自适应蒙特卡洛法测量复合肥料中缩二脲的不确定度

建立自适应蒙特卡洛法测量复合肥料中缩二脲的不确定度评定方法。依据ISO 18643:2016,使用高效液相色谱法对复合肥料中缩二脲含量进行测定,再利用计算机编程语言(Python)编写计算程序,使用自适应蒙特卡洛(MCM)法,通过建立测量模型、分解独立输入量、评估输入量概率分布、大量采样带入计算程序等步骤完成对缩二脲含量的测量不确定度评定。评定结果表明,复合肥料样品中缩二脲含量(质量分数)为(0.1054±0.0019)%(95%包含概率)。蒙特卡罗法计算过程简单直观,与传统方法JJF 1059.1—2012《测量不确定度评定与表示》(GUM)相比,避免了复杂的数学运算评定过程,无需进行多次重复测定,是GUM法的重要补充。     

蒙特卡洛法评定橡胶门尼黏度测量的不确定度

根据高聚物流变学原理,建立了门尼黏度测量模型,分析了模型中各个变量的概率分布,利用Mathcad软件进行了测量模型的门尼黏度模拟,给出了模拟最佳值、不确定度及其包含区间,实现了门尼黏度测量不确定度的蒙特卡洛法评定。与GUM法相比,蒙特卡洛法评定门尼黏度测量不确定度具有编程模式化,过程简单等优点,适合多变量测量模型的不确定度评价。     

MCM法和GUM法评定氨气检测仪示值误差不确定度的对比

氨气检测仪广泛应用于涉氨作业场所,为确保其测量结果准确可靠必须定期对其检定校准。针对其检定过程中示值误差的测量不确定度,对测量不确定度表示指南方法(GUM)和蒙特卡罗法(MCM)进行了对比研究。建立了氨气检测仪示值误差模型,分析了模型中各个变量的概率分布,利用MCM Alchimia软件进行了测量模型模拟计算,给出了模拟最佳值、不确定度及其包含区间,实现了氨气检测仪示值误差测量不确定度的蒙特卡洛法评定。与GUM法相比,蒙特卡洛法评定氨气检测仪示值误差测量不确定度具有通用性强、过程简单、结果可靠等优点,适合氨气检测仪示值误差的不确定度评价。     

基于R语言的蒙特卡洛法在不确定度评定中的应用

以盐酸溶液浓度标定试验为例,研究了基于R语言的蒙特卡洛法(MCM)在测量不确定度评定中的实际应用.将基于R语言的MCM计算结果和《测量不确定度表示指南》GUM法进行比较分析,两种方法的计算结果基本一致.MCM算得输出量的概率密度函数(PDF)与理论正态分布函数基本重合,且输出量PDF半宽与标准不确定度的比值k=1.95...     

蒙特卡洛法评定在线pH计示值误差的不确定度

以在线p H计为例,考察了在线酸度计示值误差不确定度的分布规律,利用蒙特卡洛法评定示值误差不确定度。对于0.01级的在线p H计,蒙特卡洛法与GUM法评定结果的差值为9.1%,小于不可靠性(20%);对于0.1级的在线p H计,蒙特卡洛法与GUM法评定结果的差值为3.8%,小于不可靠性(10%)。通过比较得出结论,采用GUM法验证了蒙特卡洛法(MCM)根据JJF 1547–2015评定在线p H计示值误差不确定度的方法是有效且适用的。尤其在测量模型非线性以及输出量的概率密度函数(PDF)较大程度地偏离正态分布或t分布等GUM法不适用的场合,蒙特卡洛法是评定在线分析监测仪器仪表示值误差不确定度的重要手段。     

原子荧光光谱法测定方便米饭中砷的测量不确定度评定

介绍了原子荧光光谱法测定方便米饭中砷的测量不确定度评定方法,该法根据最小二乘法原理计算校准曲线的标准不确定度,并充分分析和识别分析过程中的不确定度来源,较为全面地评定了测量不确定度,该法对原子吸收光谱法、电感耦合等离子发射光谱法和原子荧光光谱法等测定结果的不确定度评定具有参考作用。     

