ASTAR IR-200红外三波数测油仪自校方法

针对ASTAR IR-200红外三波数测油仪,提出仪器的自校方法。     

基于GLUE法的多指标水质模型参数率定方法

目前对水质模型进行参数率定通常利用计算机算法来实现,但由于水质模型日趋复杂的非线性结构往往会导致"异参同效"现象,无法通过单个似然度判断参数的取值是否能够取得真值.为避免这一情况,本文提出了一套基于GLUE法的多目标模型参数率定方法,并以WASP水质模型的应用为例,通过Sobol方法确定模型的敏感参数,并利用DO、CBOD、氨氮、硝态氮4项指标的似然函数对参数同时进行率定.结果表明,该方法既可以有效地避免因追求"过拟合"而造成模型参数取值不当,也可以减小模型参数的不确定性,为具有"异参同效"现象的复杂模型的参数率定工作提供了一个更为可靠的方法.     

How to Constrain Multi‐Objective Calibrations of the SWAT Model Using Water Balance Components

Accurate discharge simulation is one of the most common objectives of hydrological modeling studies. However, a good simulation of discharge is not necessarily the result of a realistic simulation of hydrological processes within the catchment. We propose an evaluation framework that considers both discharge and water balance components as evaluation criteria for calibration of the Soil and Water Assessment Tool (SWAT). In this study, we integrated average annual values of surface runoff, groundwater flow, and evapotranspiration in the model evaluation procedure to constrain the selection of good model runs for the Little River Experimental Watershed in Georgia, United States. For evaluating water balance and discharge dynamics, the Nash‐Sutcliffe efficiency (NSE) and percent bias (PBIAS) were used. In addition, the ratio of root mean square error and standard deviation of measured data (RSR) was calculated for individual segments of the flow duration curve to identify the best model runs in terms of discharge magnitude. Our results indicate that good statistics for discharge do not guarantee realistic simulations of individual water balance components. Therefore, we recommend constraining the ranges of water balance components to achieve a more realistic simulation of the entire hydrological system, even if tradeoffs between good statistics for discharge simulations and reasonable amounts of the water balance components are unavoidable. Editor's note : This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.     

非甲烷总烃测定中若干问题的研究

结合环境监测工作的实际情况,应用双柱双检测器气相色谱法,比较了非甲烷总烃不同的测定方法,探讨了在实际环境监测分析中非甲烷总烃测定过程的一些关键性技术问题,对色谱条件提出了几点优化建议,发现非甲烷总烃测定结果以甲烷计更接近实际监测情况,并验证了HJ/T 38—1999和HJ 604—2011方法中2种总烃标准曲线绘制方法的等效性,但在实际环境监测中HJ 604—2011方法操作更加简捷。双柱双检测器气相色谱法测定非甲烷总烃也存在严重的不足,基于FID响应值的非甲烷总烃不能准确全面反映空气有机污染程度,可能会掩盖空气中某些有机污染。     

声发射检测灵敏度校准声源实验分析

在声发射技术的应用中,传感器的工作频率和灵敏度对AE检测的结果具有决定性的作用,传感器的校准是AE定量研究的基础,也是声发射检测技术研究中的重要课题。根据二级校准的相关要求设计了三个不同声源的实验:落球、断铅和压断毛细玻璃管法。通过重复性实验,测量了不同大小的钢球从不同高度下落;不同粗细、硬度以及不同长度的铅芯的断裂;不同粗细的毛细玻璃管被压断产生的脉冲声源,分别比较以上三个实验在不同状态条件下产生的脉冲声源信号,分析其实时特征和频谱特征(峰值,上升时间),通过计算确定其不确定度和稳定度,总结优化操作方法,同时分析了实验误差。在此基础上得出最适合做声发射传感器灵敏度校准的脉冲声源,为声发射传感器校准中的参考声源提供性能评判的依据。     

