 4.278 and 2.994 obtained using k-means-SVR(support vector regression)method.The other experiment is conducted on stock data froma public data set.The proposed method achieved an average RMSE and MAE of 0.836 and 0.696,lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method,as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

Predictive value of red blood cell distribution width and hematocrit for short-term outcomes and prognosis in colorectal cancer patients undergoing radical surgery

BACKGROUND Previous studies have reported that low hematocrit levels indicate poor survival in patients with ovarian cancer and cervical cancer,the prognostic value of hematocrit for colorectal cancer(CRC)patients has not been determined.The prognostic value of red blood cell distribution width(RDW)for CRC patients was controversial.AIM To investigate the impact of RDW and hematocrit on the short-term outcomes and long-term prognosis of CRC patients who underwent radical surgery.METHODS Patients who were diagnosed with CRC and underwent radical CRC resection between January 2011 and January 2020 at a single clinical center were included.The short-term outcomes,overall survival(OS)and disease-free survival(DFS)were compared among the different groups.Cox analysis was also conducted to identify independent risk factors for OS and DFS.RESULTS There were 4258 CRC patients who underwent radical surgery included in our study.A total of 1573 patients were in the lower RDW group and 2685 patients were in the higher RDW group.There were 2166 and 2092 patients in the higher hematocrit group and lower hematocrit group,respectively.Patients in the higher RDW group had more intraoperative blood loss(P<0.01)and more overall complications(P<0.01)than did those in the lower RDW group.Similarly,patients in the lower hematocrit group had more intraoperative blood loss(P=0.012),longer hospital stay(P=0.016)and overall complications(P<0.01)than did those in the higher hematocrit group.The higher RDW group had a worse OS and DFS than did the lower RDW group for tumor node metastasis(TNM)stage I(OS,P<0.05;DFS,P=0.001)and stage II(OS,P=0.004;DFS,P=0.01)than the lower RDW group;the lower hematocrit group had worse OS and DFS for TNM stage II(OS,P<0.05;DFS,P=0.001)and stage III(OS,P=0.001;DFS,P=0.001)than did the higher hematocrit group.Preoperative hematocrit was an independent risk factor for OS[P=0.017,hazard ratio(HR)=1.256,95%confidence interval(CI):1.041-1.515]and DFS(P=0.035,HR=1.194,95%CI:1.013-1.408).CONCLUSION A higher preoperative RDW and lower hematocrit were associated with more postoperative complications.However,only hematocrit was an independent risk factor for OS and DFS in CRC patients who underwent radical surgery,while RDW was not.

A modified stochastic model for LS+AR hybrid method and its application in polar motion short-term prediction

Short-term(up to 30 days)predictions of Earth Rotation Parameters(ERPs)such as Polar Motion(PM:PMX and PMY)play an essential role in real-time applications related to high-precision reference frame conversion.Currently,least squares(LS)+auto-regressive(AR)hybrid method is one of the main techniques of PM prediction.Besides,the weighted LS+AR hybrid method performs well for PM short-term prediction.However,the corresponding covariance information of LS fitting residuals deserves further exploration in the AR model.In this study,we have derived a modified stochastic model for the LS+AR hybrid method,namely the weighted LS+weighted AR hybrid method.By using the PM data products of IERS EOP 14 C04,the numerical results indicate that for PM short-term forecasting,the proposed weighted LS+weighted AR hybrid method shows an advantage over both the LS+AR hybrid method and the weighted LS+AR hybrid method.Compared to the mean absolute errors(MAEs)of PMX/PMY sho rt-term prediction of the LS+AR hybrid method and the weighted LS+AR hybrid method,the weighted LS+weighted AR hybrid method shows average improvements of 6.61%/12.08%and 0.24%/11.65%,respectively.Besides,for the slopes of the linear regression lines fitted to the errors of each method,the growth of the prediction error of the proposed method is slower than that of the other two methods.

An Enhanced Ensemble-Based Long Short-Term Memory Approach for Traffic Volume Prediction

With the advancement of artificial intelligence,traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality.Traffic volume is an influential parameter for planning and operating traffic structures.This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems.A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process.The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships.Firstly,a dataset for automatic vehicle identification is obtained and utilized in the preprocessing stage of the ensemble empirical mode decomposition model.The second aspect involves predicting traffic volume using the long short-term memory algorithm.Next,the study employs a trial-and-error approach to select a set of optimal hyperparameters,including the lookback window,the number of neurons in the hidden layers,and the gradient descent optimization.Finally,the fusion of the obtained results leads to a final traffic volume prediction.The experimental results show that the proposed method outperforms other benchmarks regarding various evaluation measures,including mean absolute error,root mean squared error,mean absolute percentage error,and R-squared.The achieved R-squared value reaches an impressive 98%,while the other evaluation indices surpass the competing.These findings highlight the accuracy of traffic pattern prediction.Consequently,this offers promising prospects for enhancing transportation management systems and urban infrastructure planning.

