好氧/除磷颗粒对亚硝化颗粒污泥启动的影响

在室温下,采用R1、R2、R3三组相同的SBR反应器接种污水厂回流污泥,比较了添加好氧颗粒、除磷颗粒对亚硝化颗粒污泥启动及稳定运行的影响.结果表明,在S1(0~22 d)阶段,R1、R2、R3均用了19 d启动亚硝化.在S2阶段(23~56d),R1不添加颗粒污泥,R2、R3分别添加20%好氧颗粒和20%除磷颗粒诱导亚硝化絮状污泥颗粒化,R1、R2和R3分别在76、42 d和56 d平均粒径达到412、468、400μm,均成功实现颗粒化.在S3阶段(57~108 d),进水氨氮负荷和COD负荷分别由0.4 kg·(m~3·d)~(-1)提高到0.5 kg·(m~3·d)~(-1)和0.2 kg·(m~3·d)~(-1)提高到0.5 kg·(m~3·d)~(-1),R1、R2反应器中颗粒粒径增加明显,但R3发生了污泥膨胀.在运行末期(108 d),R1和R2的平均粒径分别达到689μm和893μm.接种好氧颗粒和除磷颗粒均能快速实现亚硝化污泥的颗粒化,并且接种好氧颗粒的亚硝化颗粒污泥系统能适应较高C/N比进水,耐冲击负荷,能长期稳定运行.     

一株Acinetobacter johnsonii的部分反硝化特性及动力学研究

从实验室A²/O小试设备中分离纯化出一株具有高亚硝酸盐氮（NO₂^--N）积累率的反硝化菌株ZY04,经过16SrDNA鉴定和基因比对后,初步鉴定为Acinetobacter johnsonii.使用Logistic模型可以很理想地拟合菌株ZY04的生长特性曲线,得到生长方程常数a=0.6588,b=24.08,k=0.2413.在维持初始基质中硝酸盐氮（NO₃^--N）浓度为100mg/L的条件下,改变碳源乙酸钠的浓度,使碳氮比（TOC/TN）为3.5,4.5,5.5,6.5,研究菌株ZY04部分反硝化性能,发现该菌株在不同碳氮比条件下均能够保持95%以上的NO₃^--N降解率,在碳氮比为3.5和4.5时,17h后NO₂^--N积累率达到70%以上;在碳氮比为5.5和6.5时,NO₂^--N积累率在更快的11h后达到85%以上,碳氮比为5.5时达到最高NO₂^--N积累率91%.使用Aiba,Edwards和Andrews模型对菌株的基质抑制动力学进行拟合,结果表明,3种模型均可以很好的拟合NO₃^--N和乙酸钠对菌株的单基质抑制动力学,在双基质抑制的9种组合中,有6种模型成功拟合了NO₃^--N和乙酸钠对菌株的双基质抑制动力学,得到了相关半饱和参数和基质抑制参数,相关系数（R²）可以达到98%.     

基于数据分解的AQI的CEEMD-Elman神经网络预测研究

针对Elman神经网络在预测空气质量指数（AQI）时易受到数据非平稳性的影响导致预测趋势良好但准确度较低的问题,提出以互补集合经验模态分解（Complementary Ensemble Empirical Mode Decomposition,CEEMD）为基础的CEEMD-Elman模型.应用CEEMD对AQI序列分解成不同时间尺度上的本征模态函数分量和剩余分量,进而首次将对非平稳的AQI序列的预测研究转化为对多个平稳的本征模态函数分量的研究.分别与Elman单一模型、EMD-Elman模型、BP单一模型及CEEMD-BP模型进行实验对比.结果表明：应用该方法建立的模型的均方误差、平均绝对误差和平均绝对百分比误差分别为4.80、0.71、1.84%,均小于其他模型结果;对应空气质量等级预报正确天数的频率为94.12%.该模型能有效的降低非平稳性对实验预测结果的影响,实现对空气质量等级的准确预报;该研究为进一步预测AQI的走向提供了有效依据,也为政府决策和管理部门制定空气污染控制提供了更充分的参考.     

FNA对短程硝化污泥菌群结构的影响

在SBR反应器增加游离亚硝酸（FNA）预处理单元,投加浓度为1.2mgHNO₂-N/L的FNA进行缺氧搅拌4.5h,连续处理3d,考察短程硝化污泥中FNA对氨氧化菌（AOB）,丝状菌和微生物菌群结构的影响.研究表明,FNA对AOB有短时抑制作用,并能够抑制优势丝状菌Candidatus_Microthrix（微丝菌属）和Cytophagaceae（噬纤维菌）的增殖,分别由5.1%和1.1%下降到0.78%和几乎不可见.SVI从281mL/g降低到100mL/g左右.NAR能够维持在90%左右,短程硝化不受到破坏.高通量结果显示,FNA处理后微生物菌群结构多样性与丰度出现下降,但Thauera（陶厄氏菌属）和Ottowia出现了增殖,分别增加到5.58%和7.82%,同步硝化反硝化（SND）作用明显,这使得即便只有短程硝化,总氮去除率依然能达到60%以上.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

