Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise ge...Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise generated from harsh operations and instruments,while the local feature pattern and long-term dependency in the wastewater quality time series,the prediction performance can be degraded.In this paper,a discrete wavelet transform and convolutional enhanced Transformer(DWT-Ce Transformer) method is developed to predict the influent quality in WWTPs.Specifically,we perform multi-scale analysis on time series of wastewater quality using discrete wavelet transform,effectively removing noise while preserving key data characteristics.Further,a tightly coupled convolutional-enhanced Transformer model is devised where convolutional neural network is used to extract local features,and then these local features are combined with Transformer's self-attention mechanism,so that the model can not only capture long-term dependencies,but also retain the sensitivity to local context.In this study,we conduct comprehensive experiments based on the actual data from a WWTP in Shaanxi Province and the simulated data generated by BSM2.The experimental results show that,compared to baseline models,DWT-Ce Transformer can significantly improve the prediction performance of influent COD and NH_(3)-N.Specifically,MSE,MAE,and RMSE improve by 78.7%,79.5%,and 53.8% for COD,and 79.4%,70.2%,and 54.5% for NH_(3)-N.On simulated data,our method shows strong improvements under various weather conditions,especially in dry weather,with MSE,MAE,and RMSE for COD improving by 68.9%,48.0%,and 44.3%,and for NH_(3)-N by 78.4%,54.8%,and 53.2%.展开更多
Two sequencing batch reactors（SBRs） were operated for 100 days under aerobic conditions,with one being fed with unsterilized municipal wastewater（USBR）, and the other fed with sterilized municipal wastewater（SSBR...Two sequencing batch reactors（SBRs） were operated for 100 days under aerobic conditions,with one being fed with unsterilized municipal wastewater（USBR）, and the other fed with sterilized municipal wastewater（SSBR）. Respirometric assays and fluorescence in situ hybridization（FISH） results show that active nitrifiers were present in the unsterilized influent municipal wastewater. The maximum ammonia utilization rate（AUR） and nitrite utilization rate（NUR） of the unsterilized influent were 0.32 ± 0.12 mg NH4＋-N/（L·hr） and0.71 ± 0.18 mg NO2--N/（L·hr）. Based on the maximum utilization rates, the estimated seeding intensity for the ammonia oxidizing bacteria（AOB） and nitrite oxidizing bacteria（NOB） of the USBR was 0.08 g AOB/（g AOB·day） and 0.20 g NOB/（g NOB·day） respectively. The fraction of nitrifiers/total bacteria in the influent was 5.35% ± 2.1%, the dominant AOB was Nitrosomonas spp., Nitrosococcus mobilis hybridizated with Nsm156, and the dominant NOB was Nitrospira hybridizated with Ntspa662. The influent nitrifiers potentially seeded the activated sludge of the bioreactor and hence demonstrated a mitigation of the acclimatization times and instability during start-up and early operation. The AUR and NUR in the USBR was 15% and 13% higher than the SSBR respectively during the stable stage, FISH results showed that nitrifiers population especially the Nitrospira in the USBR was higher than that in the SSBR. These results indicate that the natural continuous immigration of nitrifiers from municipal influent streams may have some repercussions on the modeling and design of bioreactors.展开更多
With the development of activated sludge model, the simulation software for the design and operation of wastewater treatment plant （WWTP） was produced and has been widely used. The dynamic change of the quality and ...With the development of activated sludge model, the simulation software for the design and operation of wastewater treatment plant （WWTP） was produced and has been widely used. The dynamic change of the quality and flow of influent are major factors causing the unstable operation of wastewater treatment process. As a basic model, ASMI model was used for the simulation of activated sludge process, and double exponential model was selected for the simulation of secondary sedimentation tank. The influences of influent change to the aeration tank and secondary sedimentation tank were investigated, and the relationship among influent change, the quality of effluent and the level of sludge blanket in secondary sedimentation tank was established. On the basis of the simulation results, the operation of the WWTP could be adjusted under the dynamic change of the influent. Furthermore, the controlling strategy combined the feed-forward on the influent flow and the feedback on the level of sludge blanket in the secondary sedimentation tank was studied.展开更多
