State of research on the automation of HAZOP studies

For more than 30 years, multiple research groups have worked on the automation of hazard and operability (HAZOP) studies, or more specifically on the hazard identification process. So far, very few of these approaches have been used in the chemical process industry. Automatic hazard identification is a knowledge-intensive process that demands high standards with regard to the way in which knowledge is stored and made available. There are various suitable approaches to the qualitative modeling of processes and plants, which are the foundation for reasoning systems that are used for the identification of hazards. Additionally, there are quantitative methods that are based on process simulations and can be used to identify potential hazards. The investigation of the state of research demonstrates that there are sophisticated technologies for automated systems that include powerful reasoning techniques. The benefits and shortcomings of existing technologies are discussed with regard to their industrial applicability. Often, the quality of the necessary specific and generic knowledge is not sufficient to detect potential hazardous events and operational malfunctions. Computer-aided HAZOP systems should be integrated with computer-aided design- or process simulation software using common data models based on the digital representation of the process plant. In order to be used by HAZOP practitioners automated systems need to be comprehensive, serve as specialized decision support systems, and be tested and evaluated using round robin tests.     

基于CBR的注塑模结构智能设计系统研究

通过对基于事例推理 (CBR)技术及注塑模设计过程的分析 ,得出将CBR思想应用到注塑模结构设计中的可行性和必要性 ,提出了一个基于事例的注塑模结构智能设计系统的总体结构 ,并分析了关键技术的实现方法     

SGJY系统中推理机的设计与实现

本文所述内容是“铁路行车事故救援专家系统”（简称ＳＧＪＹ 系统）课题研究成果的一部分。文中介绍了ＳＧＪＹ系统中推理控制策略（如推理方式、推理方向及规则的搜索方式）、推理机中的解释程序及功能的设计和实现方法     

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.     

地震灾害预测中的类比推断方法

本文推荐了地震灾害预测的一个新方法:系统类此比推断法。这一方法以动力反应分析理论和实验研究成果为基础,并结合以往的震害经验建立类比推断准则,从而使震害预测工作与场地区划工作、抗震加固工作有机地结合起来。对实际企业震害预测的结果表明,这一方法效果良好。     

Turning molehills into mountains: Sleepiness increases workplace interpretive bias

Three studies draw from evolutionary theory to assess whether sleepiness increases interpretive biases in workplace social judgments. Study 1 established a relationship between sleepiness and interpretive bias using ambiguous interpersonal scenarios from a measure commonly used in personnel selection (N = 148). Study 2 explored the boundary conditions of the sleepiness–interpretive bias link via an experimental online field survey of U.S. adults (N = 433). Sleepiness increased interpretive bias when social threats were clearly present (unfair workplace) but did not affect bias in the absence of threat (fair workplace). Study 3 replicated and extended findings from the previous two studies using objective measures of sleep loss and a quasi‐experimental manipulation of minor sleep loss (N = 175). Negative affect, ego depletion, or personality variables did not influence the observed relationships. Overall, results suggest that a self‐protection/evolutionary perspective best explains the effects of sleepiness on workplace interpretive biases. These studies advance the current research on sleep in organizations by adding a cognitive “threat interpretation” bias approach to past work examining the emotional reaction/behavioral side of sleep disruption. Interpretive biases due to sleepiness may have significant implications for employee health and counterproductive behavior. Copyright © 2015 John Wiley & Sons, Ltd.