Application of a hydroxylamine nitrate stability model to plutonium purification process equipment

A mathematical model that predicts hydroxylamine nitrate (HAN) (NH₂OH·HNO₃) stability is applied to aqueous solutions containing HAN, nitric acid and plutonium that are used in plutonium purification processes. The model estimates the stability of these solutions with respect to the rapid, hazardous, autocatalytic reaction of HAN with nitric acid that generates heat and gas. It also accounts for reaction kinetics, temperature changes, gas generation rates, solution volumes and flow rates, and distribution of plutonium and nitric acid between aqueous and organic phases. The model is applied to three typical process vessels used in solvent extraction purification of plutonium – a countercurrent aqueous/organic plutonium stripping column, an oxidation column used for HAN and hydrazine destruction, and a plutonium rework tank. Both normal and off-normal process scenarios are modeled. Two of the off-normal scenarios lead to the rapid autocatalytic reaction of HAN with nitric acid where heat and gas are generated and that could lead to damage of the process equipment and/or release of hazardous plutonium solution from the vessel. In these two cases, stationary aqueous solutions containing HAN, Pu(III), and nitric acid were allowed to slowly react until conditions for the autocatalytic reaction were reached.     

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.     

A thermal hazard risk evaluation of emulsion polymerisation and vinyl acetate monomers in the latex manufacturing process

Latex is extensively used in industrial products. However, completing some processes at scale leads to unacceptable levels of risk that need to be quantified and mitigated. Systemic risks must be eliminated wherever possible, and safety takes priority over efficiency and quality. To assess the process risks accurately, four raw materials were examined in this study: polyvinyl acetate (PVA), latex process-initiator-ammonium persulfate (APS) and hydrogen peroxide (H₂O₂), and vinyl acetate monomer (VAM). The physicochemical composition of the PVA latex process was determined via calorimeters, including differential scanning calorimetry (DSC) and vent sizing package 2 (VSP2). The calorimetry results showed that the protective colloid was a critical component in the polymerisation reaction. In addition, when adding initiators to the system, it is vital to observe the normal ratio of materials and keep the stirring system operating. The scenario system also simulated the effects of shutting down various inhibitory programs, including the build-up of free radicals that could result in a runaway reaction when the initiator was added in excess. On the other hand, the result of the risk matrix displayed as a medium level, indicating that although the probability of an accident is low, the resulting severity is at disaster level. As a result, this study provides process safety engineers with a reliable frame of reference for assessing the potential dangers in the PVA latex manufacturing process.     

丰镇发电厂送风系统噪声原因分析及降噪研究

振动和噪声普遍存在于火电厂锅炉送风系统中 ,是电厂噪声污染的一个重要来源 ,严重影响电厂安全、经济、文明运行。有效地降低送风系统的振动和噪声是火电厂急需解决的问题。笔者针对丰镇火电厂送风系统结构和运行参数 ,对由此产生的噪声进行了细致的测量 ,分析了产生振动和噪声的原因 ,认为送风机蜗舌处产生的气动噪声和管道二次涡流是引发送风系统振动和噪声的主要原因。根据产生振动和噪声的原因 ,并结合电厂要求 ,进行了技术改造即在直管中安装栅格网及弯管处安装导流栅以消除局部涡流。技术改造方案实施后经测量的结果表明 ,冷风道的噪声下降了 7～ 1 6dB ,明显降低了送风系统的振动和噪声。     

Recent developments in the application of risk analysis to waste technologies

The European waste sector is undergoing a period of unprecedented change driven by business consolidation, new legislation and heightened public and government scrutiny. One feature is the transition of the sector towards a process industry with increased pre-treatment of wastes prior to the disposal of residues and the co-location of technologies at single sites, often also for resource recovery and residuals management. Waste technologies such as in-vessel composting, the thermal treatment of clinical waste, the stabilisation of hazardous wastes, biomass gasification, sludge combustion and the use of wastes as fuel, present operators and regulators with new challenges as to their safe and environmentally responsible operation. A second feature of recent change is an increased regulatory emphasis on public and ecosystem health and the need for assessments of risk to and from waste installations. Public confidence in waste management, secured in part through enforcement of the planning and permitting regimes and sound operational performance, is central to establishing the infrastructure of new waste technologies. Well-informed risk management plays a critical role. We discuss recent developments in risk analysis within the sector and the future needs of risk analysis that are required to respond to the new waste and resource management agenda.     

Modeling thermal analysis for predicting thermal hazards relevant to transportation safety and runaway reaction for 2,2′-azobis(isobutyronitrile)

The pure decomposition behavior of 2,2′-azobis (isobutyronitrile) (AIBN) and its physical phase transformation were examined and discussed. The thermal decomposition of this self-reactive azo compound was explored using differential scanning calorimetry (DSC) to elucidate the stages in the progress of this chemical reaction. DSC was used to predict the kinetic and process safety parameters, such as self-accelerating decomposition temperature (SADT), time to maximum reaction rate under adiabatic conditions (TMR_ad), and apparent activation energy (E_a), under isothermal and adiabatic conditions with thermal analysis models. Moreover, vent sizing package 2 (VSP2) was applied to examine the runaway reaction combined with simulation and experiments for thermal hazard assessment of AIBN. A thorough understanding of this reaction process can identify AIBN as a hazardous and vulnerable chemical during upset situations. The sublimation and melting of AIBN near its apparent onset decomposition temperature contributed to the initial steps of the reaction and explained the exothermic attributes of the peaks observed in the calorimetric investigation.     

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.