Performance assessment system of health,safety and environment based on experts' weights and fuzzy comprehensive evaluation

Performance evaluation of Health, Safety and Environment (HSE) is the measurement of a company's achievement in HSE management. In order to receive a comprehensive and objective evaluation result, it is necessary to consider all evaluation factors and experts at different levels when HSE performance assessment is conducted. To improve conventional HSE performance evaluation, where weighted average method was used, a Fuzzy Comprehensive Evaluation (FCE) method is used in this study by taking experts' weights into account. Further, an HSE operating performance assessment system is designed to simplify manual and complex assessment process and generate charts and analysis reports automatically. Finally, a case of petrochemical enterprise is used to illustrate the effectiveness of the method and system.     

A hybrid model for human factor analysis in process accidents: FBN-HFACS

Human factors are the largest contributing factors to unsafe operation of the chemical process systems. Conventional methods of human factor assessment are often static, unable to deal with data and model uncertainty, and to consider independencies among failure modes. To overcome the above limitations, this paper presents a hybrid dynamic human factor model considering Human Factor Analysis and Classification System (HFACS), intuitionistic fuzzy set theory, and Bayesian network. The model is tested on accident scenarios which have occurred in a hot tapping operation of a natural gas pipeline. The results demonstrate that poor occupational safety training, failure to implement risk management principles, and ignoring reporting unsafe conditions were the factors that contributed most failures causing accident. The potential risk-based safety measures for preventing similar accidents are discussed. The application of the model confirms its robustness in estimating impact rate (degree) of human factor induced failures, consideration of the conditional dependency, and a dynamic and flexible modelling structure.     

Using fuzzy set theory to address the uncertainty of susceptibility to drought

This paper presents the technical aspects of a new methodology for assessing the susceptibility of society to drought. The methodology consists of a combination of inference modelling and fuzzy logic applications. Four steps are followed: (1) model input variables are selected—these variables reflect the main factors influencing susceptibility in a social group, population or region, (2) fuzzification—the uncertainties of the input variables are made explicit by representing them as ‘fuzzy membership functions’, (3) inference modelling—the input variables are used to construct a model made up of linguistic rules, and (4) defuzzification—results from the model in linguistic form are translated into numerical form, also through the use of fuzzy membership functions. The disadvantages and advantages of this methodology became apparent when it was applied to the assessment of susceptibility from three disciplinary perspectives: Disadvantages include the difficulty in validating results and the subjectivity involved with specifying fuzzy membership functions and the rules of the inference model. Advantages of the methodology are its transparency, because all model assumptions have to be made explicit in the form of inference rules; its flexibility, in that informal and expert knowledge can be incorporated through ‘fuzzy membership functions’ and through the rules in the inference model; and its versatility, since numerical data can be converted to linguistic statements and vice versa through the procedures of ‘fuzzification’ and ‘defuzzification’.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Trihalomethanes in Drinking Water of Greater Québec Region (Canada): Occurrence, Variations and Modelling

The levels of trihalomethanes (THMs) – the main species of by-product from water chlorination – were monitored in thedistribution systems of the five major drinking water utilitiesof the greater area of Québec City in order to investigate andmodel their occurrence on a spatial and seasonal basis. Data forTHMs and other water quality and operational parametersassociated with their formation were generated through a 16 monthsampling program involving several sites representing variablewater residence times, from the plant to the system extremity.The results demonstrate that the differences in measured THMlevels between the five utilities are mainly due to the variablequality of raw waters, the type of water treatment process beingused and the type and levels of applied disinfectant. Dependingon the utility, average THM levels were from 1.3 to 2.5 timeshigher in the system extremities than in the water leaving thetreatment plant. Also, average levels of THMs measured in summerat the distribution system extremities were, depending on theutility, from 2.5 to 5 times higher than the average levelsmeasured in winter. The seasonal differences were found to besignificantly greater than those observed by others in waterutilities in the United States and Europe and are explained inlarge part by the considerable changes, over the year, in thequality and temperature of surface waters in Southern Québec. Forthe five utilities under study, multivariate regression modelswere developed in order to predict spatial and seasonalvariations of THMs. Both residual chlorine demand and temperaturewere found to be better, statistically, as predictors for THMoccurrence. The usefulness of the developed models for routineand long term water quality management, as well as for assessmentof human exposure to THMs, are also discussed.     

