1,721,060 research outputs found
DIFFERENTIAL-PULSE ANODIC-STRIPPING VOLTAMMETRY FOR ULTRATRACE DETERMINATION OF CADMIUM AND LEAD IN ANTARCTIC SNOW
No full textCFD study of the fire response of vessels containing liquefied gases
Full text linkAccidental fires represent a serious threat for vessels devoted to the transportation and storage of liquefied gases, such as propane and butane. The present work focuses the impact of full engulfing hydrocarbon pool fires on propane and butane storage tanks. The analysis was carried out using a computational fluid dynamics (CFD) model, previously validated against experimental data. Results are presented in terms of pressurization rates, and temperature distributions showing the difference in the response to fire between propane and butane tanks. The effect of the filling degree is also pointed out. The outcomes of this work provide useful results to evaluate the possible failure conditions, thus supporting the emergency response to accidental fires in the proximity of storage tanks or transport units
A data-driven approach to improve control room operators' response
Full text linkDigitalization 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
Heavy metals in Ligurian Sea sediments: distribution of Cr, Cu, Ni and Mn in superficial sediments.
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Design and Operation of Liquid Hydrogen Storage Tanks
No full textLiquid hydrogen (LH2) is a versatile and efficient energy carrier with numerous applications in space exploration, hydrogen fuel cell vehicles, industrial processes, and the maritime sector. However, its extremely low boiling point and low density present unique challenges in handling, storage, and transportation, particularly in the prevention of loss of containment scenarios. At present, there is still limited knowledge available on the thermodynamics of liquid hydrogen contained in cryogenic storage tanks. This scientific paper delves into an examination of insulation techniques and the operation of liquid hydrogen tanks. Also, self-pressurization is explained and set into context. Furthermore, modelling of specific parameters such as temperature distribution, pressure increase and liquid level play an important role in understanding the thermodynamics inside of LH2 tanks and enable to draw conclusions for the efficient operation when avoiding the loss of hydrogen by releasing boil off gas. The ramifications of this study hold critical importance for industries reliant on hydrogen. The insights gained will facilitate the development of prediction models to enhance operational directives, and the development of effective storage systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Numerical simulation of LNG tanks exposed to fire
No full textThe increasing use of Liquefied Natural Gas (LNG) as a fuel for ships and vehicles poses relevant safety concerns, extended to the entire LNG supply chain and distribution network. Understanding the phenomena associated with the behavior of LNG tanks exposed to severe heat sources is thus a fundamental issue to identify potential safety-critical scenarios. The experimental data and modeling approaches currently available, mainly referring to small-scale pilot vessels, feature relevant limitations when extended to large-scale applications. In the present study, a two-dimensional non-equilibrium computational fluid dynamics model (2D CFD) of LNG tanks exposed to fire engulfing scenarios was developed. The 2D CFD model was validated against experimental bonfire data and was extended to simulate the behavior of large-scale vessels used in specific industrial applications, as the road transportation of LNG and the fuel supply of ships. A set of Key Performance Indicators (KPIs) was defined to support the safety assessment of LNG tanks, and to identify the potential transition to safety critical regions during fire exposure. The CFD results obtained allowed investigating the influence of operative parameters and geometry on the pressure build-up in the tanks, as well as on the transient evolution of complicating phenomena, such as the thermal stratification. The KPIs defined provide a useful support for the design of safety systems and for decision making in emergency response
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