Integrated Hazard Identification(IHI):A Quick Accident Analysis and Quantification Method for Practitioners

There are many techniques for hazard identification and are divided into shortcut,standard and advanced techniques.Among these,HAZOP and What-If techniques are mostly engaged by practitioners in the chemical process industry.Both of these have certain advantages and limitations,i.e.,HAZOP is structured,and what-if covers broad range of scenarios.There is no hazard identification method,which can cover a broad range of scenarios and is structured in nature.For this purpose,a new technique namely integrated hazard identification(IHI)is proposed in this article that integrates HAZOP and What-If.The methodology is demonstrated via hazard identification study of urea synthesis section.Risk ranking is used to sort out the worst-case scenario.This worst-case scenario is further studied in detail for quantification that is performed using the ALOHA software.This quantification has assisted to detect ammonia concentrations in nearby control room and surroundings for worst-case scenario.It is revealed that if ammonia pump is not stopped within 10 minutes,concentration inside and outside the control room may reach to 384 ppm and 2630 ppm,compared to 1100 ppm(AEGL-3).Thus the proposed method would be easy,time saving and covers more details and would be handy for practicing engineers working in different chemical process industries.

A hybrid deep neural network based on multi-time window convolutional bidirectional LSTM for civil aircraft APU hazard identification

Safety is one of the important topics in the field of civil aviation. Auxiliary Power Unit(APU) is one of important components in aircraft, which provides electrical power and compressed air for aircraft. The hazards in APU are prone to cause economic losses and even casualties. So,actively identifying the hazards in APU before an accident occurs is necessary. In this paper, a Hybrid Deep Neural Network(HDNN) based on multi-time window convolutional neural network-Bidirectional Long Short-Term Memory(CNN-Bi LSTM) neural network is proposed for active hazard identification of APU in civil aircraft. In order to identify the risks caused by different types of failures, the proposed HDNN simultaneously integrates three CNN-Bi LSTM basic models with different time window sizes in parallel by using a fully connected neural network. The CNN-Bi LSTM basic model can automatically extract features representing the system state from the input data and learn the time information of irregular trends in the time series data. Nine benchmark models are compared with the proposed HDNN. The comparison results show that the proposed HDNN has the highest identification accuracy. The HDNN has the most stable identification performance for data with imbalanced samples.

Hazard identification and risk assessment of Abuja rail mass transit

Due to multiple advantages in terms of capacity,safety,dependability and punctuality,rail mass transit plays a vital role in easing urban traffic congestion.In addition,rail mass transit is environmentally friendly.Even though it is beneficial to passengers in urban areas,security issues have become increasingly serious in recent years.To improve the safety and dependability of the rail mass transit system to some extent and limit the loss to a bare minimum,it is necessary to identify and evaluate the hazards connected with rail mass transit in the early stages of the system’s operation.Against this backdrop,this research employs a riskmatrix approach to examine the dangers connected with Abuja’s rail mass transit system(ARMT).The questionnaire is designed and delivered to 100 participants,including personnel and passengers,to get their perceptions of hazards at the ARMT.The questionnaire is the initial step in the research process.The four safety variables discussed in this study are human error,equipment design failure,environmental conditions and management.Human error is the most significant factor covered in this research.Understanding the source of risk stemming from these four safety variables at the early stages of rail mass transit operation can significantly improve the system’s overall safety and reliability,which can be paramount.According to the findings of the investigations,human-related activity is the most significant possible cause of risk at that ARMT,with a high level of risk portrayed by the data.

