Optimal Expansion Strategy for a Sewer System under Uncertainty

Because of fast urban sprawl, land use competition, and the gap in available funds and needed funds, municipal decision makers and planners are looking for more cost-effective and sustainable ways to improve their sewer infrastructure systems. The dominant approaches have turned to planning the sanitary sewer systems within a regional context, while the decentralized and on-site/cluster wastewater systems have not overcome the application barriers. But regionalization policy confers uncertainties and risks upon cities while planning for future events. Following the philosophy of smart growth, this paper presents several optimal expansion schemes for a fast-growing city in the US/Mexico borderlands—the city of Pharr in Texas under uncertainty. The waste stream generated in Pharr is divided into three distinct sewer sheds within the city limit, including south region, central region, and north region. The options available include routing the wastewater to a neighboring municipality (i.e., McAllen) for treatment and reuse, expanding the existing wastewater treatment plant (WWTP) in the south sewer shed, and constructing a new WWTP in the north sewer shed. Traditional deterministic least-cost optimization applied in the first stage can provide a cost-effective and technology-based decision without respect to associated uncertainties system wide. As the model is primarily driven by the fees charged for wastewater transfer, sensitivity analysis was emphasized by the inclusion of varying flat-rate fees for adjustable transfer schemes before contracting process that may support the assessment of fiscal benefits to all parties involved. Yet uncertainties might arise from wastewater generation, wastewater reuse, and cost increase in constructing and operating the new wastewater treatment plant simultaneously. When dealing with multiple sources of uncertainty, the grey mixed integer programming (GIP) model, formulated in the second stage, can further allow all sources of uncertainties to propagate throughout the optimization context, simultaneously leading to determine a wealth of optimal decisions within a reasonable range. Both models ran for three 5-year periods beginning in 2005 and ending in 2020. The dynamic outputs of this analysis reflect the systematic concerns about integrative uncertainties within this decision analysis, which enable decision makers and stakeholders to make all-inclusive decisions for sanitary sewer system expansion in an economically growing region.     

基于蒙特卡罗法的煤巷锚杆支护结构可靠性分析

应用工程结构可靠性理论,建立煤巷锚杆支护结构可靠性模型,针对其结构稳定性的极限状态功能函数高度非线性的特征,采用蒙特卡罗法在Matlab环境下直接产生服从各相应概率分布函数的随机变量数组计算其结构可靠度,编程过程简化,计算速度快,精度高,且不受极限状态方程非线性、随机变量非正态的限制,开辟了煤巷锚杆支护结构可靠度计算的新途径。建立了支护参数与可靠度的关系,指出了提高煤巷锚杆支护结构可靠度的方法和措施,为煤巷锚杆支护参数设计和优化提供了科学依据。     

基于社会科学统计程序(SPSS)回归性分析的尾矿库事故预测模型

基于社会科学统计程序(SPSS)软件的分析功能,对调查研究中获取的尾矿库案例进行数据提炼和分类编码,找出相关因子并进行回归性分析。最终目的是找出尾矿库各个因素的内在联系,建立简单的尾矿库事故模型,从而可以初步预测尾矿库事故发生的可能性。该预测模型为尾矿库事故的研究提出了新方法,对于防灾减灾以及保护人民生命财产安全起到了积极作用。     

高温烟气除尘滤料过滤性能检测及对比分析

对3大类不同过滤方式的高温烟气除尘滤料的压力损失、过滤速度、分级效率等性能指标进行测试对比分析,得到滤料在清洁和集尘条件下压力损失随过滤时间、粉尘负荷的变化规律.从过滤机理综合分析,集尘滤料的过滤分级效率明显高于清洁滤料过滤效率,为滤料选用提供理论依据.     

