基于信息熵评价决策模型的突发公共事件应急处置指挥方案优选

提出基于信息熵评价决策模型对突发公共事件应急处置指挥决策中的作战方案进行优选的方法。首先介绍应急处置指挥决策的概念和过程;其次,分析了信息熵以及基于信息熵评价模型的多属性决策方法;最后,给出基于信息熵的评价决策模型在突发公共事件应急处置指挥方案优选中的应用。实地演练表明,该决策模型在应急指挥决策方案排序与优选是客观合理、切实可行的,能够为应急处置指挥决策提供一定的参考。     

道路交通紧急救援服务点的优化选址

针对道路交通紧急救援对公平性、效率性、事故并发性的多目标决策需求,提出用于道路交通救援点选址的多目标决策模型。该模型通过最小化最大救援时间满足救援公平性;通过最大化一次覆盖的需求量和最小化救援点到需求点总加权时间满足救援效率;通过最大化备份覆盖的需求量满足事故并发与重大事故的救援。针对多目标决策模型,开发基于模拟退火法的优化解法,结合算例分析,能够获得减少最大救援时间、最大化一次覆盖和备份覆盖需求量的多个目标方向上最优解,实现pareto最优化的成功率均在95%以上。实例计算比较表明,笔者提出的模型与算法可以为决策者提供多目标选址方案。     

浅析总量监测中"生产周期"的确定及频次优化

根据排污周期确定加密监测采样频次,用实例说明有规律排污过程的生产特点,提出固定频次法进行监测频次优化的应用建议。     

Application of statistical modeling to optimize a coastal water quality monitoring program

The long-term water quality monitoring program implemented by the Massachusetts Water Resources Authority in 1992 is extensive and has provide substantial understanding of the seasonality of the waters in both Boston Harbor and Massachusetts Bay and the response to improvements in effluent quality and offshore transfer of the effluent in September 2000. The monitoring program was designed with limited knowledge of spatial and temporal variability and long-term trends within the system. This led to an extensive spatial and temporal sampling program. The data through 2003 showed high correlation within physical parameters measured (e.g., salinity, dissolved oxygen) and in biological measures such as chlorophyll fluorescence. To address the potential sampling redundancies in the measurement program, an assessment of the impact of reduced levels of monitoring on the ability to make water quality decisions was completed. The optimization was conducted by applying statistical models that took into account whether there was evidence of a seasonal pattern in the data. The optimization used model survey average readings to identify temporal fixed effects, model survey-average-corrected individual station readings to identify spatial fixed effects, corrected the individual station readings for temporal and spatial fixed effects and derived a correlation model for the corrected data, and applied the correlation model to characterize the correlation of annual average readings from reduced monitoring programs with true parameter levels. Reductions in the number of sampling stations were found less detrimental to the quality of the data for annual decision-making than reductions in the number of surveys per year, although there is less of a difference in this regard for dissolved oxygen than there is for chlorophyll. The analysis led to recommendations for a substantially lower monitoring effort with minimal loss of information. The recommendation supported an annual budget savings of approximately $183,000. Most of the savings was from fewer surveys as approximately $21,000 came from the reduction in the number of stations monitored from 21 to 7 and associated laboratory analytical costs.     

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.     

A process integration approach to industrial water conservation: a case study for a Chinese steel plant

A systematic approach to optimizing water network has traditionally been utilized to exam and plan water conservation in industrial processes. In the present case study, water-pinch technology was used to analyze and optimize the water network of a steel plant near China's Zhangjiakou city. A system design was developed and a limiting constraint (Cl(-) concentration) was identified based on investigations of water quality then the minimum freshwater and wastewater targets were determined without considering water losses. The analysis was then extended by calculating the additional input of freshwater required to balance the actual water losses. A nearest-neighbor algorithm (NNA) was used to distribute the freshwater and recycled water among each of the plant's operations. The results showed that with some reconstruction of the water network, the flow rates of freshwater and wastewater could be decreased by 57.5% and 81.9%, respectively.     

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。     

道路交通噪声自动监测应用探讨

结合城市道路路网状况及实际噪声监测数据和历史实验数据,对道路交通噪声自动监测数据的有效性、监测点位布设进行了研究,对道路交通噪声监测点位优化提出建议。     

基于韦伯-费希纳定律的空气质量普适韦伯指数公式

在适当设定各项空气污染物浓度"参照值"cj0的基础上,用相应于"参照值"cj0的污染物的"规范值"xj作为基于韦伯-费希纳(W-F)定律空气质量指数公式中的污染刺激量。由于各污染物同级标准的浓度"规范值"xjl差异不大,从而可以认为各污染物的韦伯指数公式等效于一个具有共同适用的韦伯常数α的W-F指数公式,并采用粒子群(PSO)算法优化得出α,得到对7项空气污染物皆适用的空气质量普适韦伯指数公式。该公式应用于多个实例分析,并与多种其它评价方法的评价结果比较表明,空气质量普适韦伯指数公式具有简单、实用和直观的特点。