氨制冷压力容器定期检验相关问题分析

随着大量制冷装置的建成和使用,氨制冷企业安全问题日益突出,定期检验作为保证在役氨制冷压力容器安全使用的一个重要环节,目前仍存检验依据不清、检验项目不明、检验针对性不足等问题.文中对氨制冷压力容器定期检验现状进行了介绍,对氨制冷压力容器损伤机理进行了分析和讨论,从损伤模式判别的角度提出了相应的检验策略,给相关人员提供一些...     

Effects of calibration on L-THIA GIS runoff and pollutant estimation

Urbanization can result in alteration of a watershed's hydrologic response and water quality. To simulate hydrologic and water quality impacts of land use changes, the Long-Term Hydrologic Impact Assessment (L-THIA) system has been used. The L-THIA system estimates pollutant loading based on direct runoff quantity and land use based pollutant coefficients. The accurate estimation of direct runoff is important in assessing water quality impacts of land use changes. An automated program was developed to calibrate the L-THIA model using the millions of curve number (CN) combinations associated with land uses and hydrologic soil groups. L-THIA calibration for the Little Eagle Creek (LEC) watershed near Indianapolis, Indiana was performed using land use data for 1991 and daily rainfall data for six months of 1991 (January 1-June 30) to minimize errors associated with use of different temporal land use data and rainfall data. For the calibration period, the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The calibrated CN values were used for validation of the model for the same year (July 1-December 31), and the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The Nash-Sutcliffe coefficient was 0.52 for January 1, 1991 to December 31, 1991 using uncalibrated CN values. As shown in this study, the use of better input parameters for the L-THIA model can improve accuracy. The effects on direct runoff and pollutant estimation of the calibrated CN values in the L-THIA model were investigated for the LEC. Following calibration, the estimated average annual direct runoff for the LEC watershed increased by 34%, total nitrogen by 24%, total phosphorus by 22%, and total lead by 43%. This study demonstrates that the L-THIA model should be calibrated and validated prior to application in a particular watershed to more accurately assess the effects of land use changes on hydrology and water quality.     

环境监测分析中校准曲线应用和检验方法的探讨

本文阐述了环境监测分析中校准曲线的分类 ,拟合方法和应用范围 ,提出对拟合标准曲线可靠性检验的概念和方法。如 :相对剩余标准差 ,截距、斜率检验 ,失拟方差检验等 ,这些方法在实际监测分析中具有一定的应用价值     

Excel软件在石化环境监测质量控制中的应用

应用Excel软件制作石化环境监测质量控制校准曲线，可节省大量的时间和化学试剂，提高工作效率。这种方法操作简便、快速、直观，并且比以前校准曲线制作数据处理的方法有所创新。     

对甲醛法测定二氧化硫的疑议

采用3种方法分别制作的SO2校准曲线无显著差异，且斜率一致，测定标准样品均在保证值范围之内，说明省略标定NaSO3的过程是可行的，同时对甲醛法给定的浓度范围和斜率值进行质疑。     

Multi-group approximation, scattering and calibration coefficients, uncertainty estimates and detection limits of a NaI(Tl)-based gamma spectrometry set-up for low-level activity analysis

This paper describes a quantitative radioactivity analysis method especially suitable for environmental samples with low-level activity. The method, consisting of a multi-group approximation based on total absorption and Compton spectra of gamma rays, is coherently formalized and a computer algorithm thereof designed to analyze low-level activity NaI(Tl) gamma ray spectra of environmental samples. Milk powder from 1988 was used as the example case. Included is a special analysis on the uncertainty estimation. Gamma sensitiveness is defined and numerically evaluated. The results reproduced the calibration data well, attesting to the reliability of the method. The special analysis shows that the uncertainty of the assessed activity is tied to that of the calibration activity data. More than 77% of measured 1461-keV photons of ⁴⁰K were counted in the range of clearly lower energies. Pile-up of single line photons (¹³⁷Cs) looks negligible compared to that of a two-line cascade (¹³⁴Cs). The detection limit varies with radionuclide and spectrum region and is related to the gamma sensitiveness of the detection system. The best detection limit always lies in a spectrum region holding a line of the radionuclide and the highest sensitiveness. The most radioactive milk powder sample showed a activity concentration of 21 ± 1 Bq g⁻¹for ¹³⁷Cs, 323 ± 13 Bq g⁻¹ for ⁴⁰K and no ¹³⁴Cs.     