In order to investigate the effect of influent condition heterogeneity on diversity of the bacterial community,the degree of microbial resolution and effluent quality,biological treatment of micro-polluted source wate...In order to investigate the effect of influent condition heterogeneity on diversity of the bacterial community,the degree of microbial resolution and effluent quality,biological treatment of micro-polluted source water is proposed. Scanning Electron Microscopy( SEM) analysis reflects that influent conditions change the morphologies of biofilm. Denaturing Gradient Gel Electrophoresis( DGGE) analysis shows differences of H values are due to succession of functional bacterial communities. Microbial resolution values and species identifications reveal organic carbon is the main cause of community differentiation and bacterial migration.展开更多
Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation. Its uncertain factors are classified into three categories including day types, weather conditions and specia...Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation. Its uncertain factors are classified into three categories including day types, weather conditions and special events, of which the latter two are considered with a BP (Back Propagation) model. On this basis, the daily period feature is taken into account in the presented model. The data from a practical sewage treatment plant utility is employed to show the effectiveness of the method.展开更多
The application of the Genetic Algorithm （GA） for the influent flow optimized distribution in the four stage pilot plant of Step-Feed Biological Nutrient Removal （BNR） System was discussed. Under decided process p...The application of the Genetic Algorithm （GA） for the influent flow optimized distribution in the four stage pilot plant of Step-Feed Biological Nutrient Removal （BNR） System was discussed. Under decided process parameter and influent water conditions, the objective function of optimization was designed to minimize the difference between estimated and required effluent concentrations at the four stage pilot plant of Step-Feed BNR System, the optimized parameter for influent distribution ratios of the four stages is 37.2%, 27.4%, 23.2% and 12.2% respectively. According to the optimizations results and raw wastewater pilot-scale experiment, the average removal efficiencies for pollutants are higher.展开更多
The k-nearest neighbor （k-NN） method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand （COD）, suspended solid （SS）, total nitrogen （T-N） and total phosphor...The k-nearest neighbor （k-NN） method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand （COD）, suspended solid （SS）, total nitrogen （T-N） and total phosphorus （T-P） at a wastewater treatment plant （WWTP）. The search range and approach for determining the number of nearest neighbors （NNs） under dry and wet weather conditions were initially optimized based on the root mean square error （RMSE）. The optimum search range for considering data size was one year. The square root-based （SR） approach was superior to the distance factor-based （DF） approach in determining the appropriate number of NNs. However, the results for both approaches varied slightly depending on the water quality and the weather conditions. The influent flow rate was accurately predicted within one standard deviation of measured values. Influent water qualities were well predicted with the mean absolute percentage error （MAPE） under both wet and dry weather conditions. For the seven-day prediction, the difference in predictive accuracy was less than 5% in dry weather conditions and slightly worse in wet weather conditions. Overall, the k-NN method was verified to be useful for predicting WWTP influent characteristics.展开更多
The water quality and energy consumption of wastewater treatment plants(WWTPs)in Taihu Basin were evaluated on the basis of the operation data from 204 municipal WWTPs in the basin by using various statistical methods...The water quality and energy consumption of wastewater treatment plants(WWTPs)in Taihu Basin were evaluated on the basis of the operation data from 204 municipal WWTPs in the basin by using various statistical methods.The influent ammonia nitrogen(NH3-N)and total nitrogen(TN)of WWTPs in Taihu Basin showed normal distribution,whereas chemical oxygen demand(COD),biochemical oxygen demand(BOD5),suspended solid(SS),and total phosphorus(TP)showed positively skewed distribution.The influent BOD5/COD was 0.4%-0.6%,only 39.2%SS/BOD5 exceeded the standard by 36.3%,the average BOD5/TN was 3.82,and the probability of influent BOD5/TP>20 was 82.8%.The average energy consumption of WWTPs in Taihu Basin in 2017 was 0.458 kWh/m^3.The specific energy consumption of WWTPs with a daily treatment capacity of more than 5×10^4 m^3 in Taihu Basin was stable at 0.33 kWh/m^3.A power function relationship was observed between the reduction in COD and NH3-N and the specific energy consumption of pollutant reduction,and the higher the pollutant reduction is,the lower the specific energy consumption of pollutant reduction presents.In addition,a linear relationship existed between the energy consumption of WWTPs and the specific energy consumption of influent volume and pollutant reduction.Therefore,upgrading and operation with less energy consumption of WWTPs is imperative and the suggestions for Taihu WWTPs based on stringent discharge standard are proposed in detail.展开更多
