The role of situation awareness in accidents of large-scale technological systems

In the last two decades, several serious accidents at large-scale technological systems that have had grave consequences, such as that at Bhopal, have primarily been attributed to human error. However, further investigations have revealed that humans are not the primary cause of these accidents, but have inherited the problems and difficulties of working with complex systems created by engineers. The operators have to comprehend malfunctions in real time, respond quickly, and make rapid decisions to return operational units to normal conditions, but under these circumstances, the mental workload of operators rises sharply, and a mental workload that is too high increases the rate of error. Therefore, cognivitive human features such as situation awareness (SA)—one of the most important prerequisite for decision-making—should be considered and analyzed appropriately. This paper applys the SA Error Taxonomy methodology to analyze the role of SA in three different accidents: (1) A runaway chemical reaction at Institute, West Virginia killing two employees, injuring eight people, and requiring the evacuation of more than 40,000 residents adjacent to the facility, (2) The ignition of a vapor cloud at Bellwood, Illinois that killed one person, injured two employees, and caused significant business interruption, and (3) An explosion at Ontario, California injuring four workers and caused extensive damage to the facility. In addition, the paper presents certain requirements for cognitive operator support system development and operator training under abnormal situations to promote operators’ SA in the process industry.     

驾驶员雨中面对不同颜色障碍物时制动点选择研究

为研究雨天驾驶员对障碍物颜色的感知特征,保障雨天驾驶安全,利用仿真驾驶平台,研究大雨、中雨、小雨3种状况下,驾驶员面对红、白、黑3种颜色障碍物时的鸣笛点(与障碍物距离设为L1)、刹车点(与障碍物距离设为L2)的选择和行驶初速度(V1)与通过障碍物最小速度(V2)的关系。最后,依据模拟驾驶采集得到的23位驾驶员经过各颜色障碍物的驾驶数据,通过SPSS分析L2,V1的拟合关系,结合安全制动距离模型,建立不同雨量下驾驶员受障碍物颜色影响的行车安全模型。结果表明:运行速度相同时,红色障碍物对应的L1,L2值明显大于黑色和白色对应的值;面对同种颜色的障碍物时,随着雨量的增加,V2值不断减小,而V1越大,V2值越大。     

连续型空间故障树中因素重要度分布的定义与认知

为了解工作环境条件因素对系统故障概率的影响的特征,同时丰富连续型空间故障树(CSFT)的理论框架,提出因素重要度分布的概念。因素重要度分布从经典故障树的概率重要度发展而来,目的是研究系统所处环境因素变化导致系统可靠性变化的程度。给出元件和系统的因素重要度分布概念、公式及所需基础数据,并分析因素重要度分布的正负分布特点。使用该概念研究元件X1和系统T的因素t重要度分布和因素c重要度分布。结果表明:在不同环境中,对于因素t或c变化影响元件或系统的故障概率变化程度是不同的。因素重要度分布有效地表达了t和c对元件或系统的故障概率影响特征。     

绿灯倒计时信号对驾驶行为的影响

为揭示绿灯倒计时信号对交叉口交通安全的影响,针对不同交通负荷与信号控制方式下的驾驶行为开展研究。选择大连市内的倒计时信号交叉口与非倒计时信号交叉口各2处,开展车辆行驶速度及不良驾驶行为调查。用数理统计方法,对比分析2类交叉口在平峰与高峰时段的车辆行驶速度特性,同时对比分析上述不同条件下闯黄灯及违规变道行为的发生数量。研究表明,绿灯倒计时信号在绿灯时间末期,会诱发一部分驾驶员加速行驶通过交叉口;绿灯倒计时信号在平峰时段对闯黄灯行为数量的影响较小,而对违规变道行为的数量有显著影响。     

工贸行业安全生产预警指数数学模型构建

通过对《冶金等工贸行业安全生产标准化基本规范评分细则》的仔细解读,将安全预警指标分解成13个一级指标和38个二级指标,对应指标给以相应的经过归一化处理后的权重。针对这十三个安全预警指标建立了数学量化模型,给出了计算方法和安全预警指数的状态划分。结合GM(1,1)灰色预测理论和等维新息模型,构建适合工贸企业的预测模型,对未来的安全生产预警指数作出预测,帮助工贸企业实时掌握安全生产动态,做到科学合理决策。     