Data fusion modeling for groundwater systems

Engineering projects involving hydrogeology are faced with uncertainties because the earth is heterogeneous, and typical data sets are fragmented and disparate. In theory, predictions provided by computer simulations using calibrated models constrained by geological boundaries provide answers to support management decisions, and geostatistical methods quantify safety margins. In practice, current methods are limited by the data types and models that can be included, computational demands, or simplifying assumptions. Data Fusion Modeling (DFM) removes many of the limitations and is capable of providing data integration and model calibration with quantified uncertainty for a variety of hydrological, geological, and geophysical data types and models. The benefits of DFM for waste management, water supply, and geotechnical applications are savings in time and cost through the ability to produce visual models that fill in missing data and predictive numerical models to aid management optimization. DFM has the ability to update field-scale models in real time using PC or workstation systems and is ideally suited for parallel processing implementation. DFM is a spatial state estimation and system identification methodology that uses three sources of information: measured data, physical laws, and statistical models for uncertainty in spatial heterogeneities. What is new in DFM is the solution of the causality problem in the data assimilation Kalman filter methods to achieve computational practicality. The Kalman filter is generalized by introducing information filter methods due to Bierman coupled with a Markov random field representation for spatial variation. A Bayesian penalty function is implemented with Gauss–Newton methods. This leads to a computational problem similar to numerical simulation of the partial differential equations (PDEs) of groundwater. In fact, extensions of PDE solver ideas to break down computations over space form the computational heart of DFM. State estimates and uncertainties can be computed for heterogeneous hydraulic conductivity fields in multiple geological layers from the usually sparse hydraulic conductivity data and the often more plentiful head data. Further, a system identification theory has been derived based on statistical likelihood principles. A maximum likelihood theory is provided to estimate statistical parameters such as Markov model parameters that determine the geostatistical variogram. Field-scale application of DFM at the DOE Savannah River Site is presented and compared with manual calibration. DFM calibration runs converge in less than 1 h on a Pentium Pro PC for a 3D model with more than 15,000 nodes. Run time is approximately linear with the number of nodes. Furthermore, conditional simulation is used to quantify the statistical variability in model predictions such as contaminant breakthrough curves.     

Tentative guidelines for studying the behaviour of p,p'‐DDT under field and laboratory conditions

Abstract

The objectives of the IPP Project—Periodic Inspection on Crop Sprayers—are to develop methods for sprayer certification, analyze quality on spray operation, propose an inspection system for crop sprayers in Brazil, improve environmental quality on spray operation, and reduce costs on chemical control for plant protection systems. Periodic inspections on crop sprayers are performed in several countries and are compulsory in most of them, and it is becoming an important tool for improvement and optimization of use of chemicals. The IPP Project in Brazil is funded by FAPESP—Fundação de Amparo à Pesquisa do Estado de São Paulo. The results so far showed that all the sprayers presented failures. However, most of them could be approved with minor services. As an example, 56.6% of the sprayers with more than 2 years of use presented leaks, 47% of them had damaged hoses and 80.5% presented bad tips (nozzles). These results indicate the need for better procedures of use and maintenance of sprayers, justifying the periodic inspection system.     

Selecting parameters for calibration via sensitivity analysis: An individual-based model of mosquitofish population dynamics

A stochastic individual-based model (IBM) of mosquitofish population dynamics in experimental ponds was constructed in order to increase, virtually, the number of replicates of control populations in an ecotoxicology trial, and thus to increase the statistical power of the experiments. In this context, great importance had to be paid to model calibration as this conditions the use of the model as a reference for statistical comparisons. Accordingly, model calibration required that both mean behaviour and variability behaviour of the model were in accordance with real data. Currently, identifying parameter values from observed data is still an open issue for IBMs, especially when the parameter space is large. Our model included 41 parameters: 30 driving the model expectancy and 11 driving the model variability. Under these conditions, the use of “Latin hypercube” sampling would most probably have “missed” some important combinations of parameter values. Therefore, complete factorial design was preferred. Unfortunately, due to the constraints of the computational capacity, cost-acceptable “complete designs” were limited to no more than nine parameters, the calibration question becoming a parameter selection question. In this study, successive “complete designs” were conducted with different sets of parameters and different parameter values, in order to progressively narrow the parameter space. For each “complete design”, the selection of a maximum of nine parameters and their respective n values was carefully guided by sensitivity analysis. Sensitivity analysis was decisive in selecting parameters that were both influential and likely to have strong interactions. According to this strategy, the model of mosquitofish population dynamics was calibrated on real data from two different years of experiments, and validated on real data from another independent year. This model includes two categories of agents; fish and their living environment. Fish agents have four main processes: growth, survival, puberty and reproduction. The outputs of the model are the length frequency distribution of the population and the 16 scalar variables describing the fish populations. In this study, the length frequency distribution was parameterized by 10 scalars in order to be able to perform calibration. The recently suggested notion of “probabilistic distribution of the distributions” was also applied to our case study, and was shown to be very promising for comparing length frequency distributions (as such).     

Analysis of competition models in periodic medium

We propose special models (the so-called D-systems) with periodic delta-functions being used as the growth velocities of the populations. This allows to build, in a practically explicit form, corresponding Poincare mapping and to reveal a series of “paradoxical” phenomena and selection criteria in the dynamics of the competitors. For the analysis of non-autonomous models, new general principle of inheritance of a series of local properties by the global Poincare shift mapping is worked up. Specifically, key heritable properties are determined for non-autonomous competition models. The selection criterion for competitive selection, based on the concept of the universal allowance constant, is derived in this work.