模糊控制技术在污水处理中的应用

针对污水处理过程中采用传统的流量程序控制和时间程序控制的不足,提出了一种基于单片机的模糊控制方法。该方法以污水流量、溶解氧(DO)浓度和污泥回流比为主要被控对象,以DO浓度为主要控制参数,通过离散计算和在线查表的模糊推理方法可得到最佳的风机转速,使污水处理生化池内DO浓度保持在最佳状态,同时还通过控制曲线展示了当输入污水流量发生扰动时对DO浓度的控制精度,使污水处理质量保持稳定。通过实际应用表明,该控制方法不但能确保出口净化水水质达标,还可以节约15%的电能。     

基于ANFIS模型的基坑工程位移反分析

在软土地基深基坑开挖支护工程的设计与施工中,岩土体力学参数的取值问题是关键。基于MATLAB(R2007a)模糊工具箱中的自适应神经模糊推理系统(ANFIS)对基坑岩土体力学参数进行了位移反演分析,并结合汉口火车站出站厅基坑工程实例,动态反演了具体参数,检验了反演精度,从而验证了该方法用于基坑工程位移反分析的可行性及工程适用性。     

Applying Statistical Causal Analyses to Agricultural Conservation: A Case Study Examining P Loss Impacts

Estimating the effect of agricultural conservation practices on reducing nutrient loss using observational data can be confounded by factors such as differing crop types and management practices. As we may not have the full knowledge of these confounding factors, conventional statistical meta‐analysis methods can be misleading. We discuss the use of two statistical causal analysis methods for quantifying the effects of water and soil conservation practices in reducing P loss from agricultural fields. With the propensity score method, a subset of data was used to form a treatment group and a control group with similar distributions of confounding factors. With the multilevel modeling method, data were stratified based on important confounding factors, and the conservation practice effect was evaluated for each stratum. Both methods resulted in similar estimates of the conservation practice effect (total P load reduction avg. ~70%). In addition, both methods show evidence of conservation practices reducing the incremental increase in total P export per unit increase in fertilizer application. These results are presented as examples of the types of outcomes provided by statistical causal analyses, not to provide definitive estimates of P loss reduction. The enhanced meta‐analysis methods presented within are applicable for improved assessment of agricultural practices and their effects and can be used for providing realistic parameter values for watershed‐scale modeling.     

Risk assessment of sour gas inter-phase onshore pipeline using ANN and fuzzy inference system – Case study: The south pars gas field

Nowadays, pipelines have been extensively used for transporting oil and gas for long distances. Therefore, their risk assessment could help to identify the associated hazards and take necessary actions to eliminate or reduce the risk. In the present research, an artificial neural network (ANN) and a fuzzy inference system (FIS) were used to prepare a new model for pipeline risk assessment with higher accuracy. To reach this objective, the Muhlbauer method, as a common method for oil and gas pipeline risk assessment, was used for determining important and influential factors in the pipeline performance. Mamdani fuzzy model was developed in Matlab software by considering expert knowledge. The outcomes of this model were used to develop an ANN. To verify the developed model, the inter-phase shore pipe of phase 9–10 refinery in the South Pars Gas field was considered as a case study. The results showed that the proposed model gives a higher level of accuracy, precision, and reliability in terms of pipe risk assessment.     

Use of Scale Invariance in Evaluating Judgement Indicators

Indicators are used to draw conclusions about ecological endpoints when these endpoints cannot be measured directly. In many cases, inferences about an endpoint are only possible because assumptions have been made about the relationship between indicator and endpoint; we refer to such indicators as judgement indicators. The validity of inferences made using a judgement indicator can be gauged by examining the known or assumed form of the general relationship between indicator and endpoint. The rules for this kind of inference are a consequence of scale invariance, which originates from measurement theory. For simple indicators comprised of a single indicator measurement, the inferences allowed – equivalence, rank, equality of intervals, and equality of ratios – depend on whether the data are nominal, ordinal, interval, or ratio scaled. For composite indicators containing two or more simple indicators, inferences are also affected by the mathematical form of combination; e.g., whether the terms are summed or multiplied. Standardizing simple or composite indicators can allow inferences about the relative importance of observations, based on the natural range of occurrence. Scale invariance is a particularly important consideration in landscape assessments, since these often make use of judgement indicators.     

Effect of Widespread Agricultural Chemical Use on Butterfly Diversity across Turkish Provinces

Although agricultural intensification is thought to pose a significant threat to species, little is known about its role in driving biodiversity loss at regional scales. I assessed the effects of a major component of agricultural intensification, agricultural chemical use, and land‐cover and climatic variables on butterfly diversity across 81 provinces in Turkey, where agriculture is practiced extensively but with varying degrees of intensity. I determined butterfly species presence in each province from data on known butterfly distributions and calculated agricultural chemical use as the proportion of agricultural households that use chemical fertilizers and pesticides. I used constrained correspondence analyses and regression‐based multimodel inference to determine the effect of environmental variables on species composition and richness, respectively. The variation in butterfly species composition across the provinces was largely explained (78%) by the combination of agricultural chemical use, particularly pesticides, and climatic and land‐cover variables. Although overall butterfly richness was primarily explained by climatic and land‐cover variables, such as the area of natural vegetation cover, threatened butterfly richness and the relative number of threatened butterfly species decreased substantially as the proportion of agricultural households using pesticides increased. These findings suggest that widespread use of agricultural chemicals, or other components of agricultural intensification that may be collinear with pesticide use, pose an imminent threat to the biodiversity of Turkey. Accordingly, policies that mitigate agricultural intensification and promote low‐input farming practices are crucial for protecting threatened species from extinction in rapidly industrializing nations such as Turkey. Efectos del Uso Extensivo de Agroquímicos sobre la Diversidad de Mariposas en Provincias Turcas     

