Anaerobic tapered fluidized bed reactor for starch wastewater treatment and modeling using multilayer perceptron neural network

Anaerobic treatability of synthetic sago wastewater was investigated in a laboratory anaerobic tapered fluidized bed reactor(ATFBR) with a mesoporous granular activated carbon(GAC)as a support material.The experimental protocol was defined to examine the effect of the maximum organic loading rate(OLR),hydraulic retention time(HRT),the efficiency of the reactor and to report on its steady- state performance.The reactor was subjected to a steady-state operation over a range of OLR up to 85.44 kg COD/(m~3.d).The COD removal efficiency was found to be 92% in the reactor while the biogas produced in the digester reached 25.38 m~3/(m~3·d) of the reactor. With the increase of OLR from 83.7 kg COD/(m~3.d),the COD removal efficiency decreased.Also an artificial neural network(ANN) model using multilayer perceptron(MLP)has been developed for a system of two input variable and five output dependent variables. For the training of the input-output data,the experimental values obtained have been used.The output parameters predicted have been found to be much closer to the corresponding experimental ones and the model was validated for 30% of the untrained data.The mean square error(MSE)was found to be only 0.0146.     

事故致因的突变模型

在众多论述伤亡事故致因的理论中，都没有解释为何系统元素的连续变化引起系统状态的突变（事故），以及人、物因素在这种突变过程中的作用。本文引入突变理论这一数学工具，指出系统内某些元素的连续变化导致系统状态的突然改变，较好地解决了这个问题，通过建立事故致因突变模型，提出了提高系统安全性的原则。     

活性污泥1#数学模型(ASM1)中的组分

活性污泥l#数学模型(AMl)常常用于预测污水处理厂中的生物过程。活性污泥数学模型组分的鉴定对活性污泥l#数学模型的应用具有重要的意义。本文描述了在活性污泥l#模型(ASMl)中污水组分的概念,以及组分鉴定的研究现状。     

复合养殖系统中浮游动物种类丰度及其影响因素

利用人工湿地和养殖塘构建了复合养殖系统,通过设置不同塘间水质差异,调查了斑点叉尾鮰鱼鱼种培育段和成鱼养殖段不同梯度下的浮游动物种类丰度,并探讨了种类丰度与环境因子的关系.结果表明,2养殖阶段的循环塘中,轮虫的种类丰度随养殖密度的增加而升高,浮游甲壳动物随养殖密度的增加而降低.成鱼养殖阶段对照塘中轮虫的种类丰度高于循环塘P1、P2;枝角类和桡足类的丰度则低于P1、P2;高养殖密度塘P3中,3种形态浮游动物种类丰度均低于对照塘.15种环境因子的主成分分析以及种类丰度与环境因子的冗余度分析结果表明,影响浮游动物种类丰度的主要环境因素为水体的营养状态,水体的物理特征对种类丰度的影响较小.     

Integrating Paleobiology,Archeology, and History to Inform Biological Conservation

The search for novel approaches to establishing ecological baselines (reference conditions) is constrained by the fact that most ecological studies span the past few decades, at most, and investigate ecosystems that have been substantially altered by human activities for decades, centuries, or more. Paleobiology, archeology, and history provide historical ecological context for biological conservation, remediation, and restoration. We argue that linking historical ecology explicitly with conservation can help unify related disciplines of conservation paleobiology, conservation archeobiology, and environmental history. Differences in the spatial and temporal resolution and extent (scale) of prehistoric, historic, and modern ecological data remain obstacles to integrating historical ecology and conservation biology, but the prolonged temporal extents of historical ecological data can help establish more complete baselines for restoration, document a historical range of ecological variability, and assist in determining desired future conditions. We used the eastern oyster (Crassostrea virginica) fishery of the Chesapeake Bay (U.S.A.) to demonstrate the utility of historical ecological data for elucidating oyster conservation and the need for an approach to conservation that transcends disciplinary boundaries. Historical ecological studies from the Chesapeake have documented dramatic declines (as much as 99%) in oyster abundance since the early to mid‐1800s, changes in oyster size in response to different nutrient levels from the sixteenth to nineteenth centuries, and substantial reductions in oyster accretion rates (from 10 mm/year to effectively 0 mm/year) from the Late Holocene to modern times. Better integration of different historical ecological data sets and increased collaboration between paleobiologists, geologists, archeologists, environmental historians, and ecologists to create standardized research designs and methodologies will help unify prehistoric, historic, and modern time perspectives on biological conservation. Integración de Paleobiología, Arqueología e Historia para Informar a la Biología de la Conservación     