In wastewater treatment plants(WWTPs),microplastics(MPs)are complex,especially with mixed domestic–industrial influents.Conventional random grab sampling can roughly depict the distribution and characteristics of MPs...In wastewater treatment plants(WWTPs),microplastics(MPs)are complex,especially with mixed domestic–industrial influents.Conventional random grab sampling can roughly depict the distribution and characteristics of MPs but can not accurately reflect their daily fluctuations.In this study,the concentration,shape,polymer type,size,and color of MPs were analyzed by micro-Raman spectroscopy(detection limit of 0.05 mm)throughout treatment stages of three mixed domestic–industrial WWTPs(W1,W2,and W3)in Wuxi City,China,and the daily fluctuations of MPs were also obtained by dense grab sampling within 24 h.For influent samples,the average MP concentration of 392.2 items/L in W1 with 10%industrial wastewater was much higher than those in W2(71.2 items/L with 10%industrial wastewater)and W3(38.3 items/L with 60%industrial wastewater).White polyethylene granules with a diameter less than 0.5 mm from plastic manufacturing were the most dominant MPs in the influent of W1,proving the key role of industrial sources in MPs pollution.In addition,the daily dense sampling results showed that MP concentration in W1 influent fluctuated widely between 29.1 items/L and 4617.6 items/L within a day.Finally,few MPs(less than 4.0 items/L)in these WWTPs effluents were attributed to the effective removal of wastewater treatment processes.Thus,further attention should be paid to regulating the primary sources of MPs.展开更多
Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants(WWTPs),as it is closely related to wastewater characteristics such as biochemical oxygen demand(BOD),total ...Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants(WWTPs),as it is closely related to wastewater characteristics such as biochemical oxygen demand(BOD),total suspend solids(TSS),and pH.Previous studies have been conducted to predict influent flow rate,and it was proved that data-driven models are effective tools.However,most of these studies have focused on batch learning,which is inadequate for wastewater prediction in the era of COVID-19 as the influent pattern changed significantly.Online learning,which has distinct advantages of dealing with stream data,large data set,and changing data pattern,has a potential to address this issue.In this study,the performance of conventional batch learning models Random Forest(RF),K-Nearest Neighbors(KNN),and Multi-Layer Perceptron(MLP),and their respective online learning models Adaptive Random Forest(aRF),Adaptive K-Nearest Neighbors(aKNN),and Adaptive Multi-Layer Perceptron(aMLP),were compared for predicting influent flow rate at two Canadian WWTPs.Online learning models achieved the highest R2,the lowest MAPE,and the lowest RMSE compared to conventional batch learning models in all scenarios.The R2 values on testing data set for 24-h ahead prediction of the aRF,aKNN,and aMLP at Plant A were 0.90,0.73,and 0.87,respectively;these values at Plant B were 0.75,0.78,and 0.56,respectively.The proposed online learning models are effective in making reliable predictions under changing data patterns,and they are efficient in dealing with continuous and large influent data streams.They can be used to provide robust decision support for wastewater treatment and management in the changing era of COVID-19 and also under other unprecedented emergencies that could change influent patterns.展开更多
The prediction of the influent load is of great importance for the improvement of the control system to a large wastewater treatment plant. A systematic data analysis method is presented in this paper in order to esti...The prediction of the influent load is of great importance for the improvement of the control system to a large wastewater treatment plant. A systematic data analysis method is presented in this paper in order to estimate and predict the periodicity of the influent flow rate and ammonia （NH3） concentrations： 1） data filtering using wavelet decomposition and reconstruction; 2） typical cycle identification using power spectrum density analysis; 3） fitting and prediction model establishment based on an autoregressive model. To give meaningful