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。     

焦化废水对蚕豆和大麦毒性的影响

以COD作为主要参照指标,研究了焦化废水在符合GB 13456-92<钢铁工业水污染物排放标准>焦化一级、二级排放标准限值要求时,对蚕豆和大麦幼根生长、根尖细胞遗传毒性的影响.结果表明:在实验周期内,焦化废水对蚕豆幼根根长、根重和有丝分裂指数的影响不大;对大麦幼根根重无明显影响,而对大麦根长和有丝分裂指数有促进作用.焦...     

路面径流水环境下交通承载力分析

为了对路面径流水容许污染总量控制下的交通承载力问题进行探讨,利用神经网络具有的非线性映射能力和遗传算法具有的全局随机搜索能力,结合公路路面径流水质检测数据,提出了一种基于遗传神经网络进行公路交通环境承载力反计算的分析方法,应用该方法可根据路面径流水质污染数据反演出路段交通量大小,并可据此进行交通量与路而径流水质污染的关...     

Fast increasing of surface ozone concentrations in Pearl River Delta characterized by a regional air quality monitoring network during 2006-2011

Based on the observation by a Regional Air Quality Monitoring Network including 16 monitoring stations, temporal and spatial variations of ozone(O3), NO2and total oxidant(Ox) were analyzed by both linear regression and cluster analysis. A fast increase of regional O3concentrations of 0.86 ppbV/yr was found for the annual averaged values from 2006 to 2011 in Guangdong, China. Such fast O3increase is accompanied by a correspondingly fast NOx reduction as indicated by a fast NO2 reduction rate of 0.61 ppbV/yr. Based on a cluster analysis, the monitoring stations were classified into two major categories – rural stations(non-urban) and suburban/urban stations. The O3concentrations at rural stations were relatively conserved while those at suburban/urban stations showed a fast increase rate of 2.0 ppbV/yr accompanied by a NO2 reduction rate of 1.2 ppbV/yr. Moreover, a rapid increase of the averaged O3 concentrations in springtime(13%/yr referred to 2006 level) was observed, which may result from the increase of solar duration, reduction of precipitation in Guangdong and transport from Eastern Central China. Application of smog production algorithm showed that the photochemical O3production is mainly volatile organic compounds(VOC)-controlled. However, the photochemical O3production is sensitive to both NOx and VOC for O3pollution episode. Accordingly, it is expected that a combined NOx and VOC reduction will be helpful for the reduction of the O3 pollution episodes in Pearl River Delta while stringent VOC emission control is in general required for the regional O3 pollution control.     

基于FA-BAS-ELM的海洋油气管道外腐蚀速率预测

为提高海洋油气管道外腐蚀速率预测的精度和效率,建立基于因子分析(FA)和天牛须搜索算法(BAS)的极限学习机(ELM)腐蚀速率预测模型。利用FA对影响因素数据集进行降维处理,确定预测模型的输入变量;建立ELM预测模型,并采用BAS对ELM模型的参数进行优化,避免参数取值随机性对模型预测性能的影响;以实海挂片试验为例,通过建模仿真评价模型的预测性能,并与其他模型进行对比分析。结果表明：FA-BAS-ELM预测模型的平均绝对误差(MAPE)仅为1.92%,决定系数R²高达0.994 9,相比于其他模型,该模型具有更优的预测性能。     

基于神经网络的洪水预报研究

人工神经网络通过神经元之间的相互作用来完成整个网络的信息处理,具有自学习和自适应等一系列优点,因而用它来进行洪水预报是可行的.对洪水预报问题,初步建立了基于神经网络的洪水预报系统,给出了应用实例.     

生态恢复过程中的种群遗传学考虑

生态恢复是生态科学的最终实验,从种群角度看,恢复的目标是使种群（尤其是优势种或建群种）到具有生长、繁殖和适应进化变化的能力。要达到这个目标,种群遗传学知识必不可少。遗传变异是物种适应变化环境的基础,而局部适应则是种群适应局部环境遗传分化的结果,它们在生态恢复中起着重要的作用。分析了生态恢复过程中影响种群遗传变异因素,主要有取样误差和小种群效应（主要是瓶颈或建立者事件及其以后的近交和漂变等）,在进行     

Gradient analysis of the chrogenetic structure of lowland and highland scotch pine populations

Gradients of genetic distances (GGDs) between 26 adjacent cenopopulations of Scotch pine (Pinus sylvestris L.) along the transects through the distinct landscape-ecotopic isolation borders were analyzed graphically. The results made it possible to reveal and quantitatively estimate gradients and borders of gene-pool structural patterns in populations of this species in the central part of Northern Eurasia. In lowland areas, the most distinct borders were found between pine populations growing on dry lands and bogs (Pineta sphagnosa) in the forest zone of the Transural region, as well as between the Arakaragaiskii and Amankaragaiskii island pine forests in the steppe zone (Northern Turgai). In highland areas (the Urals and the Carpathians), the greatest GGDs were observed between low-mountain (about 600 m above sea level) and middle-mountain (850–900 m) populations. Analysis of GGDs is a promising gene-geographic method for determining population borders and studying the chorogenetic structure of species.     

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

Nowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.