Joint analysis of process and operator performance in chemical process operational safety

Independent studies of case histories by the Health and Safety Commission in the UK and by a Honeywell led industrial consortium world-wide showed that human errors represent the major cause of failure in process plant operation. In contrast to this discovery the majority of previous studies on computer aided systems for fault detection and diagnosis has focused on the process side. This paper presents a methodology, which can involve human factors into the development of systems for automatic identification and diagnosis of abnormal operations and develops methods and techniques that can be used to simultaneously capture, characterise and assess the performance of operators as well as of the process. A joint process–operator simulation platform is developed which is used as a test-bed for carrying out the study. The process part is a simulator, which simulates in high fidelity the dynamic behaviour of the process that is subject to the influence of various disturbances and operators’ interventions. The operator module is developed as a real-time expert system, which emulates operator’s behaviour in interpretation of received signals, and planning and execution of decisions. The interaction between the two modules is managed through an interaction module, which handles the real-time exchange of data using Dynamic Data Exchange. The interaction module also contains the toolkits for analysing the dynamic behaviour of the joint process–operator system. The method and system are illustrated using a simulated case study.     

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.     

How can process safety and a risk management approach guide pandemic risk management?

The coronavirus disease (COVID-19) brought the world to a halt in March 2020. Various prediction and risk management approaches are being explored worldwide for decision making. This work adopts an advanced mechanistic model and utilizes tools for process safety to propose a framework for risk management for the current pandemic. A parameter tweaking and an artificial neural network-based parameter learning model have been developed for effective forecasting of the dynamic risk. Monte Carlo simulation was used to capture the randomness of the model parameters. A comparative analysis of the proposed methodologies has been carried out by using the susceptible, exposed, infected, quarantined, recovered, deceased (SEIQRD) model. A SEIQRD model was developed for four distinct locations: Italy, Germany, Ontario, and British Columbia. The learning-based approach resulted in better outcomes among the models tested in the present study. The layer of protection analysis is a useful framework to analyze the effect of different safety measures. This framework is used in this work to study the effect of non-pharmaceutical interventions on pandemic risk. The risk profiles suggest that a stage-wise releasing scenario is the most suitable approach with negligible resurgence. The case study provides valuable insights to practitioners in both the health sector and the process industries to implement advanced strategies for risk assessment and management. Both sectors can benefit from each other by using the mathematical models and the management tools used in each, and, more importantly, the lessons learned from crises.     

Alternative method to prevent thermal runaway in case of error on operating conditions continuous reactor

Thermal runaway was studied in a continuous tubular pilot reactor under steady-state regime. Different accident scenarii were conducted by making some errors on reactant concentrations and/or temperature feed. To prevent thermal runaway, control by direct contact by solvent injection was used at different reactor locations. This injection allowed controlling the maximum reaction temperature. A simplified analytical method to estimate the maximum reaction temperature along the reactor was used.Benefit of this control method was the diminution of computational time. Furthermore, by injecting solvent to control maximum reaction temperature, there is no need to shut down the unit. The control method was validated experimentally.     

A methodology for identifying and addressing dead-legs and corrosion issues in a Process Hazard Analysis (PHA)

Identifying dead-legs and related corrosion issues continues to be a challenge in the process industry. Pipeline corrosion has been a factor in several recent incidents involving releases and fires. A review of incident reports and citations over the past ten years indicates that Process Hazard Analysis (PHA) revalidations have been noted for not addressing the hazards of a process including corrosion mechanisms and dead-legs. In order for the hazards to be addressed, they must first be accurately identified in a PHA and documented along with any recommended actions for preventive maintenance. This paper describes a methodology for identifying and addressing dead-legs and related corrosion issues in a PHA that can be used to update corporate PHA procedures to be more robust in preventing corrosion related incidents.     

AB法污水处理工艺概况

ＡＢ法（生物吸附－氧化）近年来成为发展最快的城市污水处理工艺，在我国受到广泛重视，本文主要介绍ＡＢ工艺基本原理及运用ＡＢ工艺的污水处理厂处理情况．     

危险废物处理处置技术评价方法研究

在对不同危险废物处理处置技术评价方法适用性进行分析的基础上,对危险废物处理处置技术评价方法进行了系统研究.结果发现专家评价法和环境技术评价(EnTA)法以定性评价为主,简单易行,应用范围广泛;生命周期评价(LCA)法以定量为主,但由于数据采集困难,目前在应用上还面临很多困难;层次分析(AHP)法把定性与定量评价融合得比较好,对于解决危险废物处理处置技术的选择问题行之有效.     

废水厌氧处理研究前沿和趋势探讨

废水厌氧处理作为一种有竞争力的、经济的、日益成熟的生物处理工艺,是一种集环境保护、能源回收和生态良性循环于一体的技术,具有良好的社会、经济、环境效益,其研究和应用日益受到重视,工程化推广应用极其广泛。本文讨论了废水厌氧反应器的发展历程、厌氧氨氧化生物脱氮技术、两相生物反应器、高硫酸盐有机废水厌氧处理技术,并对目前废水厌氧处理的热点问题及发展趋势进行了展望。     

SBR工艺设计经验探讨

提出了SBR工艺中,反应池容积、反应时间以及需氧量的计算方法和设计经验。同时,在研读和对比国内外相关资料的基础上,探讨了SBR工艺发生污泥膨胀的可能性和原因,介绍了控制方法与措施。