基于数学模型的多模式AAO系统运行优化研究

构建了综合考虑出水水质、污泥产量和系统能耗的运行成本指数CPI,并利用数学模型对多模式厌氧/缺氧/好氧(AAO)系统的AAO、倒置AAO(RAAO)和缺氧/好氧(AO) 3种模式进行对比优化研究.在处理成本相近的前提下,在污泥龄5~25d范围内AAO模式的污染物去除效率和聚磷菌浓度明显高于RAAO和AO模式.在运行模式筛选的基础上,通过回流比优化确定了排放标准约束下AAO工艺的达标运行区域和最佳运行工况.动态模拟结果表明,优化工况能够显著改善出水水质,出水高于一级A的时间由78.4%下降至37.7%,CPI降低3.9%.     

基于绝热火焰温度多元混合气体可燃性极限的理论预测

为了预测多元混合气体可燃性极限,通过化学平衡计算软件分析确定了气体在可燃性下限（LFL）和可燃性上限（UFL）的燃烧产物及计算绝热火焰温度（CAFT）,基于能量平衡方程和简化反应模型,分别建立了混合气体LFL和UFL预测模型。应用该预测模型对CH4、C2H4、C3H8、C3H6和CO组成的不同比例混合气体可燃性极限进行预测。结果表明:简化反应模型对于LFL和UFL预测值与文献中实验值的平均相对误差分别为2.76%和5.45%,相关系数分别为0.995和0.950;同时发现两步简化模型对含有C2H4和CO混合组分预测结果误差较大,但对于平均碳原子数大于2的混合气体,预测结果一致性较好。     

保定电厂冲灰水下渗污染地下水的数学模型

在对保定电厂储灰场冲灰水、地下水化学成分和环境水地质条件分析的基础上，运用一维均质数学模型，对灰水下渗污染地下水的状况进行了数值模拟预测。结果表明，灰水下渗已造成储灰场周围地下水污染，但短时间内不会对距储灰场5000m处的保定市水源地水质造成影响。     

Effectiveness of multiple release sites in reintroduction of Persian fallow deer

Releasing animals in more than one location may increase or decrease the probability of success of a reintroduction project, yet the question of how many release sites to use has received little attention. We used empirical data from the reintroduction program of the Persian fallow deer (Dama mesopotamica) (Galilee region in northern Israel) in an individual-based spatially explicit simulation model to assess the effects of releasing deer from multiple sites. We examined whether multiple release sites increase reintroduction success, and if so, whether the optimal number of sites for a given scenario can be determined and whether the outcome differs if animals are released alternately (i.e., the location of the release alternates yearly between sites) or consecutively (i.e., one release site is used for several years, then another is used, and so forth). We selected 8 potential release sites in addition to the original site and simulated the release of 180 individuals at a rate of 10 individuals per year in different combinations of the original site and 1-4 additional sites. In our model, releasing animals into the wild at multiple sites produced higher population growth and greater spatial expansion than releasing animals at only one site and a consecutive-release approach was superior to an alternate-release approach. We suggest that through the use of simulation modeling that is based on empirical data from previous releases, managers can make better-informed decisions regarding the use of multiple release sites and greatly improve the probability of reintroduction success.     

Spatial smoothing techniques for the assessment of habitat suitability

Precise knowledge about factors influencing the habitat suitability of a certain species forms the basis for the implementation of effective programs to conserve biological diversity. Such knowledge is frequently gathered from studies relating abundance data to a set of influential variables in a regression setup. In particular, generalised linear models are used to analyse binary presence/absence data or counts of a certain species at locations within an observation area. However, one of the key assumptions of generalised linear models, the independence of observations is often violated in practice since the points at which the observations are collected are spatially aligned. In this paper, we describe a general framework for semiparametric spatial generalised linear models that allows for the routine analysis of non-normal spatially aligned regression data. The approach is utilised for the analysis of a data set of synthetic bird species in beech forests, revealing that ignorance of spatial dependence actually may lead to false conclusions in a number of situations.

