Learning From Major Accidents: A Meta-Learning Perspective

Learning from the past is essential to improve safety and reliability in the chemical industry. In the context of Industry 4.0 and Industry 5.0, where Artificial Intelligence and IoT are expanding throughout every industrial sector, it is essential to determine if an artificial learner may exploit historical accident data to support a more efficient and sustainable learning framework. One important limitation of Machine Learning algorithms is their difficulty in generalizing over multiple tasks. In this context, the present study aims to investigate the issue of meta-learning and transfer learning, evaluating whether the knowledge extracted from a generic accident database could be used to predict the consequence of new, technology-specific accidents. To this end, a classi-fication algorithm is trained on a large and generic accident database to learn the relationship between accident features and consequence severity from a diverse pool of examples. Later, the acquired knowledge is transferred to another domain to predict the number of fatalities and injuries in new accidents. The methodology is eval-uated on a test case, where two classification algorithms are trained on a generic accident database (i.e., the Major Hazard Incident Data Service) and evaluated on a technology-specific, lower-quality database. The results suggest that automated algorithms can learn from historical data and transfer knowledge to predict the severity of different types of accidents. The findings indicate that the knowledge gained from previous tasks might be used to address new tasks. Therefore, the proposed approach reduces the need for new data and the cost of the analyses

Process hazard and operability analysis of BPCS and SIS malicious manipulations by POROS 2.0

The increasing interconnectivity with external networks and the higher reliance on digital systems make the facilities of the chemical, process, and Oil&Gas industry more vulnerable to cyber-attacks. These attacks have the potential of causing events with severe consequences on property, people, and the surrounding environment such as major event scenarios. The application of the currently available methodologies for cyber risk identification to complex plants with a large number of units may be demanding and cumbersome. The present study proposes an updated methodology, named POROS 2.0, that allows reducing time and effort in application by limiting the scope of the analysis to relevant cybersecurity scenarios. The latter are identified by investigating the potential escalation of consequences propagating among process and/or utility nodes of the manipulations of BPCS and SIS, similar to what is done in the HazOp technique in the safety domain. POROS 2.0 was demonstrated by the application to a case study addressing a fixed offshore platform for gas exploitation

The performance of inorganic passive fire protections: An experimental and modelling study

The installation of fireproofing materials on equipment and structures is a widely applied and effective solution for the protection of critical process elements against severe fires, in order to prevent possible damages escalation. The choice and design of fireproofing materials is crucial for granting adequate performances. As a matter of fact, properties such as, among others, thermal conductivity and density change substantially when the material is exposed to severe temperatures. In the present study, a methodological approach, integrating experimental and modelling activities, was proposed. Focus was set on a particular class of PFP: inorganic fireproofing materials. A reference set of commercial PFP materials (rock wool, glass wool, silica blanket, etc.) was selected. Small scale experiments allowed determining the variation of the most relevant thermal properties of the coatings and to obtain detailed correlation models for their description. A finite element model (FEM) was developed in order to reproduce the behaviour of real scale equipment exposed to fire and to provide a sound design of the fire protection system

Domino effects related to explosions in the framework of land use planning

The present study analyses the possible escalation due to the damage of industrial equipment containing hazardous materials loaded by pressure waves produced either by an accidental source as a Vapour Cloud Explosion, or by a voluntary external attack such as the explosion of a TNT charge located nearby the industrial facility. The results obtained evidence the similarities and the differences for the two explosion sources in terms of structural damage, loss of containment and of expected impacts on the population. In particular, a specific vulnerability assessment was carried out defining a case-study in order to evidence the different potential impact of domino effect triggered by internal process causes respect to escalation scenarios caused by external acts of interference. © 2013, AIDIC Servizi S.r.l

Damage models for storage and process equipment involved in flooding events

The present study focuses on the accidents caused by the impact of floods on storage and process equipment. This type of accident is classified as a NaTech (Natural-Technological) event and resulted in severe consequences in several past accidents. A methodology was developed for the determination of vulnerability models aimed at the estimation of equipment damage probability on the basis of severity or intensity parameters of the flooding. A mechanical model was developed, based on the comparison between the flooding intensity and the resistance of a vessel and/or its support. Simplified vulnerability functions were derived. Finally, a case-study was set up and analysed to show the potentialities of the methodology and the implementation of results in quantitative risk analysis. © 2013, AIDIC Servizi S.r.l

Assessment of Failure Frequencies of Pipelines in Natech Events Triggered by Earthquakes