information for feedforward control systems, predictions in different time scales are tested to compare the corresponding predicting accuracy. Considering the influence of the rainfalls, a linear fitting model is derived to estimate the relationship between flow rate trend and rain events. Measurements used to support coefficient fitting and model testing are acquired from two municipal wastewater treatment plants in China. The results show that 1） for both of the two plants, the periodicity affects the flow rate and NH3 concentrations in different cycles （especially cycles longer than 1 day）; 2） when the flow rate and NH3 concentrations present an obvious periodicity, the decreasing of prediction accuracy is not distinct with increasing of the prediction time scales; 3） the periodicity influence is larger than rainfalls; 4） the rainfalls will make the periodicity of flow rate less obvious in intensive rainy periods.展开更多
Shortcut nitrification-denitrification,anaerobic ammonium oxidation(ANAMMOX),and methanogenesis have been successfully coupled in an Expanded Granular Sludge Bed-Biological Aerated Filter(EGSB-BAF)integrated system.As...Shortcut nitrification-denitrification,anaerobic ammonium oxidation(ANAMMOX),and methanogenesis have been successfully coupled in an Expanded Granular Sludge Bed-Biological Aerated Filter(EGSB-BAF)integrated system.As fed different synthetic wastewater with chemical oxygen demand(COD)of 300-1200 mg·L^(-1)and NH_(4)^(+)-N of 30-120 mg·L^(-1)at the outer recycle ratio of 200%,the influence of influent on ANAMMOX in the integrated system was investigated in this paper.The experimental results showed that higher COD concentration caused an increase in denitrification and methanogenesis but a decrease in ANAMMOX;however,when an influent with the low concentration of COD was used,the opposite changes could be observed.Higher influent NH_(4)^(+)-N concentration favored ANAMMOX when the COD concentration of influent was fixed.Therefore,low COD=NH_(4)^(+)-N ratio would decrease competition for nitrite between ANAMMOX and denitrification,which was favorable for reducing the negative effect of organic COD on ANAMMOX.The good performance of the integrated system indicated that the bacterial community of denitrification,ANAMMOX,and methanogenesis could be dynamically maintained in the sludge of EGSB reactor for a certain range of influent.展开更多
Per-and polyfluoroalkyl substances(PFAS)are found ubiquitously in wastewater treatment plants(WWTPs)due to their multiple sources in industry and consumer products.In Australia,limited spatial data are available on PF...Per-and polyfluoroalkyl substances(PFAS)are found ubiquitously in wastewater treatment plants(WWTPs)due to their multiple sources in industry and consumer products.In Australia,limited spatial data are available on PFAS levels inWWTPs influent,while no temporal data have been reported.The aim of this study was to investigate the occurrence and temporal trend of PFAS in the influent of two large WWTPs in Australia(WWTP A and B)over a four-year period.Daily influent samples were collected over one week at different seasons from 2014 to 2017.Eleven perfluoroalkyl acids(PFAA)(i.e.seven perfluoroalkyl carboxylic acids(PFCAs)and four perfluoroalkyl sulfonic acids(PFSA))were detected with mean S11PFAA concentrations of 57±3.3e94±17 ng/L at WWTP A,and 31±6.1e142±73 ng/L at WWTP B.The highest mean concentrations were observed for perfluorohexanoate(PFHxA)(20±2 ng/L)in WWTP A,and perfluorooctane sulfonate(PFOS)(17±13 ng/L)in WWTP B.The precursor 6:2 fluorotelomer sulfonate was detected over five sampling periods from Aug 2016 to Oct 2017,with mean concentrations of 37±18e138±51 ng/L for WWTP A and 8.8±4.5e29±5.1 ng/L for WWTP B.Higher concentration of 6:2 FTS(1.8e11 folds)than those of PFOA and PFOS in WWTP A indicate a likely substitution of C8 PFAA by fluorotelomer-based PFAS in this catchment.Temporal trends(annual and seasonal)in per-capita mass load were observed for some PFAA,increasing for PFPeA,PFHxA,PFHpA,PFNA,and PFHxS,while decreasing for PFBS and PFOS in either WWTPs.Notably,elevated levels of PFOS in October 2017 were observed at both WWTPs with the highest per capita mass load of up to 67 mg/day/inhabitant.For some PFAS release trends,longer sampling periods would be required to achieve acceptable statistical power.展开更多
By predicting influent quantity,a wastewater treatment plant(WWTP)can be well controlled.The non-linear dynamic characteristic of WWTP influent quantity time series was analyzed,with the assumption that the series was...By predicting influent quantity,a wastewater treatment plant(WWTP)can be well controlled.The non-linear dynamic characteristic of WWTP influent quantity time series was analyzed,with the assumption that the series was predictable.Based on this,a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction.Reasonable forecasting results were achieved using this method.展开更多
