城市下水道污水水质模型的开发与应用

建立了下水道水质转化概念模型,以ASM3(活性污泥3号模型)为基础开发了下水道污水水质数学模型,通过模拟试验,运用遗传算法和曲线拟合技术进行了模型率定与参数估值.模型经现场试验验证可较好地模拟下水道中的ρ(DO)与ρ(TOC)的变化.应用该模型进行数值模拟,探讨了初始ρ(DO)和水力停留时间(HRT)等可控因素对下水道中微生物作用及有机质降解的影响.结果表明,在下水道中设置曝气点,可提高污水的ρ(DO),能有效地提高微生物增殖速率,强化有机物的生化降解能力.      

循环式活性污泥法中好氧选择区的运行条件研究

研究了循环式活性污泥法好氧生物选择区的停留时间和整个系统中泥龄、污泥负荷、曝气时间等运行参数对选择区内微生物吸附性能的影响。结果表明 ,选择器停留时间 3 0 min,系统泥龄 9～ 15 d,曝气 2 h,污泥有机负荷为 0 .3 g CODCr/(g ML SS·d)左右时 ,选择器中微生物的吸附性能最优 ,可去除进水中 90以上的 CODCr。     

Application of a new type of moving bio-film in aerobic sequencing batch reactor (aerobic-SBR)

A moving bio-film (MB), made from the inner tube of used tyres was applied in a conventional-aerobic-SBR for increasing the system efficiency and quality of bio-sludge due to good sedimentation (the density of 1.925+/-0.21 g/cm(3)), non-biodegradability and re-usability of the media without any regeneration. The total bio-sludge mass of the MB-aerobic-SBR was about 30% higher than that of the conventional-aerobic-SBR resulting in a reduction of the F/M value of the system and amount of suspended bio-sludge waste. The amount of suspended bio-sludge waste, SVI and SRT of the MB-aerobic-SBR under a low organic loading of 80+/-9.3g BOD(5)/m(3)-d were 1,485+/-146 mg/d, 51+/-3.7 ml/g and 10.1+/-5.1 days, respectively while they were 1,800+/-152 mg/d, 69+/-4.0 ml/g and 8.3+/-5.3 days, respectively in the conventional-aerobic-SBR. The BOD(5), TKN and TP removal efficiencies of the MB-aerobic-SBR were about 1-2, 2-3 and 10-12% higher, respectively, than that of the conventional-aerobic-SBR. Also, the BOD(5) and COD removal efficiencies of the MB-aerobic-SBR were higher than 95% even when the system was operated with synthetic wastewater containing 800 mg/l BOD(5) under a very low HRT of 1.5 days (organic loading of 528+/-50.8 g BOD(5)/m(3)-d). The effluent BOD(5), COD, total kjeldahl nitrogen, total phosphorus and suspended solids of the MB-aerobic-SBR under a high organic loading of 528+/-50.8 g BOD(5)/m(3)-d were 45+/-5.1, 37+/-3.6, 4.1+/-1.0, 1.5+/-0.80 and 41+/-2mg/l, respectively.     

The application of potassium ferrate for sewage treatment

The comparative performance of potassium ferrate(VI), ferric sulphate and aluminium sulphate for the removal of turbidity, chemical oxygen demand (COD), colour (as Vis400-abs) and bacteria in sewage treatment was evaluated. For coagulation and disinfection of sewage, potassium ferrate(VI) can remove more organic contaminants, COD and bacteria in comparison with the other two coagulants for the same doses used. Also, potassium ferrate(VI) produces less sludge volume and removes more contaminants, which should make subsequent sludge treatment easier.     

城市污水处理系统去除药物和个人护理用品（PPCPs）的机理研究

在简单介绍药物和个人护理用品（PPCPs）的主要来源和进入水体途径的基础上,本文对PPCPs在城市污水处理系统中的去除机理进行了详细的分析探讨,主要包括污泥吸附、生物转化和气提;并对其它去除机理如膜分离技术、气浮等进行了简要介绍。     

长江三角洲地区城市污水污泥重金属含量研究

通过现场采样及室内分析,研究了长江三角洲地区南京、苏州、上海、杭州等15个城市的污水处理厂44份污泥样品中6种重金属元素(Cu、Zn、Pb、Cd、Cr、Ni)的含量。结果表明,不同污水处理厂的污泥样品中重金属元素的总量存在较大的差异,6种重金属元素平均含量的大小顺序为Zn>Cu>Ni>Cr>Pb>Cd。对各类型污泥重金属污染指数比较表明,工业污水为主城市污泥中重金属污染程度明显高于生活污水为主污泥和混流污水污泥。用主成分分析法提取的3个主成分较好地反映了6个重金属指标的信息,累积贡献率为82.0%,第一主成分能解释接近50%的信息量,它主要反映各金属在污泥中均有不同程度的富集。为了保护生态环境和人类健康,防止二次污染,污泥土地利用时需考虑重金属含量,采取相应控制措施。     

