Accidental release of Liquefied Natural Gas in a processing facility: Effect of equipment congestion level on dispersion behaviour of the flammable vapour

An accidental leakage of Liquefied Natural Gas (LNG) can occur during processes of production, storage andtransportation. LNG has a complex dispersion characteristic after release into the atmosphere. This complexbehaviour demands a detailed description of the scientific phenomena involved in the dispersion of the releasedLNG. Moreover, a fugitive LNG leakage may remain undetected in complex geometry usually in semi-confined orconfined areas and is prone to fire and explosion events. To identify location of potential fire and/or explosionevents, resulting from accidental leakage and dispersion of LNG, a dispersion modelling of leakage is essential.This study proposes a methodology comprising of release scenarios, credible leak size, simulation, comparison ofcongestion level and mass of flammable vapour for modelling the dispersion of a small leakage of LNG and itsvapour in a typical layout using Computational Fluid Dynamics (CFD) approach. The methodology is applied to acase study considering a small leakage of LNG in three levels of equipment congestion. The potential fire and/orexplosion hazard of small leaks is assessed considering both time dependent concentration analysis and areabased model. Mass of flammable vapour is estimated in each case and effect of equipment congestion on sourceterms and dispersion characteristics are analysed. The result demonstrates that the small leak of LNG can createhazardous scenarios for a fire and/or explosion event. It is also revealed that higher degree of equipmentcongestion increases the retention time of vapour and intensifies the formation of pockets of isolated vapourcloud. This study would help in designing appropriate leak and dispersion detection systems, effective monitoring procedures and risk assessmen

Dynamic safety assessment of a nonlinear pumped-storage generating system in a transient process

This paper focuses on a pumped-storage generating system with a reversible Francis turbine and presents an innovative framework for safety assessment in an attempt to overcome their limitations. Thus the aim is to analyze the dynamic safety process and risk probability of the above nonlinear generating system. This study is carried out based on an existing pumped-storage power station. In this paper we show the dynamic safety evaluation process and risk probability of the nonlinear generating system using Fisher discriminant method. A comparison analysis for the safety assessment is performed between two different closing laws, namely the separate mode only to include a guide vane and the linkage mode that includes a guide vane and a ball valve. We find that the most unfavorable condition of the generating system occurs in the final stage of the load rejection transient process. It is also demonstrated that there is no risk to the generating system with the linkage mode but the risk probability of the separate mode is 6 percent. The results obtained are in good agreement with the actual operation of hydropower stations. The developed framework may not only be adopted for the applications of the pumped-storage generating system with a reversible Francis turbine but serves as the basis for the safety assessment of various engineering applications.National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesScientific research funds of Northwest A&F UniversityScience Fund for Excellent Young Scholars from Northwest A&F University and Shaanxi Nova progra

Parametric analysis of pyrolysis process on the product yields in a bubbling fluidized bed reactor

This paper presents a numerical study of operating factors on the product yields of a fast pyrolysis process in a 2-D standard lab-scale bubbling fluidized bed reactor. In a fast pyrolysis process, oxygen-free thermal decomposition of biomass occurs to produce solid biochar, condensable vapours and non-condensable gases. This process also involves complex transport phenomena and therefore the Euler-Euler approach with a multi-fluid model is applied. The eleven species taking part in the process are grouped into a solid reacting phase, condensable/non-condensable phase, and non-reacting solid phase (the heat carrier). The biomass decomposition is simplified to ten reaction mechanisms based on the thermal decomposition of lignocellulosic biomass. For coupling of multi-fluid model and reaction rates, the time-splitting method is used. The developed model is validated first using available experimental data and is then employed to conduct the parametric study. Based on the simulation results, the impact of different operating factors on the product yields are presented. The results for operating temperature (both sidewall and carrier gas temperature) show that the optimum temperature for the production of bio-oil is in the range of 500–525 °C. The higher the nitrogen velocity, the lower the residence time and less chance for the secondary crack of condensable vapours to non-condensable gases and consequently higher bio-oil yield. Similarly, when the height of the biomass injector was raised, the yields of condensable increased and non-condensable decreased due to the lower residence time of biomass. Biomass flow rate of 1.3 kg/h can produce favourable results. When larger biomass particle sizes are used, the intraparticle temperature gradient increases and leads to more accumulated unreacted biomass inside the reactor and the products’ yield decreases accordingly. The simulation indicated that the larger sand particles accompanied by higher carrier gas velocity are favourable for bio-oil production. Providing a net heat equivalent of 6.52 W to the virgin biomass prior to entering the reactor bed leads to 7.5% higher bio-oil yields whereas other products’ yields stay steady. Results from different feedstock material show that the sum of cellulose and hemicellulose content is favourable for the production of bio-oil whereas the biochar yield is directly related to the lignin content