基于偏序集的采空区塌陷危险性评价研究

针对采空区塌陷危险性评价方法中的赋权争议问题,基于偏序集理论提出偏序集评价模型。首先阐明评价指标并确定其分级标准;然后应用偏序集评价模型得到Hasse图,并通过图展现的层集信息判别采空区塌陷危险程度;最后应用该模型对大宝山矿11个采空区的塌陷程度进行判别,评价结果准确合理。研究结果表明:该模型避免了以往研究中的赋权争议问题,克服了样本量不足致使模型无法应用的问题,而且能够应用更多的赋权方法识别排序的稳定程度,体现出样本间的分层信息。     

Pipeline leakage detection and isolation: An integrated approach of statistical and wavelet feature extraction with multi-layer perceptron neural network (MLPNN)

Leakage diagnosis of hydrocarbon pipelines can prevent environmental and financial losses. This work proposes a novel method that not only detects the occurrence of a leakage fault, but also suggests its location and severity. The OLGA software is employed to provide the pipeline inlet pressure and outlet flow rates as the training data for the Fault Detection and Isolation (FDI) system. The FDI system is comprised of a Multi-Layer Perceptron Neural Network (MLPNN) classifier with various feature extraction methods including the statistical techniques, wavelet transform, and a fusion of both methods. Once different leakage scenarios are considered and the preprocessing methods are done, the proposed FDI system is applied to a 20-km pipeline in southern Iran (Goldkari-Binak pipeline) and a promising severity and location detectability (a correct classification rate of 92%) and a low False Alarm Rate (FAR) were achieved.     

Regression analysis of forest inventory data with time and space dependencies

In this paper the data of a forest health inventory are analyzed. Since 1983 the degree of defoliation, together with various explanatory variables (covariates) concerning stand, site, soil and weather, are recorded by the second of the two authors, in the forest district of Rothenbuch (Spessart, Bavaria). The focus is on the space and time dependencies of the data. The mutual relationship of space-time functions and the set of covariates is evaluated. For this we use generalized linear models (GLMs) for ordinal response variables and semiparametric estimation approaches. By using goodness-of-fit measures it turns out that (i) the contribution of space-time functions is quantitatively comparable with that of the set of covariates, (ii) the contribution of space-time functions is small compared with the contribution of a set of variables describing the last-year and neighboring response values. By applying appropriate residual methods a detailed analysis of the individual sites in the area can be carried out. This analysis reveals where the predictive power of the covariates fail to explain the observed defoliation.     

不同驯化方式实现SBR中部分反硝化的对比研究

部分反硝化（NO₃^--N→NO₂^--N）是一种非常具有前景的硝酸盐废水处理方法,其可以结合厌氧氨氧化实现低碳源、低能耗脱氮.本文以葡萄糖为碳源,对比研究3种部分反硝化驯化方式：R1（SBR缺氧）、R2（SBR缺氧-好氧交替运行）、R3（SBR缺氧搅拌伴随低曝气）.经过120d驯化后,对三种反应器污泥进行沿程实验,探索3种反硝化规律.结果显示引入溶解氧驯化后的R2、R3污泥相比R1能够更好地实现部分反硝化,且R3最佳.采用高通量测序技术,对运行109d时R1,R2,R3进行微生物群落结构对比分析.结果显示在有溶解氧参与的R2,R3反应器中的优势菌属为Candidatus Saccharibacteria,相对丰度分别为45.44%和34.96%,这是该菌首次作为反硝化污泥优势菌属被报道.另外R1反应器中微生物多样性要远大于R2,R3,这也从侧面说明R2,R3的反硝化菌更专性.初始pH值对R3驯化污泥影响的批试实验表明,初始pH值对亚硝酸盐积累有明显影响,且初始pH值越高,亚硝酸盐积累越多.     

厌氧/好氧SPNDPR系统实现低C/N城市污水同步脱氮除磷的优化运行

为了解同步短程硝化内源反硝化除磷(SPNDPR)系统的脱氮除磷特性,以低C/N城市污水为处理对象,采用延时厌氧(180 min)/好氧运行的SBR反应器,通过联合调控曝气量和好氧时间,考察了该系统启动与优化运行特性.结果表明,当系统好氧段曝气量为0. 8 L·min~(-1),好氧时间为150 min时,出水PO_4~(3-)-P浓度约为1. 5 mg·L~(-1)左右,出水NH_4~+-N和NO_3~--N浓度由10. 28 mg·L~(-1)和8. 14 mg·L~(-1)逐渐降低至0 mg·L~(-1)和2. 27 mg·L~(-1),出水NO_2~--N浓度逐渐升高至1. 81 mg·L~(-1);当曝气量提高至1. 0 L·min~(-1)且好氧时间缩短至120min后,系统除磷、短程硝化性能逐渐增强,但总氮(TN)去除性能先降低后逐渐升高,最终出水PO_4~(3-)-P、NH_4~+-N分别稳定低于0. 5 mg·L~(-1)和1. 0 mg·L~(-1),好氧段亚硝积累率和SND率分别达98. 65%和44. 20%,TN去除率达79. 78%. SPNDPR系统内好氧段好氧吸磷、反硝化除磷、短程硝化、内源反硝化同时进行保证了低C/N污水的同步脱氮除磷.