Impact of PCB-118 and transformer oil toxicity on anaerobic digestion of sludge: Anaerobic toxicity assay results

In this study, possible toxicity of increasing doses of PCB-118 and transformer oil (TO) on anaerobic sludge digestion was investigated. For this purpose, five different sets of reactors were prepared in which four different PCB-118 concentration (1, 10, 20, and 30 mg L⁻¹) and three different TO concentration (0.38, 0.76, and 1.52 g L⁻¹) were applied. Throughout the study, biogas production and composition, pH, TS, VS, and COD as well as PCB concentration were monitored. Toxicity was investigated by anaerobic toxicity assay (ATA) evaluating the reduction in methane production. A notable inhibition was observed mostly in 30 mg L⁻¹ PCB reactors. A negative influence of PCB-118 and TO was observed on COD and solids removal. A maximum of 26.5% PCB-118 removal was attained.     

The use of state workers' compensation administrative data to identify injury scenarios and quantify costs of work-related traumatic brain injuries

PROBLEM: Traumatic brain injury (TBI) is a public health problem but little is known about the nature of that problem in the working population. METHOD: The author used a national definition to identify cases in Washington State from workers' compensation (WC) hospital billing data, quantified the cost of WC insurance benefits using actuarial cost estimates, and identified high risk industries using ANSI Z16.2 typology. RESULTS: There were 928 cases of TBI with a lifetime claim cost of $159 million from the Washington State Fund (1994-2001). Sixty percent of injuries resulted in death or disability. The highest risks of TBI are concentrated in 16 industrial insurance risk classes and the highest costs in 19 North American Industry Classification codes. Injury scenarios were identified for nine industrial insurance risk classes. CONCLUSIONS: TBI is a disabling and costly workplace injury in the state of Washington, affecting even teenagers and seniors who are not generally considered to be part of the workforce. Injury typology codes provide useful information for improving workplace safety. IMPACT ON INDUSTRY: This research provides industry with quantitative information regarding the cost of work-related traumatic brain injury and the usefulness of using workers' compensation claims data to reduce the burden of workplace injury.     

Modeling and optimization of activated sludge bulking for a real wastewater treatment plant using hybrid artificial neural networks-genetic algorithm approach

Prediction of sludge bulking is a matter of growing importance around the world. Sludge volume index (SVI) should be monitored to predict sludge bulking for a wastewater treatment plant. This study was an effort to develop hybrid artificial neural network-genetic algorithm models (MLPANN-GA and RBFANN-GA) to accurately predict SVI. Operating parameters, including MLVSS, pH, DO, temperature, TSS, COD and total nitrogen were the inputs of neural networks. Genetic algorithm was utilized in order to optimize weights and thresholds of the MLPANN and RFBANN models. Training procedures for SVI estimation were successful for both the MLPANN-GA and RBFANN-GA models. The training and validation models showed an almost perfect match between experimental and predicted values of SVI. The results indicated that with low experimental values of input data to train ANNs, the MLPANN-GA compared with the RBFANN-GA is more accurate due to higher coefficient of determination (R²) and lower root mean squared error (RMSE) values. The values of RMSE and R² for the optimal models approached 0 and 1, respectively. The mean average error for the ANN models did not exceed 3% of the input values of the measured SVI. The GA increased the accuracy of all the MLPANN and RBFANN models.     

Improving anaerobic sewage sludge digestion by implementation of a hyper-thermophilic prehydrolysis step

The present study focuses on a two-step process for treatment and stabilisation of primary sludge. The process consists of a hyper-thermophilic hydrolysis step operated at 70 degrees C and a hydraulic retention time (HRT) of 2 days followed by a thermophilic (55 degrees C) anaerobic digestion step at a HRT of 13 days. A one-step anaerobic digester operated at 55 degrees C and 15 days HRT was used as a reference process. The two-step process was characterized by a 12% higher organic suspended solids removal efficiency and better pathogen reduction effect than the conventional one-step digestion. The microbial community of the digester fed with pre-treated sludge was characterised by a higher activity compared to that of the digester treating raw sludge. Moreover, the pre-treatment of the primary sludge resulted up to 48% increase of the methane potential (20.09 and 13.56mmolCH(4)g(-)VS(-1) with and without pre-treatment, respectively) and up to 115% increase of the methane production rate. Finally it was shown that the extra energy requirements for the operation of a pre-treatment step would be covered by the energy produced from the extra methane production and in addition there would be a significant energy surplus of 2.17kJd(-1) for the system tested.