Pseudoreplication in Tropical Forests and the Resulting Effects on Biodiversity Conservation

Tropical forest ecosystems are threatened by habitat conversion and other anthropogenic actions. Timber production forests can augment the conservation value of primary forest reserves, but studies of logging effects often yield contradictory findings and thus inhibit efforts to develop clear conservation strategies. We hypothesized that much of this variability reflects a common methodological flaw, simple pseudoreplication, that confounds logging effects with preexisting spatial variation. We reviewed recent studies of the effects of logging on biodiversity in tropical forests (n = 77) and found that 68% were definitively pseudoreplicated while only 7% were definitively free of pseudoreplication. The remaining proportion could not be clearly categorized. In addition, we collected compositional data on 7 taxa in 24 primary forest research plots and systematically analyzed subsets of these plots to calculate the probability that a pseudoreplicated comparison would incorrectly identify a treatment effect. Rates of false inference (i.e., the spurious detection of a treatment effect) were >0.5 for 2 taxa, 0.3–0.5 for 2 taxa, and <0.3 for 3 taxa. Our findings demonstrate that tropical conservation strategies are being informed by a body of literature that is rife with unwarranted inferences. Addressing pseudoreplication is essential for accurately assessing biodiversity in logged forests, identifying the relative merits of specific management practices and landscape configurations, and effectively balancing conservation with timber production in tropical forests. Pseudoreplicación en Bosques Tropicales y Efectos Resultantes Sobre la Conservación de Biodiversidad     

A Bayesian strategy for combining predictions from empirical and process-based models

We present a strategy for using an empirical forest growth model to reduce uncertainty in predictions made with a physiological process-based forest ecosystem model. The uncertainty reduction is carried out via Bayesian melding, in which information from prior knowledge and a deterministic computer model is conditioned on a likelihood function. We used predictions from an empirical forest growth model G-HAT in place of field observations of aboveground net primary productivity (ANPP) in a deciduous temperate forest ecosystem. Using Bayesian melding, priors for the inputs of the process-based forest ecosystem PnET-II were propagated through the model, and likelihoods for the PnET-II output ANPP were calculated using the G-HAT predictions. Posterior distributions for ANPP and many PnET-II inputs obtained using the G-HAT predictions largely matched posteriors obtained using field data. Since empirical growth models are often more readily available than extensive field data sets, the method represents a potential gain in efficiency for reducing the uncertainty of process-based model predictions when reliable empirical models are available but high-quality data are not.     

Bayesian analysis under ordered functions of parameters

Ordered parameter problems arise in a wide variety of real world situations and are dealt with extensively in the literature. Traditional frequentist methods for dealing with these problems are rather complicated theoretically, especially when sample sizes are small. Bayesian methods are not widely used because high dimensional numerical integration is often required. However, Markov chain Monte Carlo methods provide alternatives to such numerical integration and also deal with ordered parameter problems in a straightforward manner. Little is known about the situation where functions of parameters are ordered. Such problems may seem to be of little practical concern initially, but one can readily see their importance in situations where ordering is placed on the means and variances of several normal or Gamma populations. For the Gamma distribution we will present real examples where we will analyze monthly precipitation data from San Francisco, California and Oakland Mills, Iowa. For the San Francisco data we will simultaneously order both monthly precipitation means and variances. For the Iowa data we will place ordering on seasonal average while still estimating monthly means. Our results show that we would obtain sharper, more accurate inference when order restrictions are employed.     

A General Model for Analyzing Data from Mark-recapture Experiments with an Application to the Pacific Halibut

A general model is developed to examine the patterns of the regional movement of tagged and released fish from mark-recapture experiments. It is a stochastic model that incorporates fishing mortality, natural mortality, fish movement, tag-shedding, and different rates of reporting. A likelihood function is constructed for estimating its parameters. We used this model to analyze data on the Pacific halibut from mark-recapture experiments conducted by the International Pacific Halibut Commission (IPHC), with a total of 36,058 releases from 1982 to 1986 and 5,826 recoveries from 1982 to 2000. We estimated their rates of movement among IPHC management areas, along with their instantaneous rates of natural and fishing mortalities. Our analysis revealed that fish movement was not significant among areas, with a resident probability of > 0.92. This lends support to the IPHC catch-at-age stock assessment model (which has no built-in movement components). The estimated instantaneous rate of natural mortality (0.198 year⁻¹) lies between that assumed in all IPHC stock assessments before 1998 (0.20 year⁻¹) and that from 1999 onwards (0.15 year⁻¹). The estimates of the instantaneous rates of fishing mortality were consistent with those from the IPHC stock assessment model. Received: April 2003 / Revised: May 2005