Factors contributing to US chemical plant process safety incidents from 2010 to 2020

Process safety incidents can result in injuries, fatalities, environmental impacts, facility damage, downtime & lost production, as well as impacts on a company's and industry's reputation. This study is focused on an analysis of the most commonly reported contributing factors to process safety incidents in the US chemical manufacturing industry. The database for the study contained 79 incidents from 2010 to 2019, partly investigated by the Chemical Safety Board (CSB). To be included in the study, the CSB archive of incident investigations were parsed to include only incidents which occurred at a company classified as 325 in the North American Industry Classification System (NAICS), assigned to businesses that participate in chemical manufacturing. For each incident, all of the identified contributing factors were catalogued in the database. From this list of identified contributing factors, it was possible to name the ‘top three’ contributing factors. The top three contributing factors cited for the chemical manufacturing industry were found to be: design; preventive maintenance; and safeguards, controls & layers of protection. The relationship between these top contributing factors and the most common OSHA citations was investigated as well. The investigation and citation history for NAICS 325 companies in the Occupational Safety & Health Administration (OSHA) citations database was then analysed to assess whether there was any overlap between the top reported contributing factors to process safety events and the top OSHA citations recorded for the industry. A database consisting of the inspection and citation history for the chemical manufacturing industry identified by NAICS code 325 was assembled for inspections occurring between 2010 and 2020 (August). The analysis of the citation history for the chemical manufacturing industry specifically, identified that the list of the top contributing factors to process safety incidents overlapped with the most common OSHA violations. This finding is relevant to industry stakeholders who are considering how to strategically invest resources for achieving maximum benefit – reducing process safety risk and simultaneously improving OSHA citation history.     

Double Green Process: a low environmental impact method for recycling of CdTe,a-Si and CIS/CIGS thin-film photovoltaic modules

On the basis of the method for managing the end of life of CdTe photovoltaic panels previously proposed by the authors, a new method for the recycling of all types of thin-film panels (CdTe, a-Si and CIS/CIGS) has been developed and optimised under a research project founded by Enel Foundation and CRUI Foundation. The DGP process has been developed through a feasibility study carried out from three points of view: technical, environmental and economic. The process is composed by two sub-processes matched to each other, one suitable for CdTe panels (named DGPa) and the other one for a-Si and CIS/CIGS panels (DGPb). The Double Green Panel process is based mainly on mechanical treatments with a minimum use of chemicals and it is characterised by a greater level of automation and a high flexibility in production capacity. The potential environmental impacts of various configurations of the DGP process have been extensively analysed with LCA tool in order to develop an environmentally friendly process. The economic feasibility has been assessed through the Discounted Cash Flow Analysis (DCFA) method. The revenues associated to the recovery of valuable and common materials and the recycling costs have been taken into account.     

风电场环境影响评价原则及评价重点研究

针对风电场环境影响评价的评价原则及评价重点进行了探讨性的研究。提出了风电场环境影响评价应以早期介入和避让措施优先为基本原则；对风电场环境影响评价重点进行了识别，并提出风电场选址的环境可行性分析是风电场环评的重中之重；风电项目建设对生态环境的影响及风机噪声、光影影响也是风电场环境影响评价的重点，并创造性的提出风机噪声光影联合防护区的概念，以期对风电场环境影响评价提供理论及技术参考依据。     

Mitigation performance of the process protection system on the accident consequences of H2S-containing natural gas release and explosion

Toxic loads and explosion overpressure loads pose grave threats to the offshore oil and gas industry. Many safety measures are adopted to prevent and mitigate the adverse impacts caused by toxic loads and explosion overpressure loads. As a general safety barrier, the process protection system has been widely used but rarely evaluated. In order to assess the barrier ability, the mitigation performance of the process protection system is concerned in this study. Firstly, several chain accidents of H₂S-containing natural gas leakage and explosion are simulated by varying the response time of the process protection system with CFD code FLACS. Qualitative assessment is conducted based on the variation of the dangerous load profiles. Furthermore, the quantitative assessment of the mitigation performance is accomplished by considering its ability in reducing the probability of fatality. Emergency evacuation and no emergency evacuation are considered respectively in the quantitative assessment. The results prove that the process protection system takes effect on mitigating the toxic impact and explosion overpressure impact. The results also demonstrate that although the emergency evacuation may result in a severer explosion load to the operator, the process protection system can mitigate the adverse impacts regardless of whether the emergency evacuation is conducted or not.     

污染影响型土壤环境影响评价过程中预测和评价方法

随着国民经济的持续发展,建设项目开发过程中可能造成土壤本身或与之相关的环境要素之间在物质、能量转移循环过程中受到不同程度的影响。以《环境影响评价技术导则土壤环境》(HJ 964-2018)为依据,对污染型土壤环境进行环境影响评价,为建设项目土壤环境保护提供科学依据。     

河北省不同盐渍化土壤类型的微生物多样性与种群结构

为了探索盐渍化土壤中微生物多样性及群落构成，有效筛选盐渍土壤中耐盐微生物菌群.采用高通量测序技术对采集的河北省滨海盐渍土（原生盐渍化）、设施盐渍土（次生盐渍化）和高产粮田（健康土壤）3个生境的耕层土壤样本细菌和真菌多样性、群落结构、网络关系及其影响因子进行测定.结果表明，与大田土壤相比，设施土壤中OM、AP、AK、TS和EC显著升高，滨海盐渍土壤的TS和EC显著升高，其他养分指标则显著降低.细菌α多样性依次为：设施盐渍土>高产粮田>滨海盐渍土，真菌α多样性则为高产粮田显著高于设施盐渍土和滨海盐渍土.在门和属水平上分析盐渍化土壤的菌群结构，细菌群落中绿弯菌门（Chloroflexi）及其菌属和真菌群落中子囊菌门（Ascomycota）及其中有益菌Trichocladium和病原菌Fusarium为盐渍化土壤中的优势微生物类群.土壤EC和TS两个盐分因子是对细菌和真菌菌群分布贡献最大的因子，与绿弯菌门中unclassified_A4b和unclassified_Chloroflexi以及变形菌门中unclassified_α-Proteobacteria等细菌菌属和子囊菌门中Trichocladium、unclassified_Chaetomiaceae、Crassicarpon、Cephaliophora和Sodiomyces等真菌菌属呈显著正相关.研究结果为盐渍化土壤修复所需的微生物资源筛选提供了理论依据.     