PARAMETER ESTIMATION OF THE NONLINEAR MUSKINGUM MODEL USING HARMONY SEARCH1

ABSTRACT: A newly developed heuristic algorithm, Harmony Search, is applied to the parameter estimation problem of the nonlinear Muskingum model. Harmony Search found better values of parameters in the nonlinear Muskingum model than five other methods including another heuristic method, genetic algorithm, in terms of SSQ (the sum of the square of the deviations between the observed and routed outflows), SAD (the sum of the absolute value of the deviations between the observed and routed outflows), DPO (deviations of peak of routed and actual flows), and DPOT (deviations of peak time of routed and actual outflow). Harmony Search also has the advantage that it does not require the process of assuming the initial values of design parameters. The sensitivity analysis of Harmony Memory Considering Rate showed that relatively large values of Harmony Memory Considering Rate makes the Harmony Search converge to a better solution.     

摆管淋水试验装置的校准方法探讨

文中简述了摆管淋水试验装置的基本结构及其常见的校准方式，对其中的“摆角校准”这一技术难点提出了可行的解决方案并作了较详尽的说明分析。     

CALCULATION OF NONLINEAR CONFIDENCE AND PREDICTION INTERVALS FOR GROUND-WATER FLOW MODELS1

ABSTRACT: A method is derived to efficiently compute nonlinear confidence and prediction intervals on any function of parameters derived as output from a mathematical model of a physical system. The method is applied to the problem of obtaining confidence and prediction intervals for manually-calibrated ground-water flow models. To obtain confidence and prediction intervals resulting from uncertainties in parameters, the calibrated model and information on extreme ranges and ordering of the model parameters within one or more independent groups are required. If random errors in the dependent variable are present in addition to uncertainties in parameters, then calculation of prediction intervals also requires information on the extreme range of error expected. A simple Monte Carlo method is used to compute the quantiles necessary to establish probability levels for the confidence and prediction intervals. Application of the method to a hypothetical example showed that includsion of random errors in the dependent variable in addition to uncertainties in parameters can considerably widen the prediction intervals.     

DERIVING THE NONLINEAR RISK-BENEFIT ALGORITHM FOR RESERVOIRS1

ABSTRACT: The Nonlinear Risk-Benefit (NRB) Algorithm includes risk as one of the objectives in a multiple-objective optimization problem. The NRB Algorithm is derived by extending the Surrogate Worth Trade-Off method to quadratic programming. This category of problem is common in water resources planning and design, especially multipurpose reservoir systems. Consequently, an example is given using the algorithm for optimally operating a multipurpose reservoir.     

A New Approach to Plant Diversity Assessment Combining HPLC Data, Simplex Mixture Design and Discriminant Analysis

The quantitative assessment of plant diversity and its monitoring with time represent a key environmental issue for management and conservation of natural resources. Assessment of plant diversity could be based on chemical analyses of secondary metabolites (e.g. flavonoids, terpenoids), because of the substantial quantitative and qualitative between-individual variability in such compounds. At a geographical scale, the plant populations become widely dispersed, and their monitoring from numerous routine individual analyses could become restricting. To overcome such constraint, this study develops a multivariate calibration model giving the relative frequency of a particular taxon from a simple high-performance liquid chromatography (HPLC) analysis of a plant mixture. The model was built from a complete set of mixtures combining different taxons, according to an experimental design (Scheffé’s matrix). For each mixture, a reference HPLC pattern was simulated by averaging the individual HPLC profiles of the constitutive taxons. The calibration models, based on Bayesian discriminant analysis (BDA), gave statistical relationships between the contributions of each taxon in mixtures and reference HPLC patterns of these mixtures. Finally, these models were validated on new mixtures by using outside plants. This new biodiversity survey approach is illustrated on four chemical taxons (four chemotypes) of Astragalus caprinus (Fabaceae). The more differentiated the taxon, the better predicted its contributions (in mixtures) were by BDA calibration model. This new approach could be very useful for a global routine survey of plant diversity.