基于多目标分布估计算法的地铁网络化应急站点选址

为提升地铁网络的应急救援能力,提出兼顾地下和地面交通两种运输方式的应急资源网络化共享模式,并建立多目标优化模型,对应急站点进行优化选址。模型以应急站点数量最少和总响应时间最短为优化目标,地铁车站重要程度为关键因素,求解各地铁车站的优化覆盖方案。提出了一种多目标分布估计算法,该算法采用拥挤比较算子和锦标赛选择算法获取优势群体,借助概率模型描述可行解的离散分布,并不断更新概率模型来实现种群的进化。运用建立的模型和算法求解北京市局部地铁网络应急站点选址问题,最终获得满足地面交通运输时效、并符合问题优化目标的Pareto最优解集。与NSGE-Ⅱ算法相比,多目标分布估计算法提前123代开始收敛,且针对最小化问题,多目标分布估计算法最优解的两个目标函数值较NSGE-Ⅱ算法分别减小64%和29%。可见,模型符合地铁应急站点选址问题的决策规则;多目标分布估计算法有效地缩小了决策空间,它比NSGE-Ⅱ算法更快地收敛,且能够获得更为优化的应急站点选址方案。     

一种新的结构可靠度通用计算方法

根据可靠度指标β的几何定义,运用具有全局优化能力的改进遗传算法,提出一种新的结构可靠度通用计算方法。针对可靠度几何优化模型搜索区域大而可行区域小的特点,在可靠区域和失效区域分别产生初始种群,在两种群之间通过不断迭代的二分法,使二分点不断接近极限状态曲面(线),从而产生可行解。对可行解进行选择、交叉、变异,最后求出全局最优解。该方法不需要使用结构功能函数的偏导数,对于功能函数不能明确表达的可靠度问题尤为适用;对于功能函数可以明确表达但求导复杂的问题,可省去求导过程;可任意设定初始值,且不受结构功能函数凹凸性的限制;通过算例验证,所提方法具有较高的精度和效率。     

糖蜜酒精废水两相UASB处理工艺的酸化段特征

文章考察了糖蜜酒精废水二相UASB处理工艺的酸化段特征 ,试验结果表明酸化段的容积负荷达到3 0kgCOD/m3 时 ,系统仍能正常运行 ,TOC去除率在 3 5%以上 ,酸化段的SO4 2 -去除率在 70 %。糖蜜酒精废水酸化率在 50 % ,二相厌氧生物处理系统并未将产酸微生物与产甲烷微生物截然分开 ,在产酸相中仍含有一定数量的产甲烷菌。     

水环境质量评价的普适指数公式

在适当设定水质指标的“基准”值情况下,用相对于“基准”值的指标相对值替换对数形式的指数公式中的水质指标值,采用遗传算法优化不同指标的指数公式中的参数,得到对多项指标通用的水质普适指数公式;并用折衷型激励功效函数将单项指标的指数加权为综合指数。公式的正确性已被多处水质评价检验。      

环境空气质量评价的普适指数公式

在适当设定空气污染物的基准值和用相对于基准值的空气污染物的“相对”值情况下,采用S型曲线表示空气污染物对环境空气质量的危害程度,并应用遗传算法优化公式中的参数,得出了一个对多种空气污染物通用的环境空气质量评价的普适指数公式。借助于折衷型激励功效函数将单基污染物分指数加权为综合指数。该公式形式简单,容量计算、评价结果精度较高,且具有可比性。     

应急供水规划调控方案优化模型的研究

目前应急供水方案通常是根据经验进行确定,缺乏对于各种方案及其成本的比较。本文以水源突发性事件造成的供水短缺为前提,将应急供水系统的方案分成备用水源供水、污染水的应急处理、水量的压缩3个方面,建立了符合实际的应急供水系统方案优化模型。运用遗传算法,得到新建应急供水系统的成本并求得其最优值。将该模型用于东莞市应急供水规划调控方案,可以为实际应急供水方案提供依据和指导。     

