Lightning strikes, a prominent meteorological event, pose a significant risk of triggering technological disruptions within the process industry. To better understand this phenomenon, an analysis focused on past lightning-triggered events was carried out, examining open-source industrial-accident databases to compile a new NaTech-driven dataset of 689 records. First, an overall quantitative analysis revealed that over 80 % of these events involved incidents or loss of containment. Notably, 83.3 % of them occurred during the spring and summer, indicating a seasonal pattern. Based on the frequency of functional attributes, the chemical and petrochemical macro-sector was the most vulnerable, followed by storage and warehousing. About 40 % of all classifiable events happened on storage equipment, while 21 % happened on electric and electronic devices. Given the lack of valuable information for the principal source of data (NRC), the technological scenarios triggered were characterized using a refined subset of 127 observations, obtained considering the "other sources" of data. Fire scenarios predominated at 56 %; coincidentally, roughly 70 % of all scenarios involved hazardous substances classified as physical hazards. Estimated losses for the available information underscored the adverse consequences of lightning-triggered NaTech events, highlighting their major impact on both safety and the environment. An analysis of the event tree showed the logical path from the lightning strike to the final ignition scenarios (considering a subset of 107 records). This path accounted for 36 % of the classifiable records that directly affected the structure, while more than 50 % of them did not. Bayesian network structures made it possible to get conditional probabilities from the event tree and improved the model by adding attributes for vulnerable equipment and macro-sectors. In order to deal with the uncertain data, algorithms were used to generalize the models that were obtained from smaller subsets of data with more accurate information to the whole dataset. It provides an important additional view of