基金funded by the Natural Science Basic Research Program of Shaanxi (2024JCYBMS576)the National Natural Science Foundation of China (62366053)。
文摘Accurate prediction of wastewater treatment plants(WWTPs) influent quality can provide valuable decision-making support to facilitate operations and management.However,since existing methods overlook the data noise generated from harsh operations and instruments,while the local feature pattern and long-term dependency in the wastewater quality time series,the prediction performance can be degraded.In this paper,a discrete wavelet transform and convolutional enhanced Transformer(DWT-Ce Transformer) method is developed to predict the influent quality in WWTPs.Specifically,we perform multi-scale analysis on time series of wastewater quality using discrete wavelet transform,effectively removing noise while preserving key data characteristics.Further,a tightly coupled convolutional-enhanced Transformer model is devised where convolutional neural network is used to extract local features,and then these local features are combined with Transformer's self-attention mechanism,so that the model can not only capture long-term dependencies,but also retain the sensitivity to local context.In this study,we conduct comprehensive experiments based on the actual data from a WWTP in Shaanxi Province and the simulated data generated by BSM2.The experimental results show that,compared to baseline models,DWT-Ce Transformer can significantly improve the prediction performance of influent COD and NH_(3)-N.Specifically,MSE,MAE,and RMSE improve by 78.7%,79.5%,and 53.8% for COD,and 79.4%,70.2%,and 54.5% for NH_(3)-N.On simulated data,our method shows strong improvements under various weather conditions,especially in dry weather,with MSE,MAE,and RMSE for COD improving by 68.9%,48.0%,and 44.3%,and for NH_(3)-N by 78.4%,54.8%,and 53.2%.
基金supported by the National Natural Science Foundation for Young Scholars of China(No.51208414)the Education Department of Shaanxi Province Special Scientific Research(No.12JK0650)
文摘Two sequencing batch reactors（SBRs） were operated for 100 days under aerobic conditions,with one being fed with unsterilized municipal wastewater（USBR）, and the other fed with sterilized municipal wastewater（SSBR）. Respirometric assays and fluorescence in situ hybridization（FISH） results show that active nitrifiers were present in the unsterilized influent municipal wastewater. The maximum ammonia utilization rate（AUR） and nitrite utilization rate（NUR） of the unsterilized influent were 0.32 ± 0.12 mg NH4＋-N/（L·hr） and0.71 ± 0.18 mg NO2--N/（L·hr）. Based on the maximum utilization rates, the estimated seeding intensity for the ammonia oxidizing bacteria（AOB） and nitrite oxidizing bacteria（NOB） of the USBR was 0.08 g AOB/（g AOB·day） and 0.20 g NOB/（g NOB·day） respectively. The fraction of nitrifiers/total bacteria in the influent was 5.35% ± 2.1%, the dominant AOB was Nitrosomonas spp., Nitrosococcus mobilis hybridizated with Nsm156, and the dominant NOB was Nitrospira hybridizated with Ntspa662. The influent nitrifiers potentially seeded the activated sludge of the bioreactor and hence demonstrated a mitigation of the acclimatization times and instability during start-up and early operation. The AUR and NUR in the USBR was 15% and 13% higher than the SSBR respectively during the stable stage, FISH results showed that nitrifiers population especially the Nitrospira in the USBR was higher than that in the SSBR. These results indicate that the natural continuous immigration of nitrifiers from municipal influent streams may have some repercussions on the modeling and design of bioreactors.
文摘With the development of activated sludge model, the simulation software for the design and operation of wastewater treatment plant （WWTP） was produced and has been widely used. The dynamic change of the quality and flow of influent are major factors causing the unstable operation of wastewater treatment process. As a basic model, ASMI model was used for the simulation of activated sludge process, and double exponential model was selected for the simulation of secondary sedimentation tank. The influences of influent change to the aeration tank and secondary sedimentation tank were investigated, and the relationship among influent change, the quality of effluent and the level of sludge blanket in secondary sedimentation tank was established. On the basis of the simulation results, the operation of the WWTP could be adjusted under the dynamic change of the influent. Furthermore, the controlling strategy combined the feed-forward on the influent flow and the feedback on the level of sludge blanket in the secondary sedimentation tank was studied.