Monitoring and estimating the flow conditions and fish presence probability under various flow conditions at reach scale using genetic algorithms and kriging methods

The combination of current velocity and water depth influences stream flow conditions, and fish activities prefer particular flow conditions. This study develops a novel optimal flow classification method for identifying types of stream flow based on the current velocity and the water depth using a genetic algorithm. It is applied to the Datuan stream in northern Taiwan. Fish were sampled and their habitat investigated at the study site during the spring, summer, fall and winter of 2008-2009. The current velocity, water depth and maps of the presence probability of fish were estimated by ordinary and indicator kriging. The optimal classification results were compared with the classification results obtained using the Froude number and empirical methods. The flow classification results demonstrate that the proposed optimal flow classification method that considers depth-velocity and optimally identified criteria for classifying flow types, yields a current velocity and water depth of 0.32 (m/s) and 0.29 (m), respectively, and classifies the flow conditions in the study area as pool, run, riffle and slack. The variography results of the current velocity and the water depth data reveal that seasonal flows are not spatially stationary among seasons in the study area. Kriging methods and a two-dimensional hydrodynamic model (River 2D) with empirical and optimal flow classification methods are more effective than the Froude number method in classifying flow conditions in the study area. The flow condition classifications and probability maps were generated by River 2D, ordinary kriging and indicator kriging, to quantify the flow conditions preferred by Sicyopterus japonicus in the study area. However, the proposed optimal classification method with kriging and River 2D is an effective alternative method for mapping flow conditions and determining the relationship between flow and the presence probability of target fish in support of stream restoration.     

基于智能算法的矿区土壤修复采样优化

针对废弃矿区土壤的修复,同一地区周期性的采样评价是验证修复效果必不可少的手段。而采集及分析样本的过程耗时、耗力且昂贵;如何能在不影响整体评价精度的基础上,最大限度地降低后续采样点的数量,对土壤修复评价成本的降低有直接的好处。以广西某县的一重金属铅（Pb）污染严重的废弃矿区为研究对象,基于45个原始采样点,采用了RBF神经网络拟合了Pb的浓度空间分布,并采用遗传算法对采样点进行了进一步优化。在构建网络结构的过程中,根据种群个数及迭代次数的不同提出了6个优化方案。结果表明,种群个数为40且迭代次数为100的方案F的优化结果生成的分布图与原始分布图最接近,误差均方差为0.501 3,且优化后的样本数量减少到25个,减幅达44%。该结果反映了遗传算法能为空间变异缓慢地区的后续采样方案提供科学建议,明显降低采样点数量及分析成本。     

Investigation of the effects of battery types and power management algorithms on drive cycle simulation for a range-extended electric vehicle powertrain

Fuel cell (FC) hybrid vehicle power trains are an attractive technology especially for automotive applications because of their higher efficiency and lower emissions compared to conventional vehicles. This study focuses on the design of an FC hybrid power train system and evaluation of its simulations for a given speed profile through two alternative power management algorithms (PMAs). Parameters suitable for a small vehicle were taken into consideration in the mathematical model of the vehicle. The proposed hybrid power train consists of an energy storage system, composed of a 4-kg battery pack (either lithium-ion (Li-ion) battery, nickel metal hydride, or nickel–cadmium) and a direct methanol fuel cell (DMFC) as the range extender. The PMAs basically aim to fulfill the power requirements of the vehicle and decide how to command the power split between the battery and the FC. The model comprising a DMFC, a battery, and PMAs was developed in MATLAB/Simulink environment. The polarization curve of the FC was obtained using a one-dimensional DMFC model. Vehicle power requirements for a drive cycle were calculated using the equations of longitudinal dynamics of vehicle, and the results were integrated into MATLAB/Simulink model. As a result of the simulations, methanol consumption, state of charge of the battery, and power output of the FC were compared for the PMAs. This comparison shows the effect of PMAs on the hybrid vehicle performance for three battery types. The results indicate that the vehicle range could be increased when proper strategy is used as PMA.