基于Petri网的铁路车站安全防护系统建模及可靠性分析

采用单一的随机Petri网对车站线路结构、列车运行过程及车站防护系统建模,基于欧洲电工标准委员会定义的安全目标,确定车站安全防护系统的可靠性需求。对无防护系统的列车入站进行建模和分析,然后基于防护系统功能及可靠性特性,结合列车入站动态特性,对防护系统的功能和可靠性进行建模,并根据安全目标与车站事故率之间的数学关系进行系统的安全分析和可用性分析。其结果表明,车站防护系统的可靠性与乘客的个人风险存在着定量的关系,从而可以依据个人风险的标准来确定防护系统可靠性需求。     

基于Monte-Carlo方法的静电除尘器除尘效率

针对经典除尘效率未考虑诸多随机因素影响的问题,提出了基于Monte-Carlo方法的效率分析模型,并通过实验数据验证其可靠性。通过该模型分析了粒径大小、电场气流速度、电晕电压、两极间距、粒子浓度等因素对收尘效率的影响规律,结果表明:基于Monte-Carlo方法的仿真结果精度更高,能有效预测静电除尘器的收尘效率,对于改进静电除尘器设计具有一定的理论意义和实用价值。     

结构分析中模拟与仿真技术研究

由于像洪水、水灾、地震这样一类自然灾害的原型重复试验几乎是不可能的,因此计算机仿真技术在这一领域的应用具有重要的意义。本文探索了土木工程中模拟与仿真的概念、特征及实现途径,阐述了关于仿真智能化、可视化的技术细节及仿真的基本思路;结合面向对象程序设计（OOP）概念,提出了结构分析程序设计的原则及标准。在此基础上,编写了基于Windows的结构有限元分析软件SmartSEA.     

Disequilibrium and Indivisibility of Pollution Control Equipment Under a Permit-Trading System: A Case Study of Kao-Hsiung and Ping-Tung Counties in Taiwan

A mixed-integer programming model that minimizes the social abatement cost is used to investigate whether a market equilibrium condition could be reached in a newly proposed permit-trading market for nitrogen oxide control in Taiwan. Unlike in previous studies, unit pollution abatement cost is determined endogenously by incorporating technology adoption as a binary decision variable. The results show that when technologies are lumpy and irreversible, disequilibrium might occur due to firms’ inability to manage their emission levels after installing equipment with fixed size and control capacity.

Optimal Selection of Priority Development Areas Considering Tradeoffs Between Hydrology and Development Configuration

Mixed-integer linear programs are proposed for siting development and conservation areas in watersheds, addressing economic objectives (development perimeter and proximity) and ecological objectives. Links between watershed hydrology and ecology need not be well defined. Parameters for the linear programs are obtained from linearization of the SWAT hydrologic model.     

DPX快速萃取程序升温大体积进样GC/MS法测定水中19种多溴联苯化合物

建立DPX快速吸附萃取、程序升温(PTV)大体积进样与气相色谱/质谱(GC/MS)联用,SIM模式同时测定水中19种多溴联苯(PBBs)单体的方法。该方法在PBBs质量浓度1~50μg/L范围内线性良好,19种PBBs单体检出限为0.147~0.230μg/L,相对标准偏差为6.61%~10.5%,加标回收率为61.5%~82.6%。     

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.     

Multi-stand Forest Management under a Climatic Risk: Do Time and Risk Preferences Matter?

We propose a stochastic dynamic programming framework to model the management of a multi-stand forest under climate risk (strong wind occurrence). The preferences of the forest-owner are specified by a non-expected utility in order to separately analyze intertemporal substitution and risk aversion effects. A numerical method is developed to characterize the optimal forest management policies and the optimal consumption-saving strategy. The stochastic dynamic programming framework is applied to a non-industrial private forest-owner located in North-East of France. We show that the optimal decisions both depend upon risk and time preferences. The authors would like to thank participants at the international conference on Economics of Sustainable Forest Management in Toronto, at the PARIS 1 seminar on Environmental and Natural Resource Economics, at the 2004 Applied Microeconomics Conference in Lille and at the 13th annual conference of the European Association of Environmental and Resource Economists at Budapest.     

A Model for Landscape Planning Under Complex Spatial Conditions

We present a new mathematical programming framework that is adaptable to a variety of spatially explicit landscape problems in environmental investment, conservation, and land-use planning, transport planning, and agriculture. As part of capturing spatial interdependencies, the framework considers decision variables at two levels, finely spaced grid cells and landholdings. We applied the framework to an environmental investment problem using objective functions representing biodiversity and carbon sequestration. We also tested the model to optimize the path of a road through part of the landscape. Using the Nambucca case study in eastern Australia, we applied a hybrid greedy randomised adaptive search procedure (GRASP) to find solutions to the model.     

COMPARISON OF RESERVOIR LINEAR OPERATION RULES USING LINEAR AND DYNAMIC PROGRAMMING1

ABSTRACT: Mathematical optimization techniques are used to study the operation and design of a single, multi-purpose reservoir system. Optimal monthly release policies are derived for Hoover Reservoir, located in Central Ohio, using chance-constrained linear programming and dynamic programming-regression methodologies. Important characteristics of the former approach are derived, discussed, and graphically illustrated using Hoover Reservoir as a case example. Simulation procedures are used to examine and compare the overall performance of the optimal monthly reservoir release policies derived under the two approaches. Results indicate that, for the mean detention time and the corresponding safe yield target water supply release under existing design of Hoover Reservoir, the dynamic programming policies produce lower average annual losses (as defined by a two-sided quadratic loss function) while achieving at least as high reliability levels when compared to policies derived under the chance-constrained linear programming method. In making this comparison, the reservoir release policies, although not identical, are assumed to be linear. This restricted form of the release policy is necessary to make the chance-constrained programming method mathematically tractable.