Materials discovery of ion-selective membranes using artificial intelligence

Significant attempts have been made to improve the production of ion-selective membranes (ISMs) with higher efficiency and lower prices, while the traditional methods have drawbacks of limitations, high cost of experiments, and time-consuming computations. One of the best approaches to remove the experimental limitations is artificial intelligence (AI). This review discusses the role of AI in materials discovery and ISMs engineering. The AI can minimize the need for experimental tests by data analysis to accelerate computational methods based on models using the results of ISMs simulations. The coupling with computational chemistry makes it possible for the AI to consider atomic features in the output models since AI acts as a bridge between the experimental data and computational chemistry to develop models that can use experimental data and atomic properties. This hybrid method can be used in materials discovery of the membranes for ion extraction to investigate capabilities, challenges, and future perspectives of the AI-based materials discovery, which can pave the path for ISMs engineering

Safety assessment of hydro -generating units using experiments and grey-entropy correlation analysis

This paper focuses on the safety analysis of a nonlinear hydro-generating unit (HGU) running under different loads. For this purpose, a dynamic balance experiment implemented on an existing hydropower station in China is considered, to qualitatively investigate the stability of the system and to obtain the necessary indices for safety assessment. The experimental data are collected from four on-load units operating at different working heads including 431 m, 434 m, 437 m, and 440 m. A quantitative analysis on the safety performance of the four units was carried out by employing an integration of entropy weights method with grey correlation analysis. This assisted in obtaining the safety degree of each unit, providing the risk prompt to the operation of nonlinear hydro-generating units. The results confirm that unit 4 has the highest level of safety while unit 3 operates with the lowest safety condition. This provides the optimal operational schedule of HGUs to cope with the fluctuations of electricity demand in the studied station. The proposed methodology in this paper is not only applicable to the HGUs in the studied station but could also be adopted to assess the safety degree of any hydropower facility

Prognostic health management of repairable ship systems through different autonomy degree; From current condition to fully autonomous ship

Maritime characteristics make the progress of automatic operations in ships slow, especially compared to other means of transportation. This caused a great progressive deal of attention for Autonomy Degree (AD) of ships by research centers where the aims are to create a well-structured roadmap through the phased functional maturation approach to autonomous operation. Application of Maritime Autonomous Surface Ship (MASS) requires industries and authorities to think about the trustworthiness of autonomous operation regardless of crew availability on board the ship. Accordingly, this paper aims to prognose the health state of the conventional ships, assuming that it gets through higher ADs. To this end, a comprehensive and structured Hierarchal Bayesian Inference (HBI)-based reliability framework using a machine learning application is proposed. A machinery plant operated in a merchant ship is selected as a case study to indicate the advantages of the developed methodology. Correspondingly, the given main engine in this study can operate for 3, 17, and 47 weeks without human intervention if the ship approaches the autonomy degree of four, three, and two, respectively. Given the deterioration ratio defined in this study, the acceptable transitions from different ADs are specified. The aggregated framework of this study can aid the researchers in gaining online knowledge on safe operational time and Remaining Useful Lifetime (RUL) of the conventional ship while the system is being left unattended with different degrees of autonomy.</p

Transient safety assessment and risk mitigation of a hydroelectric generation system