New approaches to modelling fish-habitat relationships

Ecologists often develop models that describe the relationship between faunal communities and their habitat. Coral reef fishes have been the focus of numerous such studies, which have used a wide range of statistical tools to answer an equally wide range of questions. Here, we apply a series of both conventional statistical techniques (linear and generalized additive regression models) and novel machine-learning techniques (the support vector machine and three ensemble techniques used with regression trees) to predict fish species richness, biomass, and diversity from a range of habitat variables. We compare the techniques in terms of their predictive performance, and we compare a subset of the models in terms of the influence each habitat variable has for the predictions. Prediction errors are estimated by cross-validation, and variable importance is assessed using permutations of individual variable values. For predictions of species richness and diversity the tree-based models generally and the random forest model specifically are superior (produce the lowest errors). These model types are all able to model both nonlinear and interaction effects. The linear model, unable to model either effect type, performs the worst (produces the highest errors). For predictions of biomass, the generalized additive model is superior, and the support vector machine performs the worst. Depth range, the difference between maximum and minimum water depth at a given site, is identified as the most important variable in the majority of models predicting the three fish community variables. However, variable importance is highly dependent upon model type, which leads to questions regarding the interpretation of variable importance and its proper use as an indicator of causality. The representation of ecological relationships by tree-based ensemble learners will improve predictive performance, and provide a new avenue for exploring ecological relationships, both statistical and causal.     

A compass for sustainable development

A response to the Nuffield Council on Bioethics Consultation from the Institute of Biology, the Association of Applied Biology, the British Crop Protection Council, the British Ecological Society, the British Electrophoresis Society, the British Grassland Society, and the Institute of Horticulture     

利用数学模拟方法分析硝化MBR系统中SMP转换过程

为深入了解膜生物反应器(MBR)中微生物代谢产物(SMP)的生成降解以及利用情况,研究了以NH4Cl为惟一能源物质的硝化MBR反应器中SMP浓度以及分子量(MW)变化情况,并运用活性污泥模型3(ASM3)准确地计算出微生物利用底物相关的溶解性产物(UAP)和微生物死亡相关的溶解性产物(BAP)的量分别是多少,最终证明硝化系统中产生的SMP可作为能源物质被异养菌进一步利用,而且相较于BAP而言UAP更易于被生物降解,得出结论 BAP是SMP中的主要污染成分。     

生命线网络系统抗震拓扑优化的Benchmark模型

生命线系统拓扑优化问题的Benchmark模型是评测新型算法正确性和适用性的重要手段。基于此,首先以生命线网络系统抗震拓扑优化分析模型为背景,建立了该优化问题的三个Benchmark模型,并在Visual Compaq Fortran开发环境下,通过穷举法统计出解空间的所有网络,进而甄选出不同节点可靠度约束下的最优网络和若干次优网络,最后利用上述Benchmark模型对生命线网络系统抗震拓扑优化中的蚁群算法进行测试。结果表明,当网络规模较小时,蚁群算法能精确地搜索到最优解;当网络规模增大后,蚁群算法也能以较大概率搜索到最优解或次优解。     

Sensitivity of key factors and uncertainties in health risk assessment of benzene pollutant

Predicting long-term potential human health risks from contaminants in the multimedia environment requires the use of models. However,there is uncertainty associated with these predictions of many parameters which can be represented by ranges or probability distributions rather than single value.Based on a case study with information from an actual site contaminated with benzene,this study describes the application of MMSOILS model to predict health risk and distributions of those predictions generated using Monte Carlo techniques.A sensitivity analysis was performed to evaluate which of the random variables are most important in producing the predicted distributions of health risks.The sensitivity analysis shows that the predicted distributions can be accurately reproduced using a small subset of the random variables.The practical implication of this analysis is the ability to distinguish between important versus unimportant random variables in terms of their sensitivity to selected endpoints.This directly translates into a reduction in data collection and modeling effort.It was demonstrated that how correlation coefficient could be used to evaluate contributions to overall uncertainty from each parameter.The integrated uncertainty analysis shows that although drinking groundwater risk is similar with inhalation air risk,uncertainties of total risk come dominantly from drinking groundwater route.Most percent of the variance of total risk comes from four random variables.