10过程工业清洗用水减量优化算法

建立了10过程工业清洗用水数学模型，分析了10过程的工业清洗用水流量、进出口浓度、杂质传质速率之间的关系，并转化为一个有约束条件的优化问题。提出了一套基于遗传算法-模拟退火法-单纯形法的工业清洗过程用水的优化方法，编制了清洗过程用水的应用程序。优化结果表明：新鲜水可由252．42t／h降至152．82t／h，节水量99．60t／h，污水减少量99．60t／h，节水率达39．5％。     

优选地震救灾路径的图与模糊集算法

由于在大地震的紧急救灾期，交通网络具有明显的结构变动性，传统的最短路算法设计出的路线难以满足救灾在时间、安全性与通行质量方面的要求。为了解决这一问题，考虑了有关的因素，基于模糊评价，对交通网络图中的长度权进行了修正；并增加了2个新的约束，然后综合应用图论与模糊极值的理论方法，建立了2个以最大限度地保障运行体的完整性与缩短运行时间为主要目标的，在多权图上优选路线的算法。该算法可较好地满足紧急救灾路线优化设计的需要。     

A novel framework for analysis of cross-media environmental effects from agricultural conservation practices

Agricultural ecosystems are a source of greenhouse gas (GHGs) emissions and losses of nutrients to waterways. Several studies have recognized this and have documented the potential to reduce GHG fluxes and nutrient loss to waterways by using carbon offsets to fund the implementation of land retirement and afforestation. However, the ability to use land for both agricultural production and environmental conservation is also important. This study develops a novel analytical framework that is used to examine the cross-media (water and air) environmental effects of implementing offset-funded conservation practices in a working-lands setting. The framework is applied to a case study which examines the extent to which carbon pricing can affect practice implementation costs and the optimal distribution of these practices throughout an agricultural watershed. Results indicate that carbon offsets can reduce conservation practice implementation costs and have the potential to reduce greater amounts of nonpoint source pollution for a given cost of implementation. This conclusion has significant implications for policymaking, particularly with regard to using markets for GHG emissions to achieve water quality improvements where water quality trading or government conservation programs have historically been unsuccessful.     

大气环境质量的人工神经网络决策模型

为了对环境质量进行综合评价，运用误差反向传播算法的人工神经网络方法建立了环境质量评价的Ｂ－Ｐ决策模型。用此模型研究实例的大气环境质量，结果表明Ｂ－Ｐ网络用于环境质量评价具有客观性和实用性。     

基于模糊网络和时间依赖的应急车辆调度优化研究

突发灾害的应急物资调度是减轻人民财产损失、防止次生灾害发生、保障社会稳定的必要支撑。借助模糊数学中的三角模糊数学方法对不确定的需求信息特征进行描述,用模糊函数积分确定车辆在路段的模糊行驶时间。以应急车辆调度的总时间最短为调度的主要目标,同时配送数量应最大限度地满足需求点的需求量为次要目标建立应急车辆调度模型。然后设计粒子群算法对模型进行求解。最后通过算例,验证建立的模型拟合现实中的情况,结果不是确定数值,而是模糊数值范围的优化。     

地震灾害风险因素和风险评估指标的模糊算法

提出了影响地震灾害风险的主要因素 ,分析了地震震级、烈度、人口密度等因素与某些震害现象之间的相关性 ,以及建筑物的地震破坏程度与其它风险因素的关系。筛选出了地震灾害模糊风险分析指标———震损度DA(其用于风险评估的指标值为 (da) 0 )。给出了一种多变量信息分配算法的公式。在建立因果律模糊关系矩阵时 ,以归并、排序的方法解决了自变量在其样本空间组合后的重叠与错序问题 ,并简化了矩阵运算。在此基础上 ,建立了震损度指标值 (da) 0 的计算方法。     

Variance and Uncertainty in the Expected Number of Occurrences in Reserve Selection

Abstract:  Reserve selection often concerns the design of reserve networks for the long-term maintenance of biodiversity. We considered uncertainty in the context of three common reserve-selection formulations, the expected number of populations, proportional coverage of land-cover types, and the probability of having at least one population. By uncertainty, we mean variance in the outcome of any probability-based reserve selection formulation. A typical reserve-selection formulation might ask for the least expensive set of sites that contains n populations per species. It is implicit here that this requirement concerns the expected number of populations, which actually is obtained only with a 50% chance. If the requirement is changed to select the least expensive set of sites that gives n populations per species with a 95% probability, the number of sites required in the solution increases and the identity of the sites is changed toward sites that have high probabilities of persistence (or occurrence) and low associated binomial variance. Anthropogenic threat is one factor that may cause probabilistic uncertainty in the context of proportional area coverage.     

典型耦合优化算法在源项反演中的对比研究

突发大气污染事故中,污染源的快速、准确确定是应急处置的基础.为研究有效的源项评估方法,本文基于美国草原外场SO₂释放实验,利用GA-PSO、GA-NM、PSO-NM 3种耦合算法,分别与高斯点源烟羽扩散模型结合,对源强和位置等污染源参数进行反演与对比,并从算法结构与大气扩散条件方面进行反演效果差异分析.结果表明,从源强反演角度看,PSO-NM反演结果的准确性最高、稳定性最强,平均误差（11.3%）与平均标准偏差（0.7g/s）明显低于GA-NM（16.4%、13.3g/s）与GA-PSO（29.0%、26.6g/s）.从位置反演角度看,PSO-NM的反演结果最为稳定,反演的平均标准偏差（0.29m）明显低于GA-NM（3.20m）与GA-PSO（3.03m）算法;在不稳定和中性扩散条件下,PSO-NM算法的位置反演准确性最高,误差为4.97m;但在稳定扩散条件下,GA-NM的位置反演误差（7.69m）最小.从反演效率角度看,PSO-NM与GA-NM反演时间最短,更适用于污染源的快速确定.     

基于空气质量模式和数学规划模型的城市PM2.5达标策略——以临汾为例

为了改善城市空气质量,降低PM_2.5浓度,需要制定科学的控制策略,同时兼顾污染物减排量与减排成本效益.本文基于区域大气环境模型RegAEMS与数学规划模型,采用多目标遗传算法,探究城市大气PM_2.5污染的最优控制策略,并应用于临汾市（14类行业源、17个区域源）的PM_2.5浓度达标规划,实现污染物排放量最大和减排成本最小的双目标优化.结果表明,在PM_2.5平均浓度近200 μg/m³的重污染天气条件下,为达到PM_2.5浓度目标（75μg/m³）,临汾市最大污染物允许排放量为356.7t/d,最小减排成本为3.36亿元.NO_x、SO₂、NH₃、VOCs和一次颗粒物的减排量分别为98.1,49.9,44.3,155.7和105.5t/d,减排成本分别为11.7,6.8,6.2,5.5和3.5千万元.对VOCs、NO_x、PM_2.5、NH₃和SO₂减排潜力最大的行业分别为焦化源、移动源、扬尘源、农业源和民用燃烧源,分别占所有行业5种污染物减排量的21.6%、14.1%、11%、8.6%和3.8%.钢铁行业的减排成本最高（39%）;襄汾县的减排量最大,减排成本最高（达7218万元）.