Transient safety assessment of hydroelectric generation systems is a major challenge for engineers specialized in hydropower stations around the world. This includes two key scientific issues: the dynamic risk quantification in a multi-factors coupling process, and the identification of elements with highest contribution to system stability. This paper presents a novel and efficient dynamic safety assessment methodology for hydroelectric generation systems (HGSs). Based on a comprehensive fuzzy-entropy evaluation method, the dynamic safety level of the system is estimated by means of probability values, and the influence rate of assessment indices on the HGS risk profile is also obtained. Moreover, a number of risk mitigation and maintenance amendment strategies are discussed to reduce the losses in operation and maintenance (O&M) costs at hydropower stations. The methodology is implemented and validated using an existing hydropower station experiencing a start-up transient process, results of which are shown to be beneficial to operators and risk managers. It is recommended that the presented methodology is applicable not only to the HGS’s start-up process but is also promisingly useful for largely fluctuating transient processes of other engineering facilities

Modelling an integrated impact of fire, explosion and combustion products during transitional events caused by an accidental release of LNG

In a complex processing facility, there is likelihood of occurrence of cascading scenarios, i.e. hydrocarbon release, fire, explosion and dispersion of combustion products. The consequence of such scenarios, when combined, can be more severe than their individual impact. Hence, actual impact can be only representedby integration of above mentioned events. A novel methodology is proposed to model an evolving accident scenario during an incidental release of LNG in a complex processing facility. The methodology is applied to a case study considering transitional scenarios namely spill, pool formation and evaporation of LNG, dispersion of natural gas, and the consequent fire, explosion and dispersion of combustion products using Computational Fluid Dynamics (CFD). Probit functions are employed to analyze individual impacts and a ranking method is used to combine various impacts to identify risk during the transitional events.The results confirmed that in a large and complex facility, an LNG fire can transit to a vapor cloud explosion ifthe necessary conditions are met, i.e.the flammable range, ignition source with enough energy and congestion/confinement level. Therefore, the integrated consequences are more severe than those associated with the individual ones, and need to be properly assessed. This study would provide an insight for an effective analysis of potential consequences of an LNG spill in any LNG processing facility and it can be useful for the safety measured design of process facilities

Review and analysis of fire and explosion accidents in maritime transportation

The globally expanding shipping industry has several hazards such as collision, capsizing, foundering, grounding, stranding, fire, and explosion. Accidents are often caused by more than one contributing factor through complex interaction. It is crucial to identify root causes and their interactions to prevent and understand such accidents. This study presents a detailed review and analysis of fire and explosion accidents that occurred in the maritimetransportation industry during 1990–2015. The underlying causes of fire and explosion accidents are identified and analysed. This study also reviewed potential preventative measures to prevent such accidents. Additionally, this study compares properties of alternative fuels and analyses their effectiveness in mitigating fire and explosionhazards. It is observed that Cryogenic Natural Gas (CrNG), Liquefied Natural Gas (LNG) and methanol have properties more suitable than traditional fuels in mitigating fire risk and appropriate management of their hazards could make them a safer option to traditional fuels. However, for commercial use at this stage, there exist several uncertainties due to inadequate studies, and technological immaturity. This study provides an insight into fire and explosion accident causation and prevention, including the prospect of using alternative fuels for mitigating fire and explosion risks in maritime transportation

Risk-based Evaluation of Landfill Gas Flare Efficiency Using Computational Fluid Dynamics (CFD)

Methane is produced in landfills through the anaerobic digestion of organic material. Methane is a greenhouse gas with 24.5 times the global warming potential when compared to carbon dioxide (CO2). Landfill gas also contains hydrogen sulfide which may account for up to 1 percent by volume of landfill gas emissions and impacts human health even in low concentrations. As a result, landfill gas is typically collected and either flared (to convert methane and hydrogen sulphide to carbon dioxide and sulfur dioxide respectively) or used for power on site. Flaring is typically an incomplete combustion process, producing many other pollutants that may result in environmental and human health impacts. Quantifying these emissions would result in better flare designs and plume dispersion estimates. However, the efficiency of flare combustion is site and flare design specific, making predictions difficult. In this work a Computational Fluid Dynamics (CFDs) model (using Fluent as a tool) was developed to simulate the flow and combustion mechanisms of the flare. The model can be used as a tool in flare design and as a method to ensure an operating flare is working properly. It can also be used to predict dispersed gases concentrations, allowing operators to optimize environmental monitoring stations and flare operations. The model is a function of the input data and therefore critical parameters such as exit gas velocities, stack height and diameter among other parameters must be specified. The model was validated using lab data from published work. A risk assessment model is proposed as part of this work which integrates the CFD model with a risk model