Research on the IL-Bagging-DHKELM Short-Term Wind Power Prediction Algorithm Based on Error AP Clustering Analysis

With the continuous advancement of China’s“peak carbon dioxide emissions and Carbon Neutrality”process,the proportion of wind power is increasing.In the current research,aiming at the problem that the forecasting model is outdated due to the continuous updating of wind power data,a short-term wind power forecasting algorithm based on Incremental Learning-Bagging Deep Hybrid Kernel Extreme Learning Machine(IL-Bagging-DHKELM)error affinity propagation cluster analysis is proposed.The algorithm effectively combines deep hybrid kernel extreme learning machine(DHKELM)with incremental learning(IL).Firstly,an initial wind power prediction model is trained using the Bagging-DHKELM model.Secondly,Euclidean morphological distance affinity propagation AP clustering algorithm is used to cluster and analyze the prediction error of wind power obtained from the initial training model.Finally,the correlation between wind power prediction errors and Numerical Weather Prediction(NWP)data is introduced as incremental updates to the initial wind power prediction model.During the incremental learning process,multiple error performance indicators are used to measure the overall model performance,thereby enabling incremental updates of wind power models.Practical examples show the method proposed in this article reduces the root mean square error of the initial model by 1.9 percentage points,indicating that this method can be better adapted to the current scenario of the continuous increase in wind power penetration rate.The accuracy and precision of wind power generation prediction are effectively improved through the method.

Effects of Aeration on Root Physiology and Nitrogen Metabolism in Rice

In order to clarify the effects of aeration on root nitrogen metabolism in rice seedlings,rice cultivars Guodao 6 （indica） and Xiushui 09 （japonica） were investigated for root growth,the activities of glutamine synthetase （GS）,glutamic acid-pyruvic acid transaminase （GPT） and glutamic acid oxaloacetate transaminase （GOT）,the nitrate （NO 3-N） concertration,the contents of free amino acids and soluble sugar in root under hydroponics with continuous aeration treatment.The results showed that rice seedlings grown in oxygenation solutions had higher root dry matter,longer root length,stronger root activity and larger root absorption area compared with the control.In addition,the contents of soluble sugar,root vigor and the activities of GS,GOT and GPT in the aeration solutions were higher than those in the control.The results also indicated that the activities of enzymes involved in root nitrogen metabolism of Xiushui 09 were enhanced by aeration,however,there was no significant influence on root nitrogen metabolism of Guodao 6,which suggested that effect of oxygenation on rice root nitrogen metabolism might be genotype-specific.

Methanogenesis acceleration of fresh landfilled waste by micro-aeration

When municipal solid waste(MSW) with high content of food waste is landfilled, the rapid hydrolysis of food waste results in the imbalance of anaerobic metabolism in the landfill layer, indicated by accumulation of volatile fatty acids(VFA) and decrease of pH value. This occurrence could lead to long lag time before the initiation of methanogenesis and to the production of strong leachate. Simulated landfill columns with forced aeration, with natural ventilation, and with no aeration, were monitored regarding their organics degradation rate with leachate recirculation. Hydrolysis reactions produced strong leachate in the column with no aeration. With forced aeration, the produced VFA could be effectively degraded, leading to the reduction in COD of the leachate effluent since the week 3. The CH_4 in the landfill gas from the column with aeration rate of 0.39 m3/(m3·d) and frequency of twice/d, leachate recirculation rate of 12.2 mm/d and frequency of twice/d, could amount to 40%(v/v) after only 20 weeks. This amount had increased up to 50% afterward even with no aeration. Most of COD in the recirculated leachate was removed. Using natural ventilation, CH_4 could also be produced and the COD of the leachate effluent be reduced after 10 weeks of operation. However, the persistent existence of oxygen in the landfill layer yielded instability in methanogenesis process.