不同pH值对磷矿废石磷素浸出特性影响的实验研究

采用EPA 1314pH酸碱预滴定实验和pH浸出实验,研究了pH对磷矿废石磷元素浸出特性的影响,以及磷矿废石的酸碱缓冲容量。实验结果表明:磷矿废石的碱缓冲容量小,pH从8.75上升到13.50消耗了0.1mmol/g的NaOH,磷矿废石酸的缓冲容量较大,pH从5.50到1.00,共消耗2.2mmol/g的H2SO4;浸提液的酸碱性是影响磷矿废石中磷元素浸出的重要参数,酸性条件下很大程度上促进了磷元素的浸出,随着H+离子浓度的增加,总磷浓度随之升高。相反在碱性条件下的反应过程中,OH-阻碍磷元素的浸出,消耗正磷酸根离子和羟基磷酸根离子,使其生成沉淀,有一定的固磷作用,为磷元素的固化提供了更多途径。     

佛山市土地可持续利用评价研究

基于资源合理性、经济可行性、生态安全性三个角度选取17项指标构建指标体系,通过熵值法计算各指标的权重,求取非资源合理性（ NREI）、经济可行性（ EEI）、非生态安全性（ NESI）3项综合指数,运用改进的三角模型对佛山市2004年－2011年土地可持续利用状态和趋势展开分析和评价。研究表明：佛山市土地可持续状态总体较差,并且呈现不断恶化趋势。     

湘潭锰矿重金属环境安全及植物耐性研究

采集了湘潭锰矿红旗分矿开采区、沙圹村恢复区的代表性当季蔬菜(莴笋叶Fruticicolidae、小白菜Brassica chinensis、香葱Allium schoenoprasum、空心菜Ipomoea aquatica)、废弃区的优势植物(商陆Phytolacca acinosa、野茼蒿Crassocephalum crepidioides、苍耳Xanthium sibiricum)和3个研究区的土壤,通过原子吸收分光光度法分析了Mn、Pb、Zn含量。结果表明:开采区蔬菜Mn含量(8.3~84.5 mg/kg)明显高于恢复区(2.7~55.6 mg/kg),开采区和恢复区蔬菜都明显受到Pb污染(0.6~33mg/kg),蔬菜Zn含量范围为1.9~6.5mg/kg;3个研究区域土壤重金属均明显超标,最严重的是Pb污染(1 993.5~2 213.5mg/kg)。商陆、野茼蒿和苍耳中重金属含量差异较大,对重金属的耐性强,其中商陆表现出最好的耐性与长势。研究结论对锰矿土地合理利用以及矿区土壤重金属治理提供一定的科学依据。     

Cybersecurity and dynamic operation in practice: Equipment impacts and safety guarantees

Though dynamic operation of chemical processes has been extensively explored theoretically in contexts such as economic model predictive control or even considering the potential for cyberattacks on control systems creating non-standard operating policies, important practical questions remain regarding dynamic operation. In this work, we look at two of these with particular relevance to process safety: (1) evaluating dynamic operating policies with respect to process equipment fidelity and (2) evaluating procedures for determining the parameters of an advanced control law that can promote both dynamic operation as well as safety if appropriately designed. Regarding the first topic, we utilize computational fluid dynamics and finite element analysis simulations to analyze how cyberattacks on control systems could impact a metric for stress in equipment (maximum Von Mises stress) over time. Subsequently, we develop reduced-order models showing how both a process variable and maximum Von Mises stress vary over time in response to temperature variations at the boundary of the equipment, to use in evaluating how advanced control frameworks might impact and consider the stress. We close by investigating options for obtaining parameters of an economic model predictive control design that would need to meet a variety of theoretical requirements for safety guarantees to hold. This provides insights on practical safety aspects of control theory, and also indicates relationships between control and design from a safety perspective that highlight further relationships between design and control under dynamic operation to deepen perspectives from the computational fluid dynamics and finite element analysis discussions.     

Risk assessment of safety level in university laboratories using questionnaire and Bayesian network

Accidents in university laboratories not only create a great threat to students’ safety but bring significant negative social impact. This paper investigates the university laboratory safety in China using questionnaire and Bayesian network (BN) analysis. Sixteen influencing factors for building the Bayesian net were firstly identified. A questionnaire was distributed to graduate students at 60 universities in China to acquire the probability of safe/unsafe conditions for sixteen influencing factors, based on which the conditional probability of four key factors (human, equipment and material, environment, and management) was calculated using the fuzzy triangular theory and expert judgment. The determined conditional probability was used to develop a Bayesian network model for the risk analysis of university laboratory safety and identification of the main reasons behind the accidents. Questionnaire results showed that management problems are prominent due to insufficient safety education training and weak management level of management personnel. The calculated unsafe state probability was found to be 65.2%. In the BN analysis, the human factor was found to play the most important role, followed by equipment and material factor. Sensitive and inferential analysis showed that the most sensitive factors are personnel incorrect operation, illegal operation, and experiment equipment failure. Based on the analysis, countermeasures were proposed to improve the safe management and operation of university laboratories.