Ontology-based computer aid for the automation of HAZOP studies

Hazard and Operability (HAZOP) studies are conducted to identify and assess potential hazards which originate from processes, equipment, and process plants. These studies are human-centered processes that are time and labor-intensive. Also, extensive expertise and experience in the field of process safety engineering are required. There have been several attempts by different research groups to (semi-)automate HAZOP studies in the past. Within this research, a knowledge-based framework for the automatic generation of HAZOP worksheets was developed. Compared to other approaches, the focus is on representing semantic relationships between HAZOP relevant concepts under consideration of the degree of abstraction. In the course of this, expert knowledge from the process and plant safety (PPS) domain is embedded within the ontological model. Based on that, a reasoning algorithm based on semantic reasoners is developed to identify hazards and operability issues in a HAZOP similar manner. An advantage of the proposed method is that by modeling causal relationships between HAZOP concepts, automatically generated but meaningless scenarios can be avoided. The results of the enhanced causation model are high quality extended HAZOP worksheets. The developed methodology is applied within a case study that involves a hexane storage tank. The quality and quantity of the automatically generated results agree with the original worksheets. Thus the ontology-based reasoning algorithm is well-suited to identify hazardous scenarios and operability issues. Node-based analyses involving multiple process units can also be carried out by a slight adjustment of the method. The presented method can help to support HAZOP study participants and non-experts in conducting HAZOP studies.     

基于案例推理的露天矿山排土场滑坡事故预警方法研究

通过整理100组排土场滑坡的案例库,提出了基于案例推理(CBR)的排土场滑坡中长期预警方法,采用框架法表示滑坡案例,RBF神经网络和欧式距离相结合实现案例的检索,由用户和专家来完成案例的修正与调整。在高村排土场进行了实际应用,通过案例检索获得了相似的案例,确定了预警等级及合理的处置方案,为排土场滑坡事故预防提供了科学指导。     

基于二维灰云模型的LNG动力船航行过程风险推理*

针对风险多维属性问题及船舶航行多种风险表现,提出基于二维灰云模型的LNG动力船航行过程风险推理方法。针对LNG使用及船舶航行2种作业方式,采用系统方法识别安全风险影响因素体系,确定2种作业方式下各风险体系指标变化权重,描述LNG动力船航行过程中LNG使用和船舶航行发生事故概率及后果;通过引入云模型,综合计算2种作业方式下概率及后果组合风险,并采用灰云推理2种作业方式同时存在时LNG动力船航行过程风险。结果表明:LNG动力船航行过程中LNG使用和船舶航行风险耦合,风险程度受控;二维灰云模型可有效分析多维属性下风险评估问题,以及不同作业下风险耦合作用。研究结果可为船舶航行过程风险定量评估提供依据。     

Discrimination and bias in the vegan ideal

The vegan ideal is entailed by arguments for ethical veganism based on traditional moral theory (rights and/or utilitarianism) extended to animals. The most ideal lifestyle would abjure the use of animals or their products for food since animals suffer and have rights not to be killed. The ideal is discriminatory because the arguments presuppose a male physiological norm that gives a privileged position to adult, middle-class males living in industrialized countries. Women, children, the aged, and others have substantially different nutritional requirements and would bear a greater burden on vegetarian and vegan diets with respect to health and economic risks, than do these males. The poor and many persons in Third World nations live in circumstances that make the obligatory adoption of such diets, where they are not already a matter of sheer necessity, even more risky.Traditional moral theorists (such as Evelyn Pluhar and Gary Varner whose essays appear in this issue) argue that those who are at risk would beexcused from a duty to attain the virtue associated with ethical vegan lifestyles. The routine excuse of nearly everyone in the world besides adult, middle-class males in industrialized countries suggests bias in the perspective from which traditional arguments for animal rights and (utilitarian) animal welfare are formulated.     

