安全风险分级防控在油气开发领域的探索与应用

为推动油气开发领域安全风险分级防控有效落地,提高风险预控能力,本文基于风险防控现状评估结果,探索性地研究安全风险分级防控思路,构建了油气开发领域安全风险分级防控体系推进模型,提出了逆向与正向危害因素识别方法,明确了风险评价关键隐性过程原则,构建了风险-后果-能资分级防控决断模型。选取典型试点单位进行实践,结果证明,油气开发领域安全风险分级防控体系推进模型是科学合理的,其他领域也可借鉴该模型开展安全风险分级防控工作。     

数据挖掘在预测组织事故防控效果中的应用*

为进一步探索数据挖据技术在组织事故预防工作中的融入性与适用性,基于24Model构建事故预控基础模型,通过预测准确率数值及接受者操作特性曲线（ROC曲线）对比分析随机森林（RF）、支持向量机(SVM)、决策树（DT）与神经网络（NN）4种方法对组织事故防控效果的预测性能。结果表明:针对事故率控制（Y1）、职业危害预防（Y2）、财产损失3类预测目标（Y3）,RF方法均能达到较高的准确率及稳定性,具有较优的预测性能。根据特征重要度（FI）排序,明确对组织事故水平影响最显著的因素为安全实践活动认知（SC5）及安全管理程序文件（SMS3）,FI值均大于0.150 0。研究结果可为有效预测组织事故防控效果提供方法依据,同时为企业安全工作的规划设计提供思路。     

废铅酸蓄电池污染防治技术及相关政策分析

废铅酸蓄电池具有一定的回收价值,但生产过程和回收过程都容易造成污染。为了引导企业对废旧电池进行正确处理,也为了防止环境被破坏,必须采取科学合理的技术和工艺,并遵循国家相关政策,本文就对此进行了分析,希望能给相关工作提供借鉴。     

研究冷却塔噪声环境影响评价及污染防治措施

随着我国社会经济的发展速度不断加快,城市化建设层次越来越高,大量中央空调设备的冷却塔噪声影响到了人们的日常生活质量。基于此,本文重点针对冷却塔噪声环境的影响进行了分析和研究,提出了相关的有效防治策略,以此有效提高人们的生活质量和舒适度。     

环境中的微塑料:赋存、检测及其危害

近年来,由个人护理品及废旧塑料直接或间接产生的微塑料不断地在各种环境介质中被检出,且微塑料会对生态系统产生各种危害,因此对微塑料的研究受到越来越广泛的关注。阐述了微塑料在水体、沉积物、沙滩和生物体中的赋存情况,介绍了微塑料的采集与分离方法,以及定性与定量分析方法。指出微塑料对环境及生物体产生的危害,提出现阶段研究存在的主要问题,并对今后的研究方向进行了展望。     

双重预防机制在家具制造业的应用与实践

为了减少安全事故的发生,进一步加强和规范企业生产现场的安全管理,探索双重预防机制在企业安全管理中的应用,结合家具制造企业安全生产现状,运用风险分级管控与隐患排查的相关理论,详细论述了企业构建双重预防体系的要点及方法,并提出了双重预防体系的监督与考核机制。实践结果证明,双重预防机制是一个统一的有机结合体,只有将风险分级管控体系和隐患排查治理体系有机结合,才能将导致事故发生的根源性因素进行有效识别和管控,双重预防机制建设也是一个动态管理的过程,需要及时进行监督、考核和调整。     

石油化工催化剂生产过程中的职业病防治及应急处理

石化企业使用了大量的催化剂,催化剂开发、生产过程复杂,有许多原料和生产工艺,存在许多潜在的安全隐患和职业病危害因素。为预防催化剂生产过程中的事故,通过分析催化剂生产过程中的职业病危害分析,提出了相应的职业病防范及应急处置措施,加强催化剂生产过程中的职业病防治和应急处置能力,创造良好的经济、生态和社会效益,有效防止催化剂生产过程中发生安全事故和职业病。     

基于社会技术系统的复工企业疫情防控风险路径研究*

为对复工企业疫情防控中的风险致因因素进行分析,探寻复工企业疫情防控的风险路径,基于社会技术系统理论,建立宏观工效学模型,通过查阅并分析国家、地方政府、企业文件以及相关文献,从人员、技术、组织管理、内部环境及外部环境5个子系统出发,识别出16个复工企业疫情失控致因因素;运用社会网络分析（SNA）构建复工企业疫情防控关系网络,对各因素节点中心度进行计算,分析各节点在网络中的位置及影响程度;采用贝叶斯网络（BN）进行参数学习和推理学习,找出复工企业疫情失控的最大致因链。结果表明:复工企业疫情失控关系网络中,存在政府监督管理不力→防疫物资筹备不足→日常监管不到位,政府监督管理不力→ 防疫宣传、培训缺失→员工防疫知识欠缺,政府监督管理不力→防疫宣传、培训缺失→日常监管不到位3条最长风险路径。研究结果可帮助复工企业针对最长风险路径中的因素进行管理,从而有效地为复工企业防疫提供理论支持。     

A Bayesian network-based approach for the assessment and management of ageing in major hazard establishments

Currently, there is an increasing attention towards ageing of industrial equipment, as the phenomenon has been recognised as a cause of severe accidents, recorded in the last years in many process establishments. Recent studies described ageing through a number of key-factors affecting the phenomenon by accelerating or slowing it down. The Italian Competent Authority for the prevention of chemical accidents (Seveso III Directive) adopted a short-cut method, accounting for the assessment of these factors, to evaluate the adequateness of ageing management during inspections at Seveso sites. In this paper, a Bayesian Network was developed, by using the data gathered during the first application of the short-cut method, with the aim to verify the robustness of the approach for ageing assessment and the validity of the a priori assumptions used in assessing the key-factors. The structure of the Bayesian network was established by using experts’ knowledge, whereas the Counting Learning algorithm was adopted to execute the parameter learning by means of the software Netica. The results showed that this network could effectively explore the complex logical and uncertain relationships amongst factors affecting equipment ageing. Results of the present study were exploited to improve the short-cut method.     

Inherent thermal runaway hazard evaluation method of chemical process based on fire and explosion index

Thermal runaway hazard assessment provides the basis for comparing the hazard levels of different chemical processes. To make an overall evaluation, hazard of materials and reactions should be considered. However, most existing methods didn't take the both into account simultaneously, which may lead the assessment to a deviation from the actual hazard. Therefore, an integrated approach called Inherent Thermal-runaway Hazard Index (ITHI) was developed in this paper. Similar to Dow Fire and Explosion Index(F&EI) function, thermal runaway hazard of chemical process in ITHI was the product of material factor (MF) and risk index (RI) of reaction. MF was an indicator of material thermal hazards, which can be determined by initial reaction temperature and maximum power density. RI, which was the product of probability and severity, indicated the risk of thermal runaway during the reaction stage. Time to maximum rate under adiabatic conditions and criticality classes of scenario were used to indicate the runaway probability of the chemical process. Adiabatic temperature rise and heat of the desired reaction and secondary reaction were used to determine the severity of runaway reaction. Finally, predefined hazard classification criteria was used to classify and interpret the results obtained by this method. Moreover, the method was validated by case studies.