隧道爆破振动影响因素的灰色关联分析

为优化隧道爆破方案,降低爆破振动危害,采用灰色关联分析法(GRA)对爆破振动的影响因素进行分析。首先确定振动速度、主振频率和振动持续时间作为系统特征变量,确定总药量、最大段药量、掏槽孔最大段药量、雷管段数、爆心距、最小延期间隔时间、临空面数等作为相关因素变量;再结合厦门某隧道爆破实测数据,进行灰色关联计算;然后对计算结果进行排序和优势分析,得到爆破振动影响因素的主次顺序,其中准优因素为爆心距,可控准优因素为雷管段数和掏槽眼最大段药量;最后对隧道爆破参数进行优化,测点爆破振速由1.2 cm/s减小到0.74 cm/s,降振率达38.3%。结果表明,采用GRA确定爆破振动主要影响因素,为有效控制爆破振动提供理论依据。     

尖点突变模型在研究洪水引起环境突变中的应用

以1998年霍林河特大洪水为例，在进行洪水对环境影响作用分析的基础上，将洪水发生的危险系数、地面高程、地形坡度、降雨影响程度、土地盐碱率和草地开垦率6个控制变量划分为两个主分量。由于洪水引起的环境突变可以归纳为一种耦合突变，我们采用尖点突变模型进行求解，得出各主分量的数学表达式和水环境突变区域边界，绘制了该流域环境突变分区图，论证了应用尖点突变模型研究环境突变的合理性。     

渤海湾大型底栖生物群落结构变化及原因探讨

根据近6年来的渤海湾大型底栖生物群落的资料,以及2011年的现场调查数据,从渤海湾底栖生物群落结构的变化特征出发,分析了其变化的过程和规律.同时结合渤海湾水域近几年来的水动力条件以及营养盐、溶解氧等水质特征变化,探讨底栖生物群落变化的原因.结果表明,渤海湾大型底栖生物的物种数、生物量、丰度、丰富度指数、多样性指数的高值区皆集中分布在海河口北侧离岸较远的海域,而低值区则基本集中分布近岸海域,且呈现由近岸向外海逐渐增加的趋势.渤海湾大型底栖生物群落变化可大体分为3个明显的时间节点:第一个时间节点为2005年和2008年,底栖生物群落无论总物种数、生物量和丰度都较高;第二个时间节点为2009年和2010年,底栖生物群落的上述指标值最低,表明这两年渤海湾受自然变化和人为因素干扰的程度加剧;2011年,由于渤海湾水域的各项强有力的环保措施的实施,底栖生物群落的上述指标值最高,说明其得到一定程度的恢复.渤海湾水域底栖生物群落变化受到该区域的物理因素、化学因素和生物因素的共同影响,变化特征也与各环境因素的变化基本吻合.     

基于PMF模型的土壤重金属源解析中变量敏感性研究

为探究应用受体模型对土壤污染物进行源解析,输入变量对模型运行及其结果的影响,以乐安河中上游地区土壤重金属调查数据作为典型受体模型（PMF模型）的输入数据集,并在PMF模型基础方案运行结果的基础上,采用局部敏感性分析法来探讨输入变量变化对模型诊断及源识别结果的影响.结果表明：6因子数情景是研究区土壤重金属源解析PMF模型最佳运行结果;土壤中Cu、Mo、Na₂O、As、Mn和Cd等参数属于敏感变量,这些变量均为每个因子中的主要载荷元素,即每个源的特征污染物;不同变量的敏感性有较大差异,Cu、Mo的总敏感性最大,分别为12.1,8.2,大于其他输入变量的敏感性.因此,在应用PMF模型进行源解析时,特征污染物是敏感性较强的变量,其数据质量的优劣是影响源解析结果可靠性的重要因素.     

Environmental risk perception, environmental concern and propensity to participate in organic farming programmes

This paper aims to identify the factors underlying farmers' propensity to participate in organic farming programmes in a Romanian rural region that confronts non-point source pollution. For this, we employ structural equation modelling with latent variables using a specific data set collected through an agri-environmental farm survey in 2001. The model includes one 'behavioural intention' latent variable ('propensity to participate in organic farming programmes') and five 'attitude' and 'socio-economic' latent variables ('socio-demographic characteristics', 'economic characteristics', 'agri-environmental information access', 'environmental risk perception' and 'general environmental concern'). The results indicate that, overall, the model has an adequate fit to the data. All loadings are statistically significant, supporting the theoretical basis for assignment of indicators for each latent variable. The significance tests for the structural model parameters show 'environmental risk perception' as the strongest determinant of farmers' propensity to participate in organic farming programmes.