环境污染应急处置技术的CBR-MADM两步筛选法模型

突发环境污染事件进行应急处置时,需要在历史案例库和处置技术库的基础上通过筛选模型筛选出最适宜的应急处置技术.其中筛选的效率和准确性是构建技术筛选模型的首要因素,目前尚无较为满意的解决方案.本文结合案例推理技术(CBR)和模糊多属性群决策模型(MADM)的优势,建立了环境污染应急处置技术的CBR-MADM两步筛选法模型:第1步,先利用基于熵权G1法的CBR推理从案例库中匹配案例,再从相似度最高的几个案例中提取应急处置技术作为备选技术;第2步,利用MADM对备选技术进行筛选和决策.CBR-MADM两步筛选法既充分利用了历史处置经验,又极大地提高了应急处置技术的筛选速度和效率.将该方法应用于2012年底山西长治浊漳河苯胺泄漏污染事件中,成功筛选出“投加混凝剂-活性炭坝拦截”技术,同实际情况吻合,验证了其适用性和可行性.     

基于案例推理的地铁非常规突发事件应急决策方法研究

针对地铁非常规突发事故发生时,由于地下环境复杂且封闭,很难获取较全面、准确的事件信息等问题,非常规突发事件的地铁应急决策是一个复杂的过程。通过对地铁事件原则性机理进行分析和研究,得到地铁发生重大火灾事故情景分析的贝叶斯网络节点变量,借鉴CBR模型的思想,将基于结构和属性的双重情景检索的事故案例推理应急决策方法应用到地铁火灾非常规突发事件应急决策中,该方法排除了因属性值缺失造成相似度无法计算或计算有误的情况,可为地铁交通应急平台的后期建设提供理论支持     

道路交通环境中驾驶疲劳的生成模型研究

为预防由驾驶疲劳引起的交通事故,有必要研究在道路、交通和环境的综合影响下驾驶疲劳的生成机理。基于生理、心理学中的经典理论,借鉴国内外相关的研究成果,采用理论推理的方法对驾驶疲劳生成过程中驾驶员唤醒水平的变化规律及其影响因素进行分析。在此基础上建立了驾驶疲劳的生成模型,并将模型应用于工程实际。通过驾驶员唤醒水平的变化,指出驾驶疲劳的生成时刻,及其对驾驶时间的规定和道路、景观设计的影响。该模型以唤醒水平为核心,描述驾驶疲劳生成过程中驾驶员唤醒水平的变化规律,强调道路交通环境对驾驶员唤醒水平的影响。     

基于模糊规则推理的商业综合体火灾情景构建方法研究

为了提高商业综合体火灾扑救效率,在分析描述情景结构组成的基础上,设计了商业综合体火灾情景分层网络模型,将复杂的商业综合体火灾化为层次清晰的模型。引入了模糊规则推理理论,分析了商业综合体火灾情景构建的流程,运用模糊推理理论的合成推理规则方法对商业综合体火灾进行情景规则的推理建立,并构建火灾情景发展网络链路图,实现火灾情景状态发展可视化。     

基于案例推理的化工园区应急资源需求预测

为确定化工园区突发事故的应急资源需求,提出了一种基于案例推理的化工园区应急资源需求预测方法。该方法主要由三部分组成:(1)基于案例推理的模型构建;(2)化工园区事故的案例描述;(3)基于人工神经网络的案例适应。最后,以石化园区火灾爆炸事故的应急资源需求预测验证该方法的有效性。研究表明,该方法可以实时地对化工园区应急资源需求进行预测,为化工园区的应急资源储备和配置提供支持。     

基于模糊层次分析法和证据推理的铁路应急预案评价研究

为解决铁路应急预案评价中存在的不确定性问题,提出基于证据推理的应急预案评价方法,从完整性、可操作性、针对性、协作性和可持续性5个维度构建应急预案评价指标体系;采用模糊层次分析法对指标权重进行计算,并通过证据推理对专家的评价数据进行证据合成,得到各预案评价等级分布;将各预案评价等级分布与评价效用结合,最终得到各预案评价排序。结果表明:实例分析表明,提出的评价方法对于处理不确定多属性的评价问题具有较强的可行性和适用性。