Formal Safety Assessment of a Marine Seismic Survey Vessel Operation,Incorporating Risk Matrix and Fault Tree Analysis

In maritime safety research,risk is assessed usually within the framework of formal safety assessment(FSA),which provides a formal and systematic methodology to improve the safety of lives,assets,and the environment.A bespoke application of FSA to mitigate accidents in marine seismic surveying is put forward in this paper,with the aim of improving the safety of seismic vessel operations,within the context of developing an economically viable strategy.The work herein takes a close look at the hazards in North Sea offshore seismic surveying,in order to identify critical risk factors,leading to marine seismic survey accidents.The risk factors leading to undesirable events are analysed both qualitatively and quantitatively.A risk matrix is introduced to screen the identified undesirable events.Further to the screening,Fault Tree Analysis(FTA)is presented to investigate and analyse the most critical risks of seismic survey operation,taking into account the lack of historical data.The obtained results show that man overboard(MOB)event is a major risk factor in marine seismic survey operation;lack of training on safe work practice,slippery deck as a result of rain,snow or water splash,sea state affecting human judgement,and poor communication are identified as the critical risk contributors to the MOB event.Consequently,the risk control options are focused on the critical risk contributors for decision-making.Lastly,suggestions for the introduction and development of the FSA methodology are highlighted for safer marine and offshore operations in general.

Quantified Risk Assessment for Plants Producing and Storing Explosives

This paper presents a methodology for risk assessment of plants producing and storing explosives. The major procedural steps for quantified risk assessment （QRA） in explosive plants are the following： hazard identification, accident sequence modeling, data acquisition, accident sequence quantification, consequence assessment and integration of results. This methodology is demonstrated and applied in an explosive plant consisting of four separate units, which produce detonating cord, nitroglycol, dynamites and ammonium nitrate fuel oil （ANFO）. A GIS platform is used for depicting individual risk from explosions in this plant. Total individual risk is equal to 1.0 × 10^-4/y in a distance of 340m from the center of the plant, and 1.0 × 10^-6/y in a distance of 390m from the center of the plant.

Study of occupation health risk assessment on Chinese coal mine

Factors of occupation health hazard were identified and analyzed, and indexes system of occupation health risk assessment were established by applying fuzzy theory and system safety technique, the weights of index system were obtained by AHP, finally a reasonable mathematics model of occupation health risk assessment was accomplished by an example.

Safe production model for small mines

Presented a 'safe production model' that can be adopted by small mine opera- tors to achieve their production targets safely and efficiently.The model consists of eight elements ranging from management commitment and leadership to safety account-ability and communication.The model is developed considering the mine operators' resource limitations and the workers' training needs.The study concludes with a summary of a sample survey that is conducted to validate the model and estimate a parameter for each mine and determine its position in the safe production scale.

Risk-Identification-Based Hybrid Method for Estimating the System Reliability of Existing Jacket Platforms Under Fire

This paper proposes a risk-identification-based hybrid method for estimating the system reliability of steel jacket structures under fire.The proposed method starts with risk identification;according to the results of hazard identification and Dow’s fire and explosion index(F&EI) methodology,the most dangerous hazard sources are determined.In term of each equipment layout in steel jacket structures,fire load is imposed and elasto-plastic analysis is performed.According to the deformed state of steel jacket structures,the weakest failure mode of steel jacket structures is identified.In order to know the effect on ultimate bearing capacity of the offshore structural system,a series of elasto-plastic analyses are performed in which single failure element contained in the weakest failure mode is removed from the whole offshore platform structural system.Finally,the failure function of the steel jacket structure is generated and the failure probability of the steel jacket structure system is estimated under fire by genetic algorithm via MATLAB program.

Risk perception and safety analysis on petroleum production system of three gas fields in Bangladesh

Accidents are common in the petroleum industry.The risk of accidents can be easily minimized by understand-ing the harm early in the production system.This study presents a perception-based risk and safety analysis of petroleum production systems.Data were collected from three fields operated by Sylhet Gas Fields Limited in Bangladesh.The Statistical Package for the Social Sciences(SPSS)software was used to analyze the data.The results were then subjected to a frequency analysis,an analysis of variance(ANOVA),and a reliability analysis.The frequency analysis indicated the overall safety situation,and the ANOVA models and reliability analysis sub-stantiated the results.A chi-squared test indicated the association between the datasets.The outcomes of the risk matrix indicated various risk levels,such as low,moderate,and high.According to the implicit risks,necessary measures were recommended for the industry’s future.