Accident modelling and analysis in process industries

Development of the Quantitative Property-Consequence Relationship Model for Prediction of Hydrogen Leakage and Dispersion Using Response Surface Method and Artificial Neural Network Approaches

Hydrogen, an environmentally friendly and highly regarded future energy source, can form flammable vapor clouds upon leakage, which may transition into explosion. Predicting the dispersion behavior of hydrogen is crucial for preventing such incidents. This study aims to develop a quantitative property-consequence relationship (QPCR) model using the response surface method (RSM) and artificial neural network (ANN) to swiftly and accurately predict dispersion behavior. Initially, 8 variables were defined from source and dispersion models, constructing a data set through 6,561 PHAST simulations. Subsequently, the RSM-BBD (Box-Behnken design) and ANN-BPNN (Backpropagation neural network) models were developed, alongside a hybrid model incorporating BPNN after excluding four low-influence variables based on analysis of variance (ANOVA). All models achieved an R2 value exceeding 0.99. The hybrid model notably reduced computational costs by 97% compared to ANN-BPNN and exhibited lower mean square error (MSE). These results introduce a cost-effective approach for high-accuracy QPCR modeling and highlight the viability of diverse statistical methods.

The application of systematic accident analysis tools to investigate food safety incidents

Effective food safety (FS) management relies on the understanding of the factors that contribute to FS incidents (FSIs) and the means for their mitigation and control. This review aims to explore the application of systematic accident analysis tools to both design FS management systems (FSMSs) as well as to investigate FSI to identify contributive and causative factors associated with FSI and the means for their elimination or control. The study has compared and contrasted the diverse characteristics of linear, epidemiological, and systematic accident analysis tools and hazard analysis critical control point (HACCP) and the types and depth of qualitative and quantitative analysis they promote. Systematic accident analysis tools, such as the Accident Map Model, the Functional Resonance Accident Model, or the Systems Theoretical Accident Model and Processes, are flexible systematic approaches to analyzing FSI within a socio-technical food system which is complex and continually evolving. They can be applied at organizational, supply chain, or wider food system levels. As with the application of HACCP principles, the process is time-consuming and requires skilled users to achieve the level of systematic analysis required to ensure effective validation and verification of FSMS and revalidation and reverification following an FSI. Effective revalidation and reverification are essential to prevent recurrent FSI and to inform new practices and processes for emergent FS concerns and the means for their control.

Using fuzzy cognitive map in bow tie method for dynamic risk assessment of spherical storage tanks: A case study

The present study aimed to perform a dynamic risk assessment of liquefied petroleum gas (LPG) in spherical storage tanks using the Hazard and Operability (HAZOP) method. The "LPG release from spherical storage tank" scenario was determined to be a top event based on the risk level. The causes and consequences of this scenario were then specified using the Bow-Tie method. Additionally, fuzzy cognitive mapping (FCM) was utilized to identify the most critical threats, consequences, and effective barriers in a dynamic approach. Results showed that fracture/rupture in pipelines/a storage tank due to fire had the highest output degree and was identified as the most influential threat in the occurrence of the LPG release. Fatality/injury due to fire and explosion had the highest input degree and was more influenced by the release of LPG as the main consequence in the selected scenario. The findings of this study enabled us to make a logical decision about effective barriers for avoiding and minimizing the release of LPG and its threats and consequences based on the fuzzy cognitive map. Moreover, the results of this study showed that the FCM method could determine the most critical nodes with a higher degree and represent their relationships.

Systems Thinking Accident Analysis Models: A Systematic Review for Sustainable Safety Management

Accident models are mental models that make it possible to understand the causality of adverse events. This research was conducted based on five major objectives: (i) to systematically review the relevant literature about AcciMap, STAMP, and FRAM models and synthesize the theoretical and experimental findings, as well as the main research flows; (ii) to examine the standalone and hybrid applications for modeling the leading factors of the accident and the behavior of sociotechnical systems; (iii) to highlight the strengths and weaknesses of exploring the research opportunities; (iv) to describe the safety and accident models in terms of safety-I-II-III; and finally, to investigate the impact of the systemic models’ applications in enhancing the system’s sustainability. The systematic models can identify contributory factors, functions, and relationships in different system levels which helps to increase the awareness of systems and enhance the sustainability of safety management. Furthermore, their hybrid extensions can significantly overcome the limitations of these models and provide more reliable information. Applying the safety II and III concepts and their approaches in the system can also progress their safety levels. Finally, the ethical control of sophisticated systems suggests that further research utilizing these methodologies should be conducted to enhance system analysis and safety evaluations.

Analysis and Characterization of Risk Methodologies Applied to Industrial Parks

It is important to evaluate the risks in industrial parks and their processes due to the consequences of major accidents and especially the domino effect. Scientific works present a wide possibility of models to deal with these situations. In this work, based on the information extracted from the scientific literature, six groups of risk methodologies are defined, analyzed, and characterized with methods that cover the standards, preventive, probabilistic, traditional, modern, and dynamic evaluation that are applied or could be used in industrial parks. It also tries to achieve the objective of determining which are more appropriate if the possible situations and causes that can produce an accident are taken into account, identifying and evaluating them with characteristics of simultaneity and immediacy, determining the probability of an accident occurring with sufficient advance in time to avoid it under the use of a working operational procedure. There is no definitive methodology, and it is necessary that they complement each other, but considering the proposed objective, the integrated application of traditional methodologies together with the management of safety barriers, the dynamic evaluation of risks, and the inclusion of machine learning systems could fulfill the proposed objective.

New Risk Methodology Based on Control Charts to Assess Occupational Risks in Manufacturing Processes

The accident rate in the EU-28 region of the European Union showed a value of 2 fatal accidents per 100,000 people in 2019 that mainly affect construction (24%), manufacturing (19%) and logistics (19 %). To manage situations that affect occupational risk at work, a review of existing tools is first carried out taking into account three prevention, simultaneity and immediacy characteristics. As a result, a new dynamic methodology called Statistical Risk Control (SRC) based on Bayesian inference, control charts and analysis of the hidden Markov chain is presented. The objective is to detect a situation outside the limits early enough to allow corrective actions to reduce the risk before an accident occurs. A case is developed in a medium-density fiberboard (MDF) manufacturing plant, in which five inference models based on Poisson, exponential and Weibull distributions and risk parameters following gamma and normal distributions have been tested. The results show that the methodology offers all three characteristics, together with a better understanding of the evolution of the operators in the plant and the safety barriers in the scenario under study.

Failure Analysis Using Functional Model and Bayesian Network

A class of functional model known as multilevel flow model (MFM) is used to represent a pilot scale heat exchanger system. MFM is effective in representing chemical process qualitatively through graphical representation, but lacks the ability to quantify the impact of successes or failures of process events, and is not able to quantitatively distinguish between steps in a goal and their contributions towards achieving the main goal. To address this issue, the MFM is converted into its equivalent fault tree (FT) model to accommodate logical sequence of events along with the needed quantifications. The FT model is then converted into Bayesian network (BN) model to facilitate updates of probabilities. Using Hugin 8.1 software, the BN model is simulated to investigate the response of the process when subjected to various faults. The results highlight the capability of the model in detecting process faults and in identifying the associated root causes, thus pointing to the potentials of the proposed strategy in modeling complex chemical processes for higher level functions in plant operations such as facilitating alarm system and fault diagnosis.