化学计量不确定度评定研究进展

测量不确定度是表征合理地赋予被测量之值的分散性的参数。本文针对化学计量不确定度评定基础模型仅适用于线性模型、概率分布为正态分布或缩放位移t分布等局限,介绍了近年来不确定度评定的研究热点:蒙特卡罗方法(Monte Carlo Method,MCM),不确定度评定的来源、评定概念、评估方法及其发展过程,扩大了测量不确定度评定与表示的适用范围。     

化学计量中不确定度评定研究进展

测量不确定度是表征合理地赋予被测量之值的分散性的参数。本文针对化学计量不确定度评定基础模型仅适用于线性模型、概率分布为正态分布或缩放位移t分布等局限,介绍了近年来不确定度评定的研究热点:蒙特卡罗方法(Monte Carlo Method,MCM),不确定度评定的来源、评定概念、评估方法及其发展过程,扩大了测量不确定度评定与表示的适用范围。     

光电发射光谱法测定碳素钢中硅含量的测量不确定度评定

讨论了光电发射光谱法测定碳素钢中硅含量的各种不确定度因素,提供了不确定度评定过程所需各参数的采集和计算方法,评定了硅含量的测量不确定度.     

原子吸收光谱法测量不确定度的评定与应用

介绍了测量不确定度的一般评定方法。并以火焰原子吸收光谱法测定奶粉中铜含量为例,研究了原子吸收光谱法不确定度的评定步骤和计算过程。     

Implementation in MATLAB of the adaptive Monte Carlo method for the evaluation of measurement uncertainties

The ISO 98:1995 Guide to the expression of uncertainty in measurement (GUM) presents important application limitations. For its improvement, different supplements are being developed that will progressively enter into effect. The first of these supplements describes an alternative method for calculating uncertainties, the Monte Carlo method (MCM), which is not restricted to the conditions of the method described in the GUM: the linearity of the model and the application of the central limit theorem. MCM requires computer calculation systems for generating pseudo-random numbers and for evaluating the model a large number of times. There are software applications that have been specifically developed for calculating uncertainties, some of which include MCM; but they do not allow the user to control all factors in the process, particularly the result stabilization criteria. On the contrary, its implementation in a mathematical program for general purposes such as MATLAB, enables total control over the process, is simple and benefits from its calculation speed. This article details programming in MATLAB for the implementation of the adaptive MCM method.     

Comparison of different methods to estimate the uncertainty in composition measurement by chromatography

Natural gas is a mixture that contains hydrocarbons and other compounds, such as CO₂ and N₂. Natural gas composition is commonly measured by gas chromatography, and this measurement is important for the calculation of some thermodynamic properties that determine its commercial value. The estimation of uncertainty in chromatographic measurement is essential for an adequate presentation of the results and a necessary tool for supporting decision making. Various approaches have been proposed for the uncertainty estimation in chromatographic measurement. The present work is an evaluation of three approaches of uncertainty estimation, where two of them (guide to the expression of uncertainty in measurement method and prediction method) were compared with the Monte Carlo method, which has a wider scope of application. The aforementioned methods for uncertainty estimation were applied to gas chromatography assays of three different samples of natural gas. The results indicated that the prediction method and the guide to the expression of uncertainty in measurement method (in the simple version used) are not adequate to calculate the uncertainty in chromatography measurement, because uncertainty estimations obtained by those approaches are in general lower than those given by the Monte Carlo method.     

A comparison in the evaluation of measurement uncertainty in analytical chemistry testing between the use of quality control data and a regression analysis

The evaluation of measurement uncertainties has been widely applied to the calibration of measurement instruments, whereas its application to tests, despite increasing requirements, is a more recent phenomenon. The generalization of the evaluation of measurement uncertainties to tests has been a gradual process, in line with changes in the requirements of the normative framework that regulates the accreditation of tests laboratories and also as the perceived good practices have evolved. The sole identification of the relevant sources of uncertainty was followed by the requirement to provide a simplified estimate of the measurement uncertainty, and it is now an accepted requirement to properly evaluate the expanded measurement uncertainty associated with any tests. In this study, the evaluation of measurement uncertainty associated with the determination of sulfate in water will be attempted using a procedure that includes linear regression, with the regression parameters provided with associated uncertainties, and a Monte Carlo method applied as a validation tool of the conventional mainstream evaluation method, concerning the approximations in terms of linearization of the model and the assumed shape of the output distribution introduced by this approach.     