基金Sponsored by Major Science and Technology Program for Water Pollution Control and Treatment(Grant No.2012ZX07408001)State Key Laboratory of Urban Water Resource and Environment in China,Fundamental Research Funds for the Central Universities,China(Grant No.5710006113,HIT.BRETIII.201417)Postdoctoral Science Foundation of China(Grant No.2014T70324,LBH-Z12090)
文摘In order to investigate the effect of influent condition heterogeneity on diversity of the bacterial community,the degree of microbial resolution and effluent quality,biological treatment of micro-polluted source water is proposed. Scanning Electron Microscopy( SEM) analysis reflects that influent conditions change the morphologies of biofilm. Denaturing Gradient Gel Electrophoresis( DGGE) analysis shows differences of H values are due to succession of functional bacterial communities. Microbial resolution values and species identifications reveal organic carbon is the main cause of community differentiation and bacterial migration.
基金Funded by the National Natural Science Foundation of China (No.59838300)
文摘Daily influent quantity forecasting plays an important role in sewage treatment plant design and operation. Its uncertain factors are classified into three categories including day types, weather conditions and special events, of which the latter two are considered with a BP (Back Propagation) model. On this basis, the daily period feature is taken into account in the presented model. The data from a practical sewage treatment plant utility is employed to show the effectiveness of the method.
文摘The application of the Genetic Algorithm （GA） for the influent flow optimized distribution in the four stage pilot plant of Step-Feed Biological Nutrient Removal （BNR） System was discussed. Under decided process parameter and influent water conditions, the objective function of optimization was designed to minimize the difference between estimated and required effluent concentrations at the four stage pilot plant of Step-Feed BNR System, the optimized parameter for influent distribution ratios of the four stages is 37.2%, 27.4%, 23.2% and 12.2% respectively. According to the optimizations results and raw wastewater pilot-scale experiment, the average removal efficiencies for pollutants are higher.
文摘The k-nearest neighbor （k-NN） method was evaluated to predict the influent flow rate and four water qualities, namely chemical oxygen demand （COD）, suspended solid （SS）, total nitrogen （T-N） and total phosphorus （T-P） at a wastewater treatment plant （WWTP）. The search range and approach for determining the number of nearest neighbors （NNs） under dry and wet weather conditions were initially optimized based on the root mean square error （RMSE）. The optimum search range for considering data size was one year. The square root-based （SR） approach was superior to the distance factor-based （DF） approach in determining the appropriate number of NNs. However, the results for both approaches varied slightly depending on the water quality and the weather conditions. The influent flow rate was accurately predicted within one standard deviation of measured values. Influent water qualities were well predicted with the mean absolute percentage error （MAPE） under both wet and dry weather conditions. For the seven-day prediction, the difference in predictive accuracy was less than 5% in dry weather conditions and slightly worse in wet weather conditions. Overall, the k-NN method was verified to be useful for predicting WWTP influent characteristics.
文摘The water quality and energy consumption of wastewater treatment plants(WWTPs)in Taihu Basin were evaluated on the basis of the operation data from 204 municipal WWTPs in the basin by using various statistical methods.The influent ammonia nitrogen(NH3-N)and total nitrogen(TN)of WWTPs in Taihu Basin showed normal distribution,whereas chemical oxygen demand(COD),biochemical oxygen demand(BOD5),suspended solid(SS),and total phosphorus(TP)showed positively skewed distribution.The influent BOD5/COD was 0.4%-0.6%,only 39.2%SS/BOD5 exceeded the standard by 36.3%,the average BOD5/TN was 3.82,and the probability of influent BOD5/TP>20 was 82.8%.The average energy consumption of WWTPs in Taihu Basin in 2017 was 0.458 kWh/m^3.The specific energy consumption of WWTPs with a daily treatment capacity of more than 5×10^4 m^3 in Taihu Basin was stable at 0.33 kWh/m^3.A power function relationship was observed between the reduction in COD and NH3-N and the specific energy consumption of pollutant reduction,and the higher the pollutant reduction is,the lower the specific energy consumption of pollutant reduction presents.In addition,a linear relationship existed between the energy consumption of WWTPs and the specific energy consumption of influent volume and pollutant reduction.Therefore,upgrading and operation with less energy consumption of WWTPs is imperative and the suggestions for Taihu WWTPs based on stringent discharge standard are proposed in detail.
