Aspen Plus模拟计算在苯硝化HAZOP风险分析中的应用

为提高石油化工装置过程风险分析的准确性,建立一种将过程模拟融入危险与可操作性分析(HAZOP)的HAZOP-Aspen Plus定量风险分析方法。基于传统HAZOP分析结果,通过化工流程模拟软件Aspen Plus建立分析对象(苯硝化)的流程模型。利用灵敏度分析功能模拟过程参数对目标参数的影响,根据设备安全控制要求,获取过程操作参数的安全范围,实现HAZOP偏差定量化分析。将此方法应用于某公司硝基苯生产中硝化工段的风险分析中。结果表明,该方法可用来直观反映偏差对系统的影响,实现HAZOP偏差量化分析,进而提出有针对性的防护措施。     

操作规程HAZOP分析技术原理与应用分析

目前国内外危险与可操作(HAZOP)分析方法主要用于工艺过程风险分析,而很多伤害事故是由于人员操作过程出现偏差引起的,采用头脑风暴式的HAZOP分析可对操作规程安全性进行系统全面的系统化的审查.结合实例对比分析了操作规程HAZOP分析与工艺过程HAZOP分析的差异,对操作步骤与引导词的关系、分析成员差别、操作规程HAZOP分析风险等级确定和分析重心等问题等进行了研究分析.     

乙炔干燥变温吸附装置安全性分析与燃爆事故预防对策

针对乙炔变温吸附装置的燃爆事故问题,采用HAZOP方法对该装置进行安全性分析,找出存在的安全隐患,提出整改措施,指出装置发生燃爆事故的可能性;又将该装置可能发生的燃爆事故作为顶上事件,利用FTA方法进行分析,得到了装置发生燃爆事故的可能原因,制定了预防发生燃爆事故的对策。通过将HAZOP和FTA分析方法结合使用,有效地识别出乙炔装置存在的隐患,降低了装置操作的危险性,预防了燃爆事故的发生,值得推广应用。     

基于三角模糊数的HAZOP分析在石油化工装置中的应用

工艺危害分析强调运用系统的方法对危害进行辨识、分析,并采取必要的措施消除和减少危害。HAZOP分析能对工艺过程非常系统、全面的进行分析,但传统的HAZOP分析在量化风险时,对于偏差原因发生的可能性评价存在较大的主观性。本文对于没有统计资料的HAZOP分析偏差原因发生可能性,采用专家打分法,利用三角模糊数来表示其模糊发生概率。对于有统计资料的偏差原因,直接表示成三角模糊数。这种方法能够很好的表示HAZOP分析偏差发生概率。介绍了基于三角模糊数的HAZOP分析步骤,并在石油化工装置中进行了应用。这对HAZOP分析技术在石油化工装置中的推广具有重要意义。     

HAZOP分析技术改进研究

通过HAZOP分析技术研究,提出针对性的3方面改进措施:开发HAZOP偏差专家库,并在开发的辅助分析软件中进行自动关联提示,弥补分析成员经验不足;基于专业化的计划安排与分工控制分析过程,提高分析工作效率;简化分析范围,并借助偏差库的帮助简化重复工作。对开展HAZOP分析的时间给出了具体建议。最后,以改进的技术方案为基础,针对某装置HAZOP分析过程为例,与改进前HAZOP分析过程进行了对比。     

基于模糊数学的石油化工装置HAZOP风险分析技术研究

对传统的HAZOP分析中偏差原因发生可能性进行量化。对于有统计数据的,根据行业数据、公司经验及企业事故建立HAZOP风险分析统计数据库;对于没有统计数据的HAZOP分析偏差原因发生概率,通过专家主观评判,用模糊数理论将专家自然语言转换为模糊数,采用左右模糊排序法将模糊数转换为模糊失效概率值。研究了偏差后果严重程度的划分标准,并根据偏差原因概率和偏差后果严重程度确定风险等级,利用风险矩阵得出偏差风险的大小。从而把HAZOP分析方法从定性改进为半定量的分析方法。据此对石油化工装置进行了HAZOP风险分析。     

计算机辅助HAZOP技术的研究

在HAZOP原理的基础上,讨论了计算机辅助HAZOP技术的优势,并针对传统的HAZOP、基于深层知识模型SDG的计算机辅助HAZOP和应用PHA-Pro软件3种方法进行了分析和比较;介绍了研发成功的针对开、停车过程和应急阶段顺序颠倒、操作步骤遗漏的人工误操作危险与可操作性分析系统MO-HAZOP;该系统具有定量计算出所有的人工误操作顺序组合的发生概率值,结合现有知识和专家经验,判定事件发生的风险等级,从而有重点地给出预防危险、保障生产安全的建议的功能;MO-HAZOP分析系统对于石化企业的安全生产具有重要意义。     

HAZOP在钛酸酯工艺过程危险性辨识中的应用研究

介绍了工艺过程危险可操作研究(HAZOP)的分析程序、步骤等.根据HAZOP原理,分析了钛酸酯制备工艺过程中有意义的偏差、偏差发生的原因,可能导致的后果,并提出相应的对策措施.结果表明,HAZOP比其他定性分析方法更能全面、系统地识别工艺过程中潜在的危险因素.在此基础上,探讨了工艺过程HAZOP会议审查中可能出现的问题和解决方法,并提出借助计算机进行工艺过程HAZOP研究是今后的重点.     