linking various aspects of atmospheric change through a systems analysis of food

Although interdisciplinary collaboration to address a singleenvironmental problem is more common than in the past, all toooften the significant atmospheric problems of our day such asstratospheric ozone depletion, acidic deposition or climaticchange are addressed on a single issue basis. Systems analysis isa way of looking at a problem in a holistic, integrated fashionthrough including as many as practicable of the importantcomponents, and the linkages among them. Systems analysisoften begins with a conceptual model which, even if lackingquantification, is a useful means of changing ones thinking to amulti-issue approach. If possible, conceptual models areoperationalized by quantification (using the best availablescientific knowledge) of the stocks and flows of the relevantcomponents of the problem, and the processes that are involved.In this paper, a systems approach to food production is used tolink various atmospheric issues such as regional acidification andclimatic change. A spreadsheet model of food demand andproduction in various world regions examined the possible effectof atmospheric change on how much food we can grow, andwhether or not we may be able to meet the increased demand inthe year 2025. Using relatively modest changes in factors ofagricultural production, the spreadsheet model calculated globalshortfalls by the year 2025 of the order of 10 to 20% in someimportant agricultural crops, despite the improvements in cropproduction factors that are envisaged by the Food andAgricultural Organization from now until the year 2010, and thatwere extrapolated in this paper to 2025. The model alsocalculated that climatic change in combination with eithertropospheric ozone or increased UV-B radiation caused bydepletion of the stratospheric ozone layer may in general makethe situation worse than in the case of climatic change alone.Given the large uncertainties in the input data, the results in thispaper should not be viewed as predictions but rather as anexample of taking a relatively simple systems approach to foodproduction using a spreadsheet model, and calculating the effectsthat various aspects of atmospheric change might have upon it.Therefore, it is extremely important to know the effects uponcrop production factors of climatic change, tropospheric ozoneand increased UV-B radiation not only as individual issues, butalso of their combined effect since it is probable that in manyregions they will occur in combination.     

应用模糊综合指数法对伊犁河（伊宁段）水质的综合评价

模糊数学法对伊犁河(伊宁段)水质进行综合评价时,由于分别采用地面水环境质量标准中五类标准值的平均值及其中三个标准值,计算后的结果不一致,而模糊综合指数法避免了这类问题的出现,计算结果比较满意.从而表明,该法是对模糊数学法的进一步改进.     

Application of CFD Methods for Modelling in Air Pollution Problems: Possibilities and Gaps

Different urban air pollution problems deal with complex structure of air flows and turbulence. For such problems the Computer Fluid Dynamics (CFD) methods become widely used. However, this approach despite a number of advantages has some problems. Experience of use of CFD tools for development of models and suggestions of their applications for a local scale air pollution over a complex terrain and stable stratification are discussed in this paper, including: Topography and complex geometry: choose of the co-ordinate system and computer grid; Turbulence closure for air pollution modelling: modified k- model for stable stratified ABL; Boundary conditions for vertical profiles of velocity for stable-stratified atmosphere; Effects of the radiation and thermal budget of inclined surfaces to dispersion of pollutants; Artificial sources of air dynamics and circulation.Some examples of CFD applications for air pollution modelling for a flat terrain, mountainous area, mining open cast and indoor ventilation are discussed. Modified k- model for stably-stratified ABL is suggested. Due to the isotropic character of the k- model a combination of it in vertical with the sub-grid turbulence closure in horizontal can be more suitable for ABL. An effective scheme of boundary conditions for velocity profiles, based on the developed similarity theory for stable-stratified ABL, is suggested. Alongside with the common studies of atmospheric dispersion, the CFD methods have also demonstrated a good potential for studying anthropogenic and artificial-ventilation sources of air dynamic and circulation in local-scale processes of air pollution.     

Multipoint source method in air pollution modelling of cold start emission

The paper presents a new method of air pollution modelling on a micro scale. For estimation of concentration of car exhaust pollutants, each car is treated as an instantaneous moving emission source. This approach enables us to model time and spatial changes of emission, especially during cold and cool start of an engine. These stages of engine work are a source of significant pollution concentration in urban areas. In this work, two models are proposed: one for the estimation of emission after cold start of the engine and another for the prediction of pollutant concentration. The first model (defined for individual exhaust gas pollutants) enables us to calculate the emission as a function of time after the cold or cool start, ambient temperature and average speed of motion. This model uses the HBEFA database. The second mathematical model is developed in order to calculate the pollutant dispersion and concentrations. The finite volume method is applied to discretise the set of partial differential equations describing wind flow and pollutant dispersion in the domain considered. Models presented in this paper can be called short-term models on a small spatial scale. The results of numerical simulation of pollutant emission and dispersion are also presented.     

基于AHP-MF模型的用户群节水指标体系研究

从分析影响用户群节水的因素及其构成关系入手,根据专家长期节水工作的经验,利用层次分析法对用户群节水指标进行优选,并提出全面的定量化和定性化的层次结构评价模式和总体评价方法.首先构建用户群节水的层次分析结构模型及判断矩阵,应用Matlab6.5计算出各判断矩阵的最大特征值及其特征向量,并检验判断矩阵的一致性,继而确定出各指标的权重;其次,应用Fuzzy统计的方法对陕西2006年度高校用户群的节水情况进行评价,并为节水工作提供建设性意见.