斜拉桥支架法施工的自适应安全监测技术

介绍自适应控制的基本原理及其运用现状。根据斜拉桥的实际情况选用自适应控制形式,在主要部位埋设传感器和相关的测试仪器,明确监测时间并在实施中严格执行。依据实测结果对标高、应力、容重等参数按实际情况进行修正,得出施工过程中的受力状态,从而调整施工。算出主梁各节点理论施工预拱度变化曲线等数据并与实测值进行比较,反复修正。自适应法达到了仿真计算与实际施工过程保持一致的最佳施工控制效果。绥芬河斜拉桥的施工证明,自适应法是一种很适合斜拉桥支架施工安全监测的技术,值得推广应用。     

Towards a planning support system for environmental management and agri-environmental measures--the Colorfields study

The authors present the beginnings of a planning support system (PSS) for agri-environmental measures exemplified by a virtual implementation of Colorfields and blooming strips on model farms, based on real-world data. This paper starts with an introduction to the Colorfields, a concept for transdisciplinary and sustainable landscape design of set-aside land. Colorfields comprise of blooming strips of flowering annual or biennial plants, which are designed and drilled in pattern on fallow land creating Land Art. The temporary scenic arrangements of the Colorfields combine the advantages of ecological strips, e.g. providing habitats for insects (especially bees), improving soil fertility through the cultivation of intercrops, with improvements of the social recognition of farmers as producers of pleasant landscapes instead of monoculture fields.The prototype of the PSS uses two software tools of different scientific origin, the bio-economic modeling system MODAM and the landscape visualization system Lenné3D, which are linked based on geo-data. The resulting system helps to assess the economic effects and visualizes the effects of the specific landuse patterns under different scenarios.The economic assessment of blooming strips on arable land and of one Colorfield on fallow land shows that these measures prove to be profitable from an economic viewpoint assuming the current area payments for the obligatory European Union set-aside program. Furthermore, the visualizations enable the design to be tested virtually by exploring the resultant scenery. They provide artists, planners and stakeholders including farmers with a tool to virtually wander through landscape scenarios supporting a collaborative design and a shared vision for the community.The results of the two model farms and previous case studies for Colorfields demonstrate how current policy conditions could be used for the improvement of environmental and scenic qualities. Furthermore, the ability of the tools, MODAM and Lenné3D, suggests to support and promote these activities.     

室内甲醛污染影响因素的多元回归分析

本文在室内甲醛质量守恒定律基本公式合理推导的基础上,对影响甲醛浓度的主要因素做了正交实验。对实验数据进行多元回归分析,建立室内甲醛浓度预测模型。对模型做检验,检验结果表明,本文的预测模型能较好地预测室内空气中的甲醛浓度。     

基于PSO-SVM算法的环境监测数据异常检测和缺失补全

针对环境监测数据异常和数据缺失问题,提出了基于支持向量机的粒子群优化数据异常检测和缺失补全算法。利用粒子群优化算法选取较优的支持向量机训练参数组合,以此建立非线性的支持向量机模型,并利用结果模型对测得的真实数据拟合预测。以宁夏回族自治区某污水处理厂的污染物测量数据作为实验数据,结果表明,利用该算法预测数据的准确率可达97.977%,检测异常数据准确度高,缺失数据补全正确。     

PSO和SVM混合算法确定太湖入湖河流水质主要影响因子

以影响太湖入湖河流水质的24个因子值为研究对象,将PSO算法与SVM算法相结合。PSO算法用于优化SVM算法的参数c和g,以利于快速、高效地确定c和g的全局最优值;SVM算法基于最优的c和g,分别以24,21,18,15,12,9和6个因子作为特征向量预测水质的污染程度。结果表明,当特征向量为9个影响因子时预测率最高。其参数c=18.56,g=1.35,对应的预测率为：全局预测率92.59%,重度污染水质预测率88.89%,轻度污染水质预测率94.45%。因此,通过PSO和SVM混合算法,可以确定影响太湖入湖河流水质的主要因子,利用这些主要因子对水质进行预测预警,不但可以节省时间,而且可以得到精确的结果。     

Climate policy support under political consensus: exploring the varying effect of partisanship and party cues

By creating attitudinal rifts among partisan voters, political polarization is expected to negatively affect chances of effectively mitigating climate change. While such expectations generally have found support, less attention has been paid to the opposite claim that political consensus should eliminate the partisan dimension in climate change politics. This study tests this claim by studying how party identification, and party cues specifically, affects public policy attitudes in a context defined by political consensus. Using data from a large online access panel in Sweden, party identification and party cues are shown to matter for policy attitudes even in a consensus context. This effect is not limited to certain issues but is found across a wide range of policies, and the effect of party cues, for a given issue, varies across parties. The implications of this study and areas for future work are discussed.