基于HYSYS和HAZOP的工艺参数偏差模拟与后果量化

为解决危险与可操作性分析(HAZOP)不能有效回答工艺偏差临界范围及各种可能的偏差后果量化问题,拟将HAZOP分析与化工过程模拟软件(HYSYS)相结合,实现量化分析。以某汽提塔抽提工艺为例,对工艺参数偏差进行传统HAZOP分析及风险筛分。采用HYSYS对筛分出的高风险偏差进行动态模拟,定量描述偏差后果。结果表明:HAZOP与HYSYS相结合的量化模拟分析方法可用于指导化工装置在实际运行阶段的偏差量化分析与事故预防,为过程风险控制措施及相关建议提供可操作的量化指标。     

基于序列偏差的化工过程危险辨识

研究了化工事故中偏差连锁关系以及控制化工事故的作用连锁关系.将变化论的思想应用到HAZOP分析中,提出了一种基手序列偏差的HAZOP分析程序,用于化工过程危险辨识.在常规HAZOP分析的基础上构建序列偏差图,以此确定各个偏差的重要度,选择偏差控制策略.以某聚氯乙烯聚合工艺过程中的偏差"引发剂贮槽搅拌无"为例介绍了该方法.应用该方法认清了偏差如何发展为事故,如何选择控制策略,以预防事故发生,达到系统安全的目的.     

基于SDG模型的HAZOP分析方法在钻井作业中的应用

HAZOP分析方法是目前危险性分析领域最盛行的分析方法之一,广泛地应用于石油化工行业。但是其分析过程仅依靠专家积累的知识与经验,不仅评价的内容不严格,而且分析的可信程度有限,对实际工作的指导意义不高,不能适应工业现场的要求。鉴于HAZOP分析方法中的不足,提出了基于SDG模型的HAZOP分析方法,并利用该方法对钻井作业过程进行了危险性分析。基于SDG模型的HAZOP分析方法从复杂系统的内部逻辑入手,进行深层次的推理,不仅提高了分析效率,而且分析所得结果的完备性较好。     

HAZOP、LOPA和SIL方法的应用分析

通过概括介绍危险与可操作性分析(HAZOP)、保护层分析(LOPA)和安全完整性等级分析(SIL)三种方法的特点,总结三种分析方法之间的关系.LOPA分析是HAZOP分析的继续,可以解决HAZOP分析中残余风险不能定量化的不足,是对HAZOP分析结果的丰富和补充;SIL分析则在LOPA分析的基础上,进一步对需要增加的安全仪表系统(SIS)进行设计,并对LOPA分析结果进行验证,即HAZOP、LOPA分析是SIL分析的前期准备工作.因此,在详细介绍SIS的组成、安全生命周期阶段、SIL的选择确定方法以及SIL分析流程之前,也简要介绍了HAZOP、LOPA分析方法,梳理了两种方法的分析流程.最后通过引入示例来展示三种分析方法之间的关系.     

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.     

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.     

Using a system theory based method (STAMP) for hazard analysis in process industry

Different hazard analysis techniques are used in the chemical process industry. Most of these techniques were developed long time ago. There is an argument that significant changes have occurred in the process industry but these techniques have not been modified accordingly. Therefore, there may be limitations in identifying hazards related to the modern process industry in complex sociotechnical environment using these techniques. Recently new hazard analysis techniques based on systems theory have been introduced. STPA (System-Theoretic Process Analysis) is one of these systemic techniques. It has been used in some fields with promising results but with limited application in the process industry. This paper applies STPA to a process plant as an industrial case study. Recommendations generated by STPA are compared with the HAZOP study of the process plant which is a traditional technique. Conclusions are presented on the application of systemic techniques to the process industry as a complementary study to analyze the safety of the system as a whole and also to capture interactions between component of the system. Further studies are required to study STPA in more details and evaluate if it can be used as an alternative to HAZOP.     

基于贝叶斯网络气化炉供料系统风险分析

为解决当前气化炉供料系统风险分析不完善的状况，提出1种基于贝叶斯网络和HAZOP的风险分析模型。以某单日投煤量3 000 t级气化炉煤化工企业实际运行情况为研究对象，应用HAZOP法对其进行风险分析，并将HAZOP分析结果中各偏差产生原因转化为贝叶斯网络节点；考虑到先验知识的缺乏问题，引入Leaky Noisy OR模型，通过文献资料和相关领域专家经验知识获得先验概率，并利用贝叶斯网络进行风险分析，找出系统运行的薄弱环节。结果表明:未知因素影响会使各节点的后验概率值差异性降低，更加贴合实际；在引入未知因素影响后，系统运行薄弱环节并未发生改变。