MULTIPLE CRITERIA ANALYSIS FOR WATER QUALITY MANAGEMENT IN THE NITRA BASIN1

ABSTRACT: This paper presents the implementation of one element of a decision support system (DSS) for regional water quality management, applied to the Nitra River Basin in Slovakia. A model-based, aspiration-led methodology for multicriteria decision support has been used for the study. Several reusable, modular software tools have been developed and implemented: a problem-specific generator to produce the core part of the mathematical programming model, tools for the generation and interactive modification of multicriteria problems, and a solver for the resulting mixed-integer optimization problem. Provided in the paper are the following: a complete formulation of the mathematical model (including the imbedded water quality model), a summary of the aspiration-reservation-led multiple criteria optimization approach applied to decision support, and an overview of results that illustrate the applied approach and provide some interesting insights to the case study.     

A multi-objective optimization framework for surfactant-enhanced remediation of DNAPL contaminations

The occurrence of Dense Non-Aqueous Phase Liquid (DNAPL) contaminations in the subsurface is a threat for drinkwater resources in the western world. Surfactant-Enhanced Aquifer Remediation (SEAR) is widely considered as one of the most promising techniques to remediate DNAPL contaminations in-situ, be it with considerable additional costs compared to classical pump-and-treat remediations. A cost-effective design of the remediation set-up is therefore essential. In this work, a pilot SEAR test is executed at a DNAPL contaminated site in Belgium in order to collect data for the calibration of a multi-phase multi-component model. The calibrated model is used to assess a series of scenario-analyses for the full-scale remediation of the site. The remediation variables that were varied were the injection and extraction rate, the injection and extraction duration, and the surfactant injection concentrations. A constrained multi-objective optimization of the model was applied to obtain a Pareto set of optimal remediation strategies with different weights for the two objectives of the remediation: (i) the maximal removal of DNAPL and (ii) a total minimal cost. These Pareto curves can help decision makers to select an optimal remediation strategy in terms of cost and remediation efficiency. The Pareto front shows a considerable trade-off between the total remediation cost and the removed DNAPL mass.     

A robust approach for iterative contaminant source location and release history recovery

Contamination source identification is a crucial step in environmental remediation. The exact contaminant source locations and release histories are often unknown due to lack of records and therefore must be identified through inversion. Coupled source location and release history identification is a complex nonlinear optimization problem. Existing strategies for contaminant source identification have important practical limitations. In many studies, analytical solutions for point sources are used; the problem is often formulated and solved via nonlinear optimization; and model uncertainty is seldom considered. In practice, model uncertainty can be significant because of the uncertainty in model structure and parameters, and the error in numerical solutions. An inaccurate model can lead to erroneous inversion of contaminant sources. In this work, a constrained robust least squares (CRLS) estimator is combined with a branch-and-bound global optimization solver for iteratively identifying source release histories and source locations. CRLS is used for source release history recovery and the global optimization solver is used for location search. CRLS is a robust estimator that was developed to incorporate directly a modeler's prior knowledge of model uncertainty and measurement error. The robustness of CRLS is essential for systems that are ill-conditioned. Because of this decoupling, the total solution time can be reduced significantly. Our numerical experiments show that the combination of CRLS with the global optimization solver achieved better performance than the combination of a non-robust estimator, i.e., the nonnegative least squares (NNLS) method, with the same solver.     

废水污染物总量监测中采样方法的研究

通过对21家有代表性的废水污染源进行现场采样、排放曲线分析、采样频率优化的研究,确定总量监测中采样频率优化结果,为污染源总量控制监测提供技术指导。     

高阶段尾砂胶结充填体力学研究与博弈树配比优化

在实验室试验了不同配比尾砂胶结试块强度,在充填采场取样测试了采场充填体强度.结果表明,如果以实验室试验的力学参数为充填配比设计依据,充填体最小设计安全系数为1.6～1.8比较合理.分析高阶段充填体受力,推导了分层充填力学计算公式及安全系数计算方法;根据充填工艺,确定最小充填高度,沿采场高度方向划分配比优化单元,建立了高阶段充填体配比优化设计模型;采用博弈树分析方法,解决了高阶段充填体配比优化问题,并对安庆铜矿5号矿房配比进行了优化.结果显示:与经验设计方法相比,采用本文方法优化充填配比,不仅使高阶段充填体内安全系数分布更趋合理,而且降低了水泥消耗.     

Importance of incorporation of personal communication devices in evacuation simulators

The importance of the incorporation of personal communication devices in emergency evacuation simulators will be discussed. Four case studies are examined to identify usage statistics and human behavior when using the devices in an emergency. The case studies are: 9/11 World Trade Center Attacks, London train bombings, Virginia Tech shooting and United Flight #93. The case studies’ findings are implemented into the Vacate evacuation simulator, which uses particle swarm optimization to mimic human pedestrian behavior. The physical actions of using a device, as well as the reaction to the information transmitted during usage, are examined. Findings show that the effects of the personal communication devices can affect individuals that are close to the user of the device, as well as those not in the local vicinity of the user. These effects, combined with the rapid mainstream acceptance of communication technologies, justify the implementation of personal communication devices into evacuation simulators.     

报废梯黑铝装药提取制备硫酸铝工艺研究

为回收梯黑铝混合装药中的铝,通过单因素试验及五因素四水平正交试验对提取工艺进行优化。结果表明,五因素影响顺序为:酸液浓度>颗粒尺寸>反应温度>液固比>反应时间;提取最佳工艺条件为:酸液浓度0.8 mol/L,炸药颗粒尺寸40~50目,反应温度45℃,液固比13 mL/g,反应时间90 min;所得硫酸铝产品品质较优。     

基于证据理论的煤矿瓦斯涌出组合预测

为提高煤矿瓦斯涌出量预测的准确度,引入证据理论组合预测方法。根据瓦斯涌出量及其主要影响因素间的实验数据,采用3个不同的粒子群神经网络模型对涌出量进行初步预测。并由BP、RBF网络对预测误差及预测点的影响因素进行分析建模,以获取每个模型的可信度。再利用证据理论对其进行合成,确定组合模型的权值,最终实现对瓦斯涌出量的组合预测。实例结果表明,该组合预测方法的平均绝对误差、均方误差分别为18.5%、5.8%,均小于神经网络组合法及等权平均法的相应预测误差,适用于煤矿瓦斯涌出量预测。     

火灾环境中油罐罐壁冷却优化部署研究

对油罐火灾中着火罐罐壁和邻近罐罐壁的温度分布进行了科学分析,并进行了罐壁冷却实验研究,研究不同油罐罐壁材质对冷却效果的的影响,以及有热源作用和无热源作用罐壁在不同流量冷却水作用下的温度变化规律。结果表明,不同罐壁材质对冷却效果的影响不大,在实际火灾中可忽略罐壁材质的影响;流量越大,冷却速度越快,但达到火灾冷却要求时,不同大小流量冷却水的冷却效果差异并是很大,合适的供水强度是油罐高效冷却的前提。对油罐火灾冷却力量的部署提出了优化原则,为防灭火工作提供了一定的科学依据。     

环境模型参数识别与不确定性分析

在对水文模型实例的参数不确定性分析基础上,分别采用传统灵敏度分析方法、HSY算法、线性回归等方法对模型参数特性进行了识别与比较研究.结果表明参数优化算法与传统灵敏度分析方法不能解释模型结构复杂性特征,采用不确定性分析方法对环境模型参数进行识别提供了深入分析与理解模型系统的有效途径.