Learning From Major Accidents: A Meta-Learning Perspective

Learning from the past is essential to improve safety and reliability in the chemical industry. In the context of Industry 4.0 and Industry 5.0, where Artificial Intelligence and IoT are expanding throughout every industrial sector, it is essential to determine if an artificial learner may exploit historical accident data to support a more efficient and sustainable learning framework. One important limitation of Machine Learning algorithms is their difficulty in generalizing over multiple tasks. In this context, the present study aims to investigate the issue of meta-learning and transfer learning, evaluating whether the knowledge extracted from a generic accident database could be used to predict the consequence of new, technology-specific accidents. To this end, a classi-fication algorithm is trained on a large and generic accident database to learn the relationship between accident features and consequence severity from a diverse pool of examples. Later, the acquired knowledge is transferred to another domain to predict the number of fatalities and injuries in new accidents. The methodology is eval-uated on a test case, where two classification algorithms are trained on a generic accident database (i.e., the Major Hazard Incident Data Service) and evaluated on a technology-specific, lower-quality database. The results suggest that automated algorithms can learn from historical data and transfer knowledge to predict the severity of different types of accidents. The findings indicate that the knowledge gained from previous tasks might be used to address new tasks. Therefore, the proposed approach reduces the need for new data and the cost of the analyses

A data-driven approach to improve control room operators' response

Digitalization 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

Factors affecting the spread of "Bois Noir" disease in north Italy vineyards

To define control strategies for “Bois Noir” disease (BN) it is necessary to know factors favouring its spreading by the vector Hyalesthes obsoletus Signoret. During 2003-2006 a research was carried out in 18 vineyards of a grape-growing area of North Italy to assess the influence of insecticides, applied on grapevine canopies, and environment surrounding the vineyards on disease spreading. The vector population density was higher outside than in the centre of the vineyards. Insecticides applied to grapevine canopies did not significantly influence the vector population level in the centre of the vineyards. The majority of vineyards showed randomized distribution of symptomatic grapevines. Seven vineyards had an aggregate distribution due to an edge effect from a border side with nettle. The incidence of border sides not contiguous to other grapevine rows on vineyard surface was positively related to higher levels of BN. The incidence of border sides with nettle on vineyard surface was positively correlated to disease incidence in the vineyards with aggregate distribution of symptomatic grapevines. All the data support the importance of surrounding vegetation as source of inoculum of BN phytoplasma. Molecular analyses on ribosomal and tuf genes show that 16 out of the 18 vineyards were affected only by BN: in 13 only tuf-type I was identified, in 2 only tuf-type II, in 1 both tuf-types, and in 2 it was not possible to identify the tuf-type of phytoplasmas detected. In the weeds tested only tuftype II phytoplasmas were identified while H. obsoletus was carrying both phytoplasma tuf-types.

On the Mechanical Energy Involved in the Catastrophic Rupture of Liquid Hydrogen Tanks

Hydrogen can play a central role in the energy transition thanks to its unique properties. However, its low density is one of the main drawbacks. The liquefaction process can drastically increase its density up to virtually 71 kg m-3 at atmospheric pressure and -253°C (NIST, 2019). The safety knowledge gap on physical explosions is still broad in the case of liquid hydrogen (LH2). For instance, it is unclear what are the consequences yields as well as the probabilities of a catastrophic rupture of an LH2 tank. A boiling liquid expanding vapour explosion (BLEVE) might arise after this top event. In this case, the expansion of the compressed gaseous phase is followed by the flashing of a fraction of the liquid. Moreover, combustion may occur for hydrogen since it is highly flammable. This complex phenomenon was not widely explored for LH2 yet. This study focused on the physical explosion by also considering the combustion process. Many integral models were adopted to estimate the mechanical energy developed by the explosion. The tank pressure prior to the rupture was considered below the critical one (1.298 MPa (NIST, 2019)). It was assumed that both liquid and gaseous phases are present inside the tank. The influences of the filling degree of the tank (liquid level) and the temperatures of the liquid and gaseous phases on the explosion energy were analysed. The results were compared with the ones of a previous study where similar models were employed to estimate the mechanical energy of an LH2 tank with different initial conditions (Ustolin et al., 2020a). In particular, the effect of the combustion process on the explosion energy and shock wave overpressure was not accounted for. The aim of this study is to conduct a comparison between different models and assess which are the most and the least conservative. The outcomes of this work provide critical suggestions on the consequence analysis of cryogenic liquefied gas vessels explosions

Molecular characterization of ‘Candidatus Phytoplasma mali’ strains in outbreaks of apple proliferation in north eastern Italy, Hungary, and Serbia