A Monte Carlo approach for estimating measurement uncertainty using standard spreadsheet software

Despite the importance of stating the measurement uncertainty in chemical analysis, concepts are still not widely applied by the broader scientific community. The Guide to the expression of uncertainty in measurement approves the use of both the partial derivative approach and the Monte Carlo approach. There are two limitations to the partial derivative approach. Firstly, it involves the computation of first-order derivatives of each component of the output quantity. This requires some mathematical skills and can be tedious if the mathematical model is complex. Secondly, it is not able to predict the probability distribution of the output quantity accurately if the input quantities are not normally distributed. Knowledge of the probability distribution is essential to determine the coverage interval. The Monte Carlo approach performs random sampling from probability distributions of the input quantities; hence, there is no need to compute first-order derivatives. In addition, it gives the probability density function of the output quantity as the end result, from which the coverage interval can be determined. Here we demonstrate how the Monte Carlo approach can be easily implemented to estimate measurement uncertainty using a standard spreadsheet software program such as Microsoft Excel. It is our aim to provide the analytical community with a tool to estimate measurement uncertainty using software that is already widely available and that is so simple to apply that it can even be used by students with basic computer skills and minimal mathematical knowledge.     

Monte Carlo simulation for the evaluation of measurement uncertainty of spent fuel analytical results

The present paper describes an approach based on Monte Carlo simulation for the evaluation of uncertainty of nuclear spent fuel analysis. The mathematical model of measurement was established by examining the dissolution process step by step. The results are consistent with those obtained by the classical propagation of variance approach. This paper shows the importance of taking the process into account in order to give a more reliable uncertainty assessment to the result of a concentration ratio of two isotopes in spent fuel. Indeed, for some radionuclides, the uncertainty associated with the upstream steps of the analysis (“process” uncertainty) can represent up to 95 % of the overall uncertainty.     

A comparative investigation of pressure distortion coefficient of a pneumatic piston gauge and its associated uncertainty using varied approaches

Accreditation and Quality Assurance - This paper reports the evaluation of the measurement uncertainty of the pressure distortion coefficient (λ) of a piston gauge using the Monte Carlo method...     

Problems and risks occurred during uncertainty evaluation of a quantity calculated from correlated parameters: a case study of pH measurement

Parameters of a model describing a measurement process obtained during a calibration experiment allow one to calculate a measurement result, but a simple estimation of measurement uncertainties of the parameters is not sufficient to assess the uncertainty of the result. In this paper, an example of a pH measurement conducted using an ion-selective electrode is presented, in which the uncertainty is evaluated taking into consideration the existing correlation between the parameters of the electrode. The calculations apply either covariances or correlation coefficients that have to be computed additionally. The example presented in this paper illustrates that there are some problems with rounding of variables which, because of the existing very strong correlations, significantly changes the sought uncertainty. This approach is compared with other approaches, that is, usage of uncorrelated variables and Monte Carlo simulations that are described in an earlier work. It is concluded that the approach of uncertainty evaluation, in which covariances or correlation coefficients are explicitly calculated, is work-consuming and may cause significant discrepancies between correct and obtained assessments if some roundings or approximations are done, or if the correlation coefficient is obtained experimentally based on data including random errors.     

Uncertainty evaluation for the quantification of low masses of benzo[a]pyrene: Comparison between the Law of Propagation of Uncertainty and the Monte Carlo method

A proper evaluation of the uncertainty associated to the quantification of micropollutants in the environment, like Polycyclic Aromatic Hydrocarbons (PAHs), is crucial for the reliability of the measurement results. The present work describes a comparison between the uncertainty evaluation carried out according to the GUM uncertainty framework and the Monte Carlo (MC) method. This comparison was carried out starting from real data sets obtained from the quantification of benzo[a]pyrene (BaP), spiked on filters commonly used for airborne particulate matter sampling. BaP was chosen as target analyte as it is listed in the current European legislation as marker of the carcinogenic risk for the whole class of PAHs.