Factors associated with driving under the influence of alcohol

Objective: This study examined the risk factors of driving under the influence of alcohol (DUI) among drivers of specific vehicle categories (DSC). On the basis of this research, the variables related to DUI and involvement in traffic crashes were defined. The analysis was conducted for car drivers, bicyclists, motorcyclists, bus drivers, and truck drivers.

Method: The research sample included drivers involved in traffic crashes on the territory of Serbia in 2016 (60,666). Two types of analyses were conducted in this study. Logistic regression established the correlation between DUI and DSC and the The Technique for Order of Preference by Similarity to Ideal Solution (Multi-criteria decision making) method was applied to consider the scoring and explore the potential for the prevalence of DUI on the basis of 2 data sets (DUI and non DUI).

Results: The study results showed that driver error and male drivers were the 2 most significant risk factors for DUI, with the highest scores and potential for prevalence. The nonuse of restraint systems, driver experience, and driver age are the factors with a significant prediction of involvement in an accident and an insignificant prediction of DUI.

Conclusions: Following the development of the logistic prediction models for DUI drivers, testing of the model was conducted for 3 control driver groups: Car, motorcycle, and bicycle. The prediction model with a probability greater than 50% showed that 77% of car drivers were under the influence of alcohol. Similarly, the prediction percentage for motorcyclists and bicyclists amounted to 71 and 67%, respectively. The recommendation of the study is that drivers whose DUI probability is above 50% should be potentially suspected of DUI. The results of this study can help to understand the problem of DUI among specific driver categories and detect DUI drivers, with the aim of creating successful traffic safety policy.     

Injury severity analysis of rollover crashes for passenger cars and light trucks considering temporal stability: A random parameters logit approach with heterogeneity in mean and variance

Problem: The rollover crash is a serious crash type that often causes higher injury severities. Moreover, factors that contribute to the injury severities of rollover crashes may show instabilities in different vehicle types and time periods, which requires further investigations. This study utilizes the rollover crash data in North Carolina from Highway Safety Information System (HSIS) to study the effect instabilities of factors in vehicle type and time periods in rollover crashes. Methods: The injury severities of drivers are estimated using the random parameters logit (RPL) model with heterogeneity in means and variances. Available factors in HSIS have been categorized into three groups, which are drivers, road, and environment, respectively. This study also justifies the segmentations through transferability tests. The effects of identified significant factors are evaluated using marginal effects. Results: Factors such as FWP (farm, wood, and pasture areas), unhealthy physical condition, impaired physical condition, road adverse, and so forth have shown instabilities in marginal effects among vehicle types and time periods. Practical Applications: The finding of this research could provide important references for policy makers and automobile manufactures to help mitigate the injury severity of rollover crashes.     

民用燃煤源中多环芳烃排放因子实测及其影响因素研究

民用燃煤是我国目前关注较多的多环芳烃(polycyclic aromatic hydrocarbons,PAHs)排放源.根据我国民用燃煤所涉及的煤种、燃烧方式和炉灶类型,利用全流稀释采样系统对5种不同成熟度的煤种,分别以散块煤和蜂窝煤形式在3种炉灶类型中燃烧产生的烟气进行了采样和分析,初步获取了我国民用燃煤烟气中PAHs排放因子(EFPAHs)的实测数据.结果表明,烟煤的EFPAHs在块煤燃烧方式下的变化范围为1.1～3.9 mg.kg-1,在蜂窝煤燃烧方式下的变化范围为2.5～21.1 mg.kg-1,无烟煤的EFPAHs在块煤与蜂窝煤燃烧方式下分别为0.2 mg.kg-1与0.6 mg.kg-1.在民用燃煤的EFPAHs的各种影响因素中,煤的地质成熟度起主导作用,不同成熟度煤种的EFPAHs差异幅度高达1～2个数量级.其次是燃烧形态(块煤/蜂窝煤),主要表现为同一种地质成熟度的煤在蜂窝煤燃烧方式下排放的PAHs比块煤高2～6倍.炉灶类型的影响最小.     

A data-driven narratives skeleton pattern recognition from accident reports dataset for human-and-organizational-factors analysis

Accidents in the process industry involve several interacting factors, including human and organizational factors (HOFs). A long-standing obstacle to HOFs analysis is lack of data. Accident reports are an essential data source to learn from the past and contain HOFs-related data, but they are usually unstructured text in a not standardized format. Some studies have explored the extraction of information automatically from accident reports based on Natural Language Processing (NLP) techniques. However, they were not dedicated to HOFs. Risk communication is considered an essential pillar in safety and risk science. This research develops a HOFs-focused risk communication framework based on the NLP techniques that can support risk assessment and mitigation. The proposed approach automatically extracts the target groups oriented “Who, When, Where, Why” (4Ws) information from accident reports.This framework was applied to explore the eMARS database. The results show that the “4Ws” skeleton of narratives has appreciated performance in pattern recognition and holistic information analysis. The graphical representation interfaces are designed to display the features of HOFs-related accidents, which can better be communicated to the sharp-end operators and decision-makers.