Accident modelling and analysis in process industries

Accident modelling is a methodology used to relate the causes and effects of events that lead to accidents. This modelling effectively seeks to answer two main questions: (i) Why does an accident occur, and (ii) How does it occur. This paper presents a review of accident models that have been developed for the chemical process industry with in-depth analyses of a class of models known as dynamic sequential accident models (DSAMs). DSAMs are sequential models with a systematic procedure to utilise precursor data to estimate the posterior risk profile quantitatively. DSAM also offers updates on the failure probabilities of accident barriers and the prediction of future end states. Following a close scrutiny of these methodologies, several limitations are noted and discussed, and based on these insights, future work is suggested to enhance and improve this category of models further.     

Modelling fertility: an application of count regression models

Often the lifecycle data occur as count of the vital events and are recorded as integers. The purpose of this article is to model the fertility behavior based on religious, educational, economic, and occupational characteristics. The responses of classified groups according to these determinants are examined for significant influence on fertility using Poisson regression model (PRM) based on the National Family Health Survey -3 dataset. The observed and predicted probabilities under PRM indicate modal value of two children for the Poisson distribution modeled data. Presence of dominance of two child in the data motivates the authors to adopt multinomial regression model (MRM) in order to link fertility with various socioeconomic indicators responsible for fertility variation. Choice of the explanatory factors is limited to the availability of data. Trends and patterns of preference for birth counts suggest that religion, caste, wealth, female education, and occupation are the dominant factors shaping the observed birth process. Empirical analysis suggests that both the models used in the study perform similarly on the sample data. However, fitting of MRM by taking birth count of two as comparison category shows improved Akaike information criterion and consistent Akaike information criterion values. Current work contributes to the existing literature as it attempts to provide more insight into the determinants of Indian fertility using Poisson and MRM.     

环境模型在风险评估中的应用

环境模型方法是化学品风险评估中经常使用的方法。环境模型可提供化学品的浓度、归趋和主要迁移转化途径等信息,目前已被广泛应用于各类环境系统中。本文对该方法使用的一般步骤进行介绍,并对我国的环境模型工作提出了几点建议。     

Revealing photon transmission in an ultraviolet reactor: Advanced approaches for measuring fluence rate distribution in water for model validation

Fluence rate (FR) distribution (optical field) is of great significance in the optimal design of ultraviolet (UV) reactors for disinfection or oxidation processes in water treatment. Since the 1970s, various simulation models have been developed, which can be combined with computational fluidic dynamic software to calculate the fluence delivered in a UV reactor. These models strive for experimental validation and further improvement, which is a major challenge for UV technology in water treatment. Herein, a review of the simulation models of the FR distribution in a UV reactor and the applications of the current main experimental measurement approaches including conventional flat-type UV detector, spherical actinometer, and micro-fluorescent silica detector (MFSD), is presented. Moreover, FR distributions in a UV reactor are compared between various simulation models and MFSD measurements. In addition, the main influential factors on the FR distribution, including inner-wall reflection, refraction and shadowing effects of adjacent lamps, and turbidity effect are discussed, which is helpful for improving the accuracy of the simulation models and avoiding dark regions in the reactor design. This paper provides an overview on the simulation models and measurement approaches for the FR distribution, which is helpful for the model selection in fluence calculations and gives high confidence on the optimal design of UV reactors in regard to present methods.     

低分子量有机酸提取土壤中部分重金属的拟合模型研究

为研究不同浓度低分子量有机酸与提取的土壤重金属含量之间的关系,本文以采自安徽省安庆市某铜矿区及其周围的土壤为例,通过浸提法,研究了不同浓度苹果酸、甲酸对采样点土壤中重金属Cu、Mn、Zn、Pb提取能力的影响并进行模型拟合.结果表明:不同浓度苹果酸和甲酸对采样点土壤中重金属的提取量不同,两种有机酸对低pH、高重金属含量采样点及其周围土壤中的重金属提取量较大,两种低分子量有机酸对S6采样点的重金属Mn的提取量最大,分别为322.5 mg·kg~(-1)和193.58 mg·kg~(-1),提取率达70.02%和42.03%,Zn、Pb次之,Cu最低;随着苹果酸、甲酸浓度的升高,土壤中重金属Cu、Mn、Zn、Pb的提取量增加,苹果酸对土壤中重金属的提取能力大于甲酸;拟合模型对于两种低分子量有机酸与其提取的土壤中部分重金属含量之间的关系,具有较好的拟合性,其决定系数R~2在0.8293～0.9990之间,都具有显著性(p0.05),该模型可以定量表征低分子量有机酸对土壤重金属的提取能力,此研究结果为土壤中重金属的固定化和增强植物修复受重金属污染的土壤提供了理论支持.     

液氨储罐泄漏扩散模型的改进研究

针对液氨储罐孔洞泄漏的实际工况,综合考虑泄漏及由于泄漏导致液氨闪蒸造成 的罐压变化,以及储罐的许用压力,对其连续泄漏过程进行了分析,以此改进现有的泄漏扩 散后果分析模型,获得较忽略这些因素更为严重的后果.最后针对灾难性事故发生前连续泄 漏最大持续时间的影响因素进行了分析.结果表明,环境温度与初始罐压对其影响较大,是决定...     