基金This work was supported by the Major Science and Technology Program forWater Pollution Control and Treatment(China)(No.2017ZX07302-001)the Research Program for In-depth Treatment Technology Upgradation of theWuxi Urban Sewage Treatment Plant(China)(N20191003)The authors also gratefully acknowledge the support of the Pre-research Fund of Jiangsu Collaborative Innovation Center of Technology and Material of Water Treatment(China)(No.XTCXSZ2020-2).
文摘In wastewater treatment plants(WWTPs),microplastics(MPs)are complex,especially with mixed domestic–industrial influents.Conventional random grab sampling can roughly depict the distribution and characteristics of MPs but can not accurately reflect their daily fluctuations.In this study,the concentration,shape,polymer type,size,and color of MPs were analyzed by micro-Raman spectroscopy(detection limit of 0.05 mm)throughout treatment stages of three mixed domestic–industrial WWTPs(W1,W2,and W3)in Wuxi City,China,and the daily fluctuations of MPs were also obtained by dense grab sampling within 24 h.For influent samples,the average MP concentration of 392.2 items/L in W1 with 10%industrial wastewater was much higher than those in W2(71.2 items/L with 10%industrial wastewater)and W3(38.3 items/L with 60%industrial wastewater).White polyethylene granules with a diameter less than 0.5 mm from plastic manufacturing were the most dominant MPs in the influent of W1,proving the key role of industrial sources in MPs pollution.In addition,the daily dense sampling results showed that MP concentration in W1 influent fluctuated widely between 29.1 items/L and 4617.6 items/L within a day.Finally,few MPs(less than 4.0 items/L)in these WWTPs effluents were attributed to the effective removal of wastewater treatment processes.Thus,further attention should be paid to regulating the primary sources of MPs.
文摘Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants(WWTPs),as it is closely related to wastewater characteristics such as biochemical oxygen demand(BOD),total suspend solids(TSS),and pH.Previous studies have been conducted to predict influent flow rate,and it was proved that data-driven models are effective tools.However,most of these studies have focused on batch learning,which is inadequate for wastewater prediction in the era of COVID-19 as the influent pattern changed significantly.Online learning,which has distinct advantages of dealing with stream data,large data set,and changing data pattern,has a potential to address this issue.In this study,the performance of conventional batch learning models Random Forest(RF),K-Nearest Neighbors(KNN),and Multi-Layer Perceptron(MLP),and their respective online learning models Adaptive Random Forest(aRF),Adaptive K-Nearest Neighbors(aKNN),and Adaptive Multi-Layer Perceptron(aMLP),were compared for predicting influent flow rate at two Canadian WWTPs.Online learning models achieved the highest R2,the lowest MAPE,and the lowest RMSE compared to conventional batch learning models in all scenarios.The R2 values on testing data set for 24-h ahead prediction of the aRF,aKNN,and aMLP at Plant A were 0.90,0.73,and 0.87,respectively;these values at Plant B were 0.75,0.78,and 0.56,respectively.The proposed online learning models are effective in making reliable predictions under changing data patterns,and they are efficient in dealing with continuous and large influent data streams.They can be used to provide robust decision support for wastewater treatment and management in the changing era of COVID-19 and also under other unprecedented emergencies that could change influent patterns.
文摘The prediction of the influent load is of great importance for the improvement of the control system to a large wastewater treatment plant. A systematic data analysis method is presented in this paper in order to estimate and predict the periodicity of the influent flow rate and ammonia （NH3） concentrations： 1） data filtering using wavelet decomposition and reconstruction; 2） typical cycle identification using power spectrum density analysis; 3） fitting and prediction model establishment based on an autoregressive model. To give meaningful information for feedforward control systems, predictions in different time scales are tested to compare the corresponding predicting accuracy. Considering the influence of the rainfalls, a linear fitting model is derived to estimate the relationship between flow rate trend and rain events. Measurements used to support coefficient fitting and model testing are acquired from two municipal wastewater treatment plants in China. The results show that 1） for both of the two plants, the periodicity affects the flow rate and NH3 concentrations in different cycles （especially cycles longer than 1 day）; 2） when the flow rate and NH3 concentrations present an obvious periodicity, the decreasing of prediction accuracy is not distinct with increasing of the prediction time scales; 3） the periodicity influence is larger than rainfalls; 4） the rainfalls will make the periodicity of flow rate less obvious in intensive rainy periods.
基金This research was supported by the Natural Science Foundation of China(Grant No.50378094).
文摘Shortcut nitrification-denitrification,anaerobic ammonium oxidation(ANAMMOX),and methanogenesis have been successfully coupled in an Expanded Granular Sludge Bed-Biological Aerated Filter(EGSB-BAF)integrated system.As fed different synthetic wastewater with chemical oxygen demand(COD)of 300-1200 mg·L^(-1)and NH_(4)^(+)-N of 30-120 mg·L^(-1)at the outer recycle ratio of 200%,the influence of influent on ANAMMOX in the integrated system was investigated in this paper.The experimental results showed that higher COD concentration caused an increase in denitrification and methanogenesis but a decrease in ANAMMOX;however,when an influent with the low concentration of COD was used,the opposite changes could be observed.Higher influent NH_(4)^(+)-N concentration favored ANAMMOX when the COD concentration of influent was fixed.Therefore,low COD=NH_(4)^(+)-N ratio would decrease competition for nitrite between ANAMMOX and denitrification,which was favorable for reducing the negative effect of organic COD on ANAMMOX.The good performance of the integrated system indicated that the bacterial community of denitrification,ANAMMOX,and methanogenesis could be dynamically maintained in the sludge of EGSB reactor for a certain range of influent.
基金The authors would like to thank Sharon Grant,Jake O'Brien,Ben Tscharke and Rachel Mackie for organizing sample collection and providing data.Hue T.Nguyen is also grateful to Christine M.Baduel for LC/MS-MS analytical training.Hue T.Nguyen is supported by an Australian Award Scholarship granted by Australian Department of Foreign Affairs and Trade.Jochen F.Mueller is funded by a UQ Fellowship.
文摘Per-and polyfluoroalkyl substances(PFAS)are found ubiquitously in wastewater treatment plants(WWTPs)due to their multiple sources in industry and consumer products.In Australia,limited spatial data are available on PFAS levels inWWTPs influent,while no temporal data have been reported.The aim of this study was to investigate the occurrence and temporal trend of PFAS in the influent of two large WWTPs in Australia(WWTP A and B)over a four-year period.Daily influent samples were collected over one week at different seasons from 2014 to 2017.Eleven perfluoroalkyl acids(PFAA)(i.e.seven perfluoroalkyl carboxylic acids(PFCAs)and four perfluoroalkyl sulfonic acids(PFSA))were detected with mean S11PFAA concentrations of 57±3.3e94±17 ng/L at WWTP A,and 31±6.1e142±73 ng/L at WWTP B.The highest mean concentrations were observed for perfluorohexanoate(PFHxA)(20±2 ng/L)in WWTP A,and perfluorooctane sulfonate(PFOS)(17±13 ng/L)in WWTP B.The precursor 6:2 fluorotelomer sulfonate was detected over five sampling periods from Aug 2016 to Oct 2017,with mean concentrations of 37±18e138±51 ng/L for WWTP A and 8.8±4.5e29±5.1 ng/L for WWTP B.Higher concentration of 6:2 FTS(1.8e11 folds)than those of PFOA and PFOS in WWTP A indicate a likely substitution of C8 PFAA by fluorotelomer-based PFAS in this catchment.Temporal trends(annual and seasonal)in per-capita mass load were observed for some PFAA,increasing for PFPeA,PFHxA,PFHpA,PFNA,and PFHxS,while decreasing for PFBS and PFOS in either WWTPs.Notably,elevated levels of PFOS in October 2017 were observed at both WWTPs with the highest per capita mass load of up to 67 mg/day/inhabitant.For some PFAS release trends,longer sampling periods would be required to achieve acceptable statistical power.
基金The work was supported by the Natural Science Foundation of China for Distinguished Young Scholars(Grant Nos.50225926 and 50425927)the National High-Tech Research and Development(863)Program of China(Grant No.2004AA649370)+1 种基金the Teaching and Research Award Program for Excellent Youth Teachers in Higher Education Institu-tions of MOE,China(TRAPOYT)in 2000the Specialized Research Fund for the Doctoral Program of Higher Education of Ministry of Education of China(Grant No.20020532017).
文摘By predicting influent quantity,a wastewater treatment plant(WWTP)can be well controlled.The non-linear dynamic characteristic of WWTP influent quantity time series was analyzed,with the assumption that the series was predictable.Based on this,a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction.Reasonable forecasting results were achieved using this method.