During a seismic event, underground pipelines can undergo to significant damages with severe implications in terms of life safety and economic impact. This type of scenarios falls under the definition of Natech. In recent years, quantitative risk analysis became a pivotal tool to assess and manage Natech risk. Among the tools required to perform the quantitative assessment of Natech risk, vulnerability models are required to characterize equipment damages from natural events. This contribution is focused on the review of the pipeline vulnerability models available for the case of earthquakes. Two main categories of models have been identified in the literature. A first category proposes the repair rate as performance indicator for the damage of pipeline due to seismic load, and gives as output the number of required repairs per unit length. A second category proposes fragility curves associated with risk states depending on the mechanism of ground failure. In the framework of Natech risk assessment, the latter have the important advantage of having clearly and unambiguously defined the risk status (and thus the extent of the release) with which they are associated. A subset of vulnerability models deemed more appropriate to be applied in the framework of Natech risk assessment is then identified. Their application to the assessment of the expected frequencies of release events due to pipeline damage is provided, enabling their comparison and the discussion of the relative strengths and weaknesses

Industrial accidents triggered by natural hazards: an emerging risk issue

Abstract. The threat of natural hazards impacting chemical facilities and infrastructures with the subsequent release of hazardous substances has been recognised as an emerging risk which is likely to be exacerbated by the ongoing climate change. Within the European FP7 project iNTeg-Risk, efforts are dedicated to address the problem of Natech accidents by trying to understand their underlying causes and by developing methodologies and tools to assess Natech risk. Special attention is thereby given to the risk of chemical accidents triggered by earthquakes, floods and lightning. This work outlines the ongoing efforts in the development of new concepts and tools for Natech hazard and vulnerability ranking, risk assessment, risk-based design, and emergency planning and early warning

Industrial accidents triggered by natural hazards: an emerging risk issue

The threat of natural hazards impacting chemical facilities and infrastructures with the subsequent release of hazardous substances has been recognised as an emerging risk which is likely to be exacerbated by the ongoing climate change. Within the European FP7 project iNTeg-Risk, efforts are dedicated to address the problem of Natech accidents by trying to understand their underlying causes and by developing methodologies and tools to assess Natech risk. Special attention is thereby given to the risk of chemical accidents triggered by earthquakes, floods and lightning. This work outlines the ongoing efforts in the development of new concepts and tools for Natech hazard and vulnerability ranking, risk assessment, risk-based design, and emergency planning and early warning

A data-driven approach to improve control room operators' response

Digitalization has significantly improved productivity and efficiency within the chemical industry. Distributed Control Systems and extensive use of sensor networks enable advanced control strategies and increase optimization opportunities. On the other hand, chemical plants are increasingly complex, equipment is highly interlinked, and it is more difficult to describe the system dynamics through first principles. Finding the root causes of process upsets and predicting dangerous deviations in process conditions is often challenging. Advanced and dynamic tools are needed to grant safe and stable operations in such a complex and multivariate environment. In this context, Machine Learning techniques may be used to exploit and retrieve knowledge from the large amount of data that chemical plants produce and store on a daily basis. Data-driven methods may be adopted to develop predictive models and support a proactive approach to process safety. The study aims to develop Machine Learning techniques to improve the response of control room operators during critical events. Specifically, alarm data originated in an upper-tier Seveso site have been collected, cleaned, and analyzed to identify periods of intense alarm activity. Alarm behavior following operator responses has been evaluated to assess whether the actions were adequate to prevent future alarm occurrences. In doing so, alarm events that reoccur within 30 minutes after an operator acknowledgment have been identified and labeled. Subsequently, a hybrid classification algorithm was trained to predict the probability that a critical alarm reoccurs after being acknowledged by the operator. This predictive tool might be used to support the operator's decision-making process and focus his/her attention on critical alarms that are more likely to occur again in the near future

Application of multivariate statistical methods to the modelling of a flue gas treatment stage in a waste-to-energy plant

Among all the macro-pollutants released by waste combustion, acid contaminants such as sulphur dioxide, hydrogen chloride and hydrogen fluoride have the lowest emission standards in environmental regulations in EU, USA and China. Their removal is thus a key step of flue gas treatment in waste-to-energy (WtE) plants. A widespread approach for acid gas removal is by in-duct injection of dry powdered sorbents, which neutralize the acid pollutants by gas-solid reaction. However, systems based on dry injection, albeit cost-effective and easy to operate, suffer from a limited knowledge of the gas-solid reaction process at industrial operating conditions. High excess of sorbent feed rate is generally required to obtain high acid gas removal efficiencies. The present study proposes a multivariate statistical approach to the modelling of acid gas treatment units, with the aim of extracting information from real process data in order to derive a predictive model of dynamic acid gas removal efficiency. Specifically, process data regarding the composition of the flue gas, the sorbent feed and other operating conditions were elaborated to characterise the different phenomena that influence acid gas abatement. Eventually, a partial least squares (PLS) regression was set up to predict the outlet concentration of hydrogen chloride as a function of the measured process variables. The resulting model is a step forward with respect to previously available stationary models. Its simplicity and low computational cost could make PLS a promising candidate for model-based process control. Nonetheless, a linear approach such as PLS still comes short of predicting large instantaneous deviations from the typical range of operation (e.g. abrupt peaks in inlet acid gas load), for which a modification of the PLS model to incorporate non-linear behaviour is envisaged