数值模拟技术在二沉池设计中的应用

二沉池的运行状况对整个污水处理系统的处理效果有重要影响。沉淀池的处理效果与沉淀池内水流特性密切相关。传统的沉淀池设计基于理想沉淀池假设和静置沉淀实验,对沉淀池内的水流特性考虑不多,设计参数选择范围宽,经验性强,不能保证沉淀池内的泥水分离和浓缩效果。计算流体力学是一种较为成熟的数值模拟技术,已广泛应用于水处理构筑物在设计条件下的内部水流特性和行为的研究,为预测沉淀池的运行状况和结构改进的效果提供了有效手段。本文对沉淀池内的水流特性和流体力学在沉淀池研究中的应用进行了描述。     

Development of the gap model ZELIG-CFS to predict the dynamics of North American mixed forest types with complex structures

When the development of gap models began about three decades ago, they became a new category of forest productivity models. Compared with traditional growth and yield models, which aim at deriving empirical relationships that best fit data, gap models use semi-theoretical relationships to simulate biotic and abiotic processes in forest stands, including the effects of photosynthetic active radiation interception, site fertility, temperature and soil moisture on tree growth and seedling establishment. While growth and yield models are appropriate to predict short-term stemwood production, gap models may be used to predict the natural course of species replacement for several generations. Because of the poor availability of historical data and knowledge on species-specific allometric relationships, species replacement and death rate, it has seldom been possible to develop and evaluate the most representative algorithms to predict growth and mortality with a high degree of accuracy. For this reason, the developers of gap models focused more on developing simulation tools to improve the understanding of forest succession than predicting growth and yield accurately.In a previous study, the predictions of simulations in two southeastern Canadian mixed ecosystem types using the ZELIG gap model were compared with long-term historical data. This exercise highlighted model components that needed modifications to improve the predictive capacity of ZELIG. The updated version of the model, ZELIG-CFS, includes modifications in the modelling of crown interaction effects, survival rate and regeneration. Different algorithms representing crown interactive effects between crowns were evaluated and species-specific model components that compute individual-tree mortality probability rate were derived. The results of the simulations were compared using long-term remeasurement data obtained from sample plots located in La Mauricie National Park of Canada in Quebec. In the present study, three forest types were studied: (1) red spruce-balsam fir-yellow birch, (2) yellow birch-sugar maple-balsam fir, and (3) red spruce-balsam fir-white birch mixed ecosystems. Among the seven algorithms that represented individual crown interactions, two better predicted the changes in basal area and individual-tree growth: (1) the mean available light growing factor (ALGF), which is computed from the proportion of light intercepted at different levels of individual crowns adjusted by the species-specific shade tolerance index, and (2) the ratio of mean ALGF to crown width. The long-term predicted patterns of change in basal area were consistent with the life history of the different species.     

The influences of climate and hydrology on population dynamics of waterbirds in the lower Murrumbidgee River floodplains in Southeast Australia: Implications for environmental water management

Restoration of waterbird diversity and abundance is a key objective of river system management in Australia. Therefore, understanding the effects of climatic and hydrological variables on waterbird population dynamics is fundamental for successful river restoration programs. We investigated the population dynamics of waterbirds (total abundance) and seven functional waterbird groups in the floodplains of lower Murrumbidgee River. We found a general declining abundance trend from 1983 to 2007, except for the deep water foragers. We modelled the relative contribution of the climatic and hydrological factors to waterbird population decrease using the generalized additive model (GAM) framework after identifying the negative binomial distribution. Most of the seven functional groups were positively related to both annual rainfall and water usage, defined as the total water volume intercepted by the river reach, and the models indicated that rainfall was slightly more important. Temperature also played a role in waterbird abundance: the maximum summer temperature negatively influenced the abundance of dabbling ducks, shoreline foragers and fish eaters, while the minimum winter temperature positively affected the abundance of dabbling ducks and shoreline foragers. Overall, our results support the practice of providing environmental water for sustaining waterbird populations. However, environmental water provision is likely to be most effective when timed to coincide with antecedent rainfall.     

Scaling-up spatially-explicit ecological models using graphics processors

How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to efficiently solve spatially explicit ecological models at large spatial scale using the CUDA language extension. We explain this technique by implementing three classical models of spatial self-organization in ecology: a spiral-wave forming predator-prey model, a model of pattern formation in arid vegetation, and a model of disturbance in mussel beds on rocky shores. Using these models, we show that the solutions of models on large spatial grids can be obtained on graphics processors with up to two orders of magnitude reduction in simulation time relative to normal pc processors. This allows for efficient simulation of very large spatial grids, which is crucial for, for instance, the study of the effect of spatial heterogeneity on the formation of self-organized spatial patterns, thereby facilitating the comparison between theoretical results and empirical data. Finally, we show that large-scale spatial simulations are preferable over repetitions at smaller spatial scales in identifying the presence of scaling relations in spatially self-organized ecosystems. Hence, the study of scaling laws in ecology may benefit significantly from implementation of ecological models on graphics processors.