Insight into the structural evolution of porous and fractured media by forced aeration during heap leaching

Despite lots of techniques in improving the heap leaching performance,many constraints on the industrial applications remain.We proposed a correspondingly effective and new idea of introducing forced aeration to improve the bad permeability and leaching effect of Yangla Copper Mine(YCM)during heap leaching.The dual-media theory was employed to study the impact mechanism of forced aeration on the variations of porous and fractured media during the column leaching experiments.An X-Ray Computed Tomography(CT)set was utilized to perform the pore imaging of the specimens and the fracture morphology of the particles within the columns was analyzed by Scanning Electron Microscope(SEM)as aeration rate(AR)changed.The results show that there exists copious fine particles within the heap of YCM,the particle size distribution of which is not reasonable.The forced aeration can not only promote the development of the porous and fractured structures but effectively break the blocked seepage paths.Then the leaching degree is improved and the seepage performance of the solute within the solution is enhanced.Therefore,the forced aeration is probable of making the leaching performance greatly improved.

Application of a water quality model for determining instream aeration station location and operational rules:A case study

Instream aeration has been used as a supplement to secondary treatment or a substitute for tertiary treatment for meeting dissolved oxygen （DO） standards in rivers. Many studies have used water quality models to determine the number, location, and capacity of instream aeration stations （IASs） needed to meet DO standards in combination with other pollution control measures. DO concentrations have been improved in the North Shore Channel and North Branch Chicago River by the Devon Avenue IAS for more than 35 years. A study was initiated to determine whether it was better to rehabilitate or relocate this station and to determine appropriate operational guidance for the IAS at the selected location. A water quality model capable of simulating DO concentrations during unsteady flow was used to evaluate the proper location for an IAS and operational guidance for this IAS. Three test years, a dry year, a wet year, and an extreme year, were considered in the evaluation. The study found that the Devon Avenue IAS should be rehabilitated as this location performed as well as or better than any of 10 alternative locations. According to the new operational guidance for this IAS, the amount of time with blowers operating could be substantially reduced compared to traditional operations while at the same time the attainment of the DO standards could be increased. This study shows that a carefully designed modeling study is key to effective selection, location, and operation of IASs such that attainment of DO standards can be maximized while operation hours of blowers can be minimized.

The effects of aeration and irrigation regimes on soil CO_2 and N_2O emissions in a greenhouse tomato production system

Aerated irrigation has been proven to increase crop production and quality, but studies on its environmental impacts are sparse. The effects of aeration and irrigation regimes on soil CO2 and N2O emissions in two consecutive greenhouse tomato rotation cycles in Northwest China were studied via the static closed chamber and gas chromatography technique. Four treatments, aerated deficit irrigation（AI1）, non-aerated deficit irrigation（CK1）, aerated full irrigation（AI2） and non-aerated full irrigation（CK2）, were performed. The results showed that the tomato yield under aeration of each irrigation regime increased by 18.8% on average compared to non-aeration, and the difference was significant under full irrigation（P〈0.05）. Full irrigation significantly increased the tomato yield by 23.9% on average in comparison to deficit irrigation. Moreover, aeration increased the cumulative CO2 emissions compared to non-aeration, and treatment effects were significant in the autumn-winter season（P〈0.05）. A slight increase of CO2 emissions in the two seasons was observed under full irrigation（P〉0.05）. There was no significant difference between aeration and non-aeration in soil N2O emissions in the spring-summer season, whereas aeration enhanced N2O emissions significantly in the autumn-winter season. Furthermore, full irrigation over the two seasons greatly increased soil N2O emissions compared to the deficit irrigation treatment（P〈0.05）. Correlation analysis indicated that soil temperature was the primary factor influencing CO2 fluxes. Soil temperature, soil moisture and NO3^- were the primary factors influencing N2O fluxes. Irrigation coupled with particular soil aeration practices may allow for a balance between crop production yield and greenhouse gas mitigation in greenhouse vegetable fields.

Turbulence,aeration and bubble features of air-water flows in macro-and intermediate roughness conditions

Free surface flows aeration potential causing the in macro- and intermediate flow characteristics to vary roughness conditions have a high with slopes and discharges. The underlying mechanism of two-phase flow characteristics in macro- and intermediate roughness conditions were analyzed in an experimental setup assembled at the Laboratory of Hydraulic Protection of the Territory （PITLAB） of the University of Pisa, Italy. Crushed angular rocks and hemispherical boulders were used to intensify the roughness of the bed. Flow rates per unit width ranging between 0.03 m^2/s and 0.09 m^2/s and slopes between 0.26 and 0.46 were tested over different arrangements of a rough bed. Analyses were mainly carried out in the inner flow region, which consists of both bubbly and intermediate flow regions. The findings revealed that the two-phase flow properties over the rough bed were much affected by rough bed arrangements. Turbulence features of two-phase flows over the rough bed were compared with those of the stepped chute data under similar flow conditions. Overall, the results highlight the flow features in the inner layers of the two-phase flow, showing that the maximum turbulence intensity decreases with the relative submergence, while the bubble frequency distribution is affected by the rough bed elements.

