Needs and Challenges of Nigeria Petroleum Industry in Assessing Process Safety Cumulative Risk

A cross-sectional exploratory assessment of the needs and challenges of petroleum industry in Nigeria, in assessing process safety cumulative risk for major accidents prevention was investigated. A purposive cum random sampling technique was used in this study, among selected petroleum companies operating in Nigeria. Survey questionnaires were received from 216 participants made up of asset integrity engineers/operators, process safety experts, production safety professionals in the petroleum industry in Nigeria. Data analyses were carried out to cover descriptive and inferential statistics. Overall, the study recognized that assessing process safety cumulative risk is not a simple process due largely to the changing nature of safety critical barriers degradation data. The study result showed four main challenges faced by petroleum industries in Nigeria, in assessing process safety cumulative risk: 1) the study showed that 94% of the respondents agreed that there is limited accessibility to safety critical barriers degradation data (little automation). Also 2) 94% of the respondents accounted for poor knowledge of process safety cumulative risk is and agreed it to be of low rating. The result further showed that 3) 90% of the respondents demonstrated that there are no guidance and procedures in assessing process safety cumulative risk and finally 4) 92% of the respondents reported that there is no real-time risk visualization model/ tool. Addressing these issues and challenges by the petroleum industries in the study area, will lead to successful assessment of process safety cumulative risk, thereby reducing the risk of major accidents.

A holistic approach to improving safety for battery energy storage systems

The integration of battery energy storage systems(BESS)throughout our energy chain poses concerns regarding safety,especially since batteries have high energy density and numerous BESS failure events have occurred.Wider spread adoption will only increase the prevalence of these failure events unless there is a step change in the management and design of BESS.To understand the causes of failure,the main challenges of BESS safety are summarised.BESS consequences and failure events are discussed,including specific focus on the chain of events causing thermal runaway,and a case study of a BESS explosion in Surprise Arizona is analysed.Based on the technology and past events,a paradigm shift is required to improve BESS safety.In this review,a holistic approach is proposed.This combines currently adopted approaches including battery cell testing,lumped cell mathematical modelling,and calorimetry,alongside additional measures taken to ensure BESS safety including the requirement for computational fluid dynamics and kinetic modelling,assessment of installation level testing of the full BESS system and not simply a single cell battery test,hazard and layers of protection analysis,gas chromatography,and composition testing.The holistic approach proposed in this study aims to address challenges of BESS safety and form the basis of a paradigm shift in the safety management and design of these systems.

Extraction and analysis of risk factors from Chinese chemical accident reports

Accidents in chemical production usually result in fatal injury,economic loss and negative social impact.Chemical accident reports which record past accident information,contain a large amount of expert knowledge.However,manually finding out the key factors causing accidents needs reading and analyzing of numerous accident reports,which is time-consuming and labor intensive.Herein,in this paper,a semiautomatic method based on natural language process(NLP)technology is developed to construct a knowledge graph of chemical accidents.Firstly,we build a named entity recognition(NER)model using SoftLexicon(simplify the usage of lexicon)+BERT-Transformer-CRF(conditional random field)to automatically extract the accident information and risk factors.The risk factors leading to accident in chemical accident reports are divided into five categories:human,machine,material,management,and environment.Through analysis of the extraction results of different chemical industries and different accident types,corresponding accident prevention suggestions are given.Secondly,based on the definition of classes and hierarchies of information in chemical accident reports,the seven-step method developed at Stanford University is used to construct the ontology-based chemical accident knowledge description model.Finally,the ontology knowledge description model is imported into the graph database Neo4j,and the knowledge graph is constructed to realize the structu red storage of chemical accident knowledge.In the case of information extraction from 290 Chinese chemical accident reports,SoftLexicon+BERT-Transformer-CRF shows the best extraction performance among nine experimental models.Demonstrating that the method developed in the current work can be a promising tool in obtaining the factors causing accidents,which contributes to intelligent accident analysis and auxiliary accident prevention.

Multi-phase computer modeling and laboratory study of dust capture by an inertial Vortecone scrubber

Dust generated in mining and tunneling activities is hazardous to health of persons and safety of operations. These projects employ pick-milling machines to extract minerals and rock by mechanical breakage.The machines are equipped with flooded-bed scrubbers that encase dust particles within fine water films as particles encounter a flooded wire-mesh screen. A major disadvantage is that the screen gets clogged when particles become trapped within the wire mesh, reducing airflow through the scrubber and increasing ambient dust concentrations. Thus, the system requires frequent maintenance or replacement. The application of a Vortecone scrubber as an improved alternative to conventional fibrous type scrubbers is investigated. A Vortecone forces dust-laden air and water to follow a complex, rapidly swirling motion.The momentum drives dust particles towards the periphery where they are captured by the water film.The operating characteristics of a reduced-scale physical model of a Vortecone, with its primary axis mounted in the horizontal orientation, was analyzed numerically and experimentally. Computational fluid dynamics(CFD) models depicting the spraying action and multi-phase air/water flows using the volume of fraction(VOF) approach, are presented. Experimental results, utilizing an optical particle counting technique to establish the dust-cleaning capabilities of the model, are also described.

Research on thermal risk and decomposition behavior of methyl nitrite

The thermal hazards of methyl nitrite(MN)were investigated in the present study.The determination and evaluation of MN decomposition were conducted using a C600 micro thermometer.The thermal runaway reaction characteristics of the compound under different initial pressures were obtained using a VSP2 calorimeter.The kinetic parameters of MN were obtained by regression fitting and calculation of the microthermal experimental data.The experimental and calculated results demonstrated that the potential explosion risk of MN is very high.In addition,there was a high energy barrier in the early stage of the uncontrolled decomposition of MN;however,once the decomposition reaction was initiated,the subsequent decomposition was easily conducted.Under the conditions of adiabatic simulation,the possibility that the reaction was uncontrolled increases with the initial temperature and pressure of the system,and there is a great potential safety risk.

Gas leakage recognition for CO2 geological sequestration based on the time series neural network

The leakage of stored and transported CO2 is a risk for geological sequestration technology. One of the most challenging problems is to recognize and determine CO2 leakage signal in the complex atmosphere background. In this work, a time series model was proposed to forecast the atmospheric CO2 variation and the approximation error of the model was utilized to recognize the leakage. First, the fitting neural network trained with recently past CO2 data was applied to predict the daily atmospheric CO2. Further, the recurrent nonlinear autoregressive with exogenous input(NARX) model was adopted to get more accurate prediction. Compared with fitting neural network, the approximation errors of NARX have a clearer baseline, and the abnormal leakage signal can be seized more easily even in small release cases. Hence, the fitting approximation of time series prediction model is a potential excellent method to capture atmospheric abnormal signal for CO2 storage and transportation technologies.

Landmarks in the application of electrical tomography in particle science and technology

Selected milestones in the development and use of electrical tomography in powder conveying, slurry processing and multi-phase flow are highlighted. The ability to map concentration in opaque mixtures under process-realistic conditions was a major innovation for the method and has had far reaching implications. Subsequent developments have enabled velocity information to be abstracted resulting in the ability to measure component flux and motion.