Simulation and control strategy for the variational influent of WWTP

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.

Feedback Between Influent Condition Heterogeneity and Diversity of Bacterial Community in Bioreactor at Low Substrate Concentration

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.

Daily Influent Quantity Forecasting Method for Sewage Treatment Plant Considering Uncertain Factors

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.

Application of genetic algorithm for influent flow distribution in the four stage A/O biological nutrient removal system

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.

Natural continuous influent nitrifier immigration effects on nitrification and the microbial community of activated sludge systems

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.

Evaluation of the k-nearest neighbor method for forecasting the influent characteristics of wastewater treatment plant

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.

Characteristic and correlation analysis of influent and energy consumption of wastewater treatment plants in Taihu Basin

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.

Short-term prediction of influent flow rate and ammonia concentration in municipal wastewater treatment plants

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.

Influence of influent on anaerobic ammonium oxidation in an Expanded Granular Sludge Bed-Biological Aerated Filter integrated system

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.

Distribution,characteristics and daily fluctuations of microplastics throughout wastewater treatment plants with mixed domestic-industrial influents in Wuxi City,China

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.

Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

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.

Temporal trends of per-and polyfluoroalkyl substances(PFAS)in the influent of two of the largest wastewater treatment plants in Australia

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.

Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model

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.

进水分配比对分段进水PN/A工艺脱氮性能影响

以预脱碳污水和市政污水作为目标污水,建立了好氧段分段进水的新模式,通过调整进水分配比来优化好氧段C/N,以期实现短程硝化-厌氧氨氧化(PN/A)工艺在实际工况下的稳定运行.采用4组以好氧/缺氧/好氧/缺氧(O/A/O/A)模式运行的序批式反应器(SBR),R1、R2、R3及R4以进水分配比(预脱碳污水:市政污水)分别为1:0、2:1、1:1及1:2的分段进水模式运行.结果表明,R1未能实现PN/A工艺,总氮去除率(NRE)仅为60.2%.随着进水分配比不断下降,R2、R3、R4分别在第34,30及36d实现PN/A工艺,NRE分别达到71.8%、80.3%及74.1%.其中R3系统处理性能最好且最稳定,厌氧氨氧化贡献率达到83.9%.污泥沉降性与粒径最佳.此外,高通量结果显示氨氧化菌(Nitrosomonas)和厌氧氨氧化菌(Candidatus_Kuenenia)丰度分别为2.74%和12.17%,且与好氧异养菌(AHB)形成了良好的协同作用.因此,分段进水模式在进水分配比为1:1时有利于实现稳定的PN/A工艺,此时各功能微生物可得到较好富集与平衡.

慢性肌肉骨骼疼痛患者恐动的影响因素及列线图预测模型的构建和验证

目的探讨慢性肌肉骨骼疼痛(CMP)患者恐动的影响因素,依据影响因素构建列线图预测模型并进行验证。方法前瞻性选取2021年3月至2023年10月入上海市第八人民医院的CMP患者419例为研究对象,按照随机数字表法将其分为模型组(n=319)和验证组(n=100)。调查影响CMP患者发生恐动的影响因素,依据影响因素建立列线图模型,并使用受试者操作特征(ROC)曲线验证模型的预测效果。结果模型组319例CMP患者中恐动患者113例,占比41.69%;非恐动患者186例,占比58.31%。多因素Logistic回归分析结果显示,年龄、合并症数量(2种及以上)、疼痛部位数量(3个及以上)、疼痛干扰(重度)、TSK~11量表得分、心理弹性得分、疼痛程度得分、疼痛灾难化得分和一般自我效能得分为CMP恐动的影响因素(P<0.05)。预测CMP患者恐动发生的一致性指数为0.853,95%CI:0.819~0.946;校准曲线显示,CMP患者恐动列线图预测模型预测恐动的效能较好;ROC曲线分析结果显示,曲线下面积为0.971,95%CI:0.903~0.939,敏感度为91.75%,特异度为87.43%,约登指数为0.796。验证组Hosmer-Lemeshow检验结果显示,该模型对恐动的预测值与实际值之间差异无统计学意义(P>0.05),模型具有较好的拟合度。结论年龄、合并症数量、疼痛部位数量、疼痛干扰、心理弹性、疼痛程度、疼痛灾难化和一般自我效能得分是CMP恐动的影响因素,据此构建的CMP患者恐动的列线图预测模型具有较好的区分度和拟合度。

顶/底部变频交替进水的厌氧氨氧化系统特性

针对厌氧氨氧化污泥颗粒化过程较慢且易受水力剪切力和基质浓度波动的影响,颗粒污泥的传质性能随着粒径的增长逐渐恶化,进而导致系统脱氮性能受限问题,提出一种顶/底部变频交替进水运行模式,通过延长进水时间,并增设回流提供充足水力剪切力,改变顶/底部交替进水位置及频次,有效降低了基质浓度对厌氧氨氧化菌的冲击负荷,强化了系统脱氮性能.探究了系统运行期间污泥浓度、颗粒性能、EPS分泌和脱氮稳定性等变化情况.结果表明,该进水模式下总氮去除率达84.80%,颗粒平均粒径为783.67μm,EPS含量为169.13mg/gVSS.增加进水负荷后,游离氨最高浓度为9.70mg/L,未对系统造成抑制作用.显微镜结果显示,该进水模式下系统内颗粒颜色较深,血红素c含量较高,AnAOB活性较佳.高通量测序结果表明,顶/底部变频交替进水模式能够大幅提高系统内厌氧氨氧化菌属Candidatus_Brocadia的丰度,当交替频次为3次时达43.59%的最高占比.该进水方式加快了污泥与底物的混合,缩短了颗粒化进程,同时提升了污泥活性和抗冲击负荷能力,实现了厌氧氨氧化系统的稳定运行.

以丝瓜络为碳源的固相反硝化系统性能

构建以丝瓜络为碳源的固相反硝化系统,探究不同水力停留时间(HRT)和进水硝酸盐浓度(INC)下该系统的反硝化性能,为丝瓜络作为水产养殖尾水反硝化碳源的工艺优化提供理论依据。以丝瓜络(LS)为一维反硝化反应器(denitrification reactor,DR)外加碳源,在流场环境下,测定不同HRT(16、20、24和28 h)和INC(50、75、100和125 mg/L)下反硝化系统对硝酸盐氮(NO_(3)^(-)-N)、亚硝酸盐氮(NO_(2)^(-)-N)、氨氮(NH_(4)^(+)-N)、总氮(TN)、总磷(TP)和化学需氧量(CODcr)的去除效果。并采用高通量测序技术对丝瓜络反硝化反应器(LS-DR)在运行初期和末期时的细菌群落结构进行分析。当INC为50 mg/L,HRT为24h时,LS-DR对NO_(3)^(-)-N和TN均有较好的去除效果,去除率分别为98.97%±0.52%和97.84%±0.94%,此时出水NO_(2)^(-)-N浓度也达到较低水平(小于0.5 mg/L);在HRT为24 h的基础上,当INC增加至75、100和125 mg/L时,其NO_(3)^(-)-N去除率和NO_(3)^(-)-N去除速率(NRR)均随INC的增加而显著增加,出水COD则随INC的增加而降低,但均未实现完全反硝化,然而,LS-DR在整个实验期间均能完全去除NH_(4)^(+)-N;扫描电镜结果显示,丝瓜络表面结构有利于微生物附着生长;高通量测序结果显示,LS-DR的优势菌门包括变形菌门、拟杆菌门、弯曲杆菌门、厚壁菌门和疣微菌门;被鉴定的优势菌属中热单胞菌属(1.46%)、陶厄氏菌属(0.55%)、固氮螺菌属(3.32%)、Simplicispira(1.01%)、假黄色单胞菌属(0.39%)、草螺菌属(3.02%)和Uliginosibacterium(0.9%)主要参与反硝化的进行,Cytophaga xylanolytica(1.61%)和Cloacibacterium(2.69%)主要参与了丝瓜络的降解,黄杆菌属(1.17%)和Diaphorobacter(0.64%)既能进行反硝化,也能降解丝瓜络。LS-DR的最佳HRT为24 h,最适宜的INC为50 mg/L。本研究为丝瓜络固相反硝化工艺的优化提供了理论基础,为开发新型缓释碳源在养殖尾水处理中的应用提供参考。

菌藻生物膜反应器处理养猪废水机制研究

利用菌藻共生生物膜系统处理畜禽养殖废水,反应器是关键的工序和重要的限制因素。设计菌藻生物膜反应器运行处理养猪废水,通过调节进水氨氮质量浓度探究其对污染物去除效率及微藻生物量的影响,扫描电镜观察载体上菌藻生物膜的形成情况,然后利用高通量测序进一步探究微生物群落的变化,从而揭示菌藻生物膜污水处理系统的污染物去除规律和微生物响应机制。结果表明,当进水氨氮质量浓度为200 mg/L时,氨氮的日去除速率最高可达到35.60 mg/(L·d),并在载体上观察到大量微藻和细菌的团簇体,表明菌藻生物膜形成,优势菌属为Prevotella_9、Clostridium_sensu_stricto_1、Acinetobacter和Bifidobacterium;当进水氨氮质量浓度为300 mg/L时,氨氮的日去除速率显著提高,最高可达到45.70 mg/(L·d),但持续的高氨氮负荷会对菌藻生物膜造成一定冲击,优势菌属演替为Comamonas和Christensenellaceae_R-7_group。

A^(2)/O+反硝化深床滤池在城镇污水处理厂深度经济脱氮的运行优化

脱氮是当前城镇污水处理的重点,其中A^(2)/O+反硝化滤池工艺是生活污水处理主流工艺,四川省岷江流域某城镇污水处理厂通过优化进水流量分配比、调节溶解氧和内回流比、碳源筛选等工艺试验,综合考察了工艺调控措施对系统脱氮效能的提升以及脱氮成本的经济化控制。结果表明,A^(2)/O池进水100%进入厌氧区、内回流比200%、A^(2)/O池末端溶解氧控制在1.5 mg/L、反硝化深床滤池投加液体复合碳源,在有效深度脱氮的同时节约运行费用,总氮指标满足岷沱江标准,脱氮费用降低0.37元/m^(3)。

高原生境下人工配水和实际污水处理效率比较研究

高原条件下的污水处理效能有其自身特点且缺乏系统性研究,污水处理过程中各指标含量的随机变化可能带来的耦合性影响研究更属空白,实际污水中各指标含量的不确定性极大阻碍了该领域的研究,采用人工配水可以较好的解决该问题,但人工配水与实际污水之间不同的物质构成是否会影响高原生境下的污水处理效果又成为系统研究高原生境下污水处理效能的制约因素。为此,本文以高海拔A^(2)O工艺为研究对象,采用控制变量的方法,开展对该因素的初步研究,即对高原生境下人工配水和实际污水的处理效率进行研究。研究结果表明:高原生境条件下的实际污水的处理效果总体优于人工配水且呈现出明显的区域性差异,出水水质显著高于平原地区;人工配水在各项污染物的处理效能方面呈现出较为明显的不稳定性。