Effects of forced aeration on community dynamics of free and attached bacteria in copper sulphide ore bioleaching

Bacterial community dynamics and copper leaching with applied forced aeration were investigated during low-grade copper sulphide bioleaching to obtain better bioleaching efficiency.Results illustrated that appropriate aeration improved bacterial concentrations and leaching efficiencies.The highest bacterial concentration and Cu^(2+)concentration after 14-d leaching were 7.61×10^(7) cells·mL^(−1) and 704.9 mg·L^(−1),respectively,at aeration duration of 4 h·d^(−1).The attached bacteria played a significant role during bioleaching from 1 to 7 d.However,free bacteria dominated the bioleaching processes from 8 to 14 d.This phenomenon was mainly caused by the formation of passivation layer through Fe3+hydrolysis along with bioleaching,which inhibited the contact between the attached bacteria and ore.Meanwhile,16S rDNA analysis verified the effect of Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans on the bioleaching process.The results demonstrate the importance of free and attached bacteria in bioleaching.

A novel aeration strategy in repeated-batch fermentation for efficient ethanol production from sweet sorghum juice

To improve the efficiency of ethanol production in a batch fermentation from sweet sorghum juice under a very high gravity(VHG)condition(~290 g/L of total sugar)by Saccharomyces cerevisiae NP01,repeatedbatch fermentation under an aerated condition(2.5 vvm for the first 4 h during every cycle)was done in a5-L fermenter.The average ethanol concentration(P),productivity(Qp)and yield(Yp/s)for five successive cycles were 112.31 g/L,1.55 g/L·h^-1 and 0.44,respectively with 80.97%sugar consumption.To complete sugar consumption,the total sugar of the juice was reduced to a high gravity(HG)level(~240 g/L).The results showed that yeast extract was not necessary for ethanol production,and aeration during every other cycle i.e.,alternating cycles,was sufficient to promote both yeast growth and ethanol production.The average P,Qpand Yp/svalues for eight successive cycles with aeration during alternating cycles were97.58 g/L,1.98 g/Láh and 0.41,respectively with 91.21%sugar consumption.The total fatty acids in the yeast cells under the aerated condition were^50%higher than without aeration,irrespective the initial sugar concentration,whereas the ergosterol contents under aeration condition were^29%to 49%higher than those without aeration.

Design and Operation of the Printing and Dyeing Wastewater Treatment Project by Combination of Coagulation Sedimentation - Hydrolytic Acidification with Aeration Tank - Biological Aerated Filter - Active Sand Filter

In order to protect quality of Baiyangdian surface water and Gaoyang groundwater,the project is applied to process printing and dyeing wastewater that contains complicated compositions,high concentrations of organics and SS,and lots of pollutants difficult to degrade by microorganism. The process and operating parameters of project are optimized and debugged,and its economic and environmental benefits are analyzed.The results show that the process of coagulation sedimentation-hydrolytic acidification with aeration tank-biological aerated filter-active sand filter is applied in Gaoyang Sewage Treatment Plant. The design scale of sewage treatment plant is 120000 m^3/d. The influent is as following: COD is 669mg/L; SS is 424mg/L; NH_3-N is 8.83mg/L; TP is 6.03mg/L. After the process,the best removal rates of COD,SS,NH_3-N and TP are 93. 5%,98. 8%,97. 1% and 96. 2%,respectively. The various indexes of effluent water complied with standard A of the first order in Pollutants Emission Standard of Urban Wastewater Treatment Plant( GB 18918-2002). The processing cost is only 0. 807 yuan/m^3. As a result,the project construction and operation not only improve the environment,but also promote regional economic development. Process design and operating parameters provide an important reference value for the printing and dyeing wastewater treatment industry.

Nitrogen and phosphorus removal under intermittent aeration conditions

A practice wastewater treatment plant was operated using intermittent aeration activated sludge process to enhance biological nitrogen and phosphorus removal. When the influent concentrations of COD Cr , BOD 5, TN, TP, NH 3\|N, TKN, and SS varied in a range of 207.5—1640 mg/L, 61.8—637 mg/L, 28.5—75.6 mg/L, 4.38—20.2 mg/L, 13.6—31.9 mg/L, 28.5—75.6 mg/L, and 111—1208 mg/L, the effluent means were less than 50 mg/L, 20 mg/L, 5 mg/L, 1.0 mg/L, 5 mg/L, 10 mg/L, and 20 mg/L, respectively. Based on a long time of operating results, this process is very suitable for nutrient biological removal for treating the municipal wastewater those water characteristics are similar as that of the Songjiang Municipal Waste Water Treatment Plant(SJMWTP).