During 2005-2008 apple plants of different varieties showing proliferation symptoms were observed in diverse areas of north eastern Italy, Hungary and Serbia. PCR/RFLP analyses showed that all the samples were infected with ‘Candidatus Phytoplasma mali’. In the 16S plus spacer region two phytoplasma profiles (P-I and P-II) were distinguished. P-I profile was detected in reference strains AP, AT1, AT2, in samples from Serbia, and in the majority of samples from Trentino; the P-II profile was prevalent in samples from Veneto; both profiles were identified in samples from Hungary, in some cases together in single samples. The analyses of rpl22-s3 genes allow the identification, in all the samples showing a P-I profile, the presence of phytoplasmas belonging to rpX-A subgroup, while in the samples showing a P-II profile it was possible to distinguish the other three reported rpX subgroups. In the majority of samples from the Veneto region phytoplasmas belonging to rpX-D subgroup were identified, while rpX-B and rpX-C subgroups were identified only in a few samples from Trentino and Veneto regions, respectively. Further RFLP analyses on AP13/AP10 amplicons differentiate among strains belonging to the rpX-A subgroup: the samples from Serbia show AP profiles, while those from Trentino show AT-2 profiles. In the samples from Hungary the presence of AT1, AT2, and AP profiles was identified.Keywords: Apple, ‘Candidatus Phytoplasma mali’, phytoplasma strains, PCR/RFLP analyses, epidemiolog

Risk Assessment at the Cosmetic Product Manufacturer by Expert Judgment Method

A case study was performed in a cosmetic product manufacturer. We have identified the main risk factors of occupational accidents and their causes. Risk of accidents is assessed by the expert judgment method. Event tree for the most probable accident is built and recommendations on improvement of occupational health and safety protection system at the cosmetic product manufacturer are developed. The results of this paper can be used to develop actions to improve the occupational safety and health system in the chemical industry

Human reliability analysis: exploring the intellectual structure of a research field

Humans play a crucial role in modern socio-technical systems. Rooted in reliability engineering, the discipline of Human Reliability Analysis (HRA) has been broadly applied in a variety of domains in order to understand, manage and prevent the potential for human errors. This paper investigates the existing literature pertaining to HRA and aims to provide clarity in the research field by synthesizing the literature in a systematic way through systematic bibliometric analyses. The multi-method approach followed in this research combines factor analysis, multi-dimensional scaling, and bibliometric mapping to identify main HRA research areas. This document reviews over 1200 contributions, with the ultimate goal of identifying current research streams and outlining the potential for future research via a large-scale analysis of contributions indexed in Scopus database

Low-energy absorption towards the ultra-compact binary 4U1850-087 located in the globular cluster NGC6712

We report the results of two XMM-Newton observations of the ultra-compact low-mass X-ray binary 4U1850-087 located in the galactic globular cluster NGC6712. A broad emission feature at 0.7keV was detected in an earlier ASCA observation and explained as the result of an unusual Ne/O abundance ratio in the absorbing material local to the source. We find no evidence for this feature and derive Ne/O ratios in the range 0.14-0.21, consistent with that of the interstellar medium. During the second observation, when the source was 10% more luminous, there is some evidence for a slightly higher Ne/O ratio and additional absorption. Changes in the Ne/O abundance ratio have been detected from another ultra-compact binary, 4U1543-624. We propose that these changes result from an X-ray induced wind which is evaporated from an O and Ne rich degenerate donor. As the source X-ray intensity increases so does the amount of evaporation and hence the column densities and abundance ratio of Ne and O.Comment: 9 pages, 6 figures, accepted for publication in Astronomy and Astrophysic

Do dogs and cats passively carry sars-cov-2 on hair and pads?

The epidemiological role of domestic animals in the spread and transmission of SARS-CoV-2 to humans has been investigated in recent reports, but some aspects need to be further clarified. To date, only in rare cases have dogs and cats living with COVID-19 patients been found to harbour SARS-CoV-2, with no evidence of pet-to-human transmission. The aim of the present study was to verify whether dogs and cats act as passive mechanical carriers of SARS-CoV-2 when they live in close contact with COVID-19 patients. Cutaneous and interdigital swabs collected from 48 dogs and 15 cats owned by COVID-19 patients were tested for SARS-CoV-2 by qRT-PCR. The time elapsed between owner swab positivity and sample collection from pets ranged from 1 to 72 days, with a median time of 23 days for dogs and 39 days for cats. All samples tested negative, suggesting that pets do not passively carry SARS-CoV-2 on their hair and pads, and thus they likely do not play an important role in the virus transmission to humans. This data may contribute to confirming that the direct contact with the hair and pads of pets does not represent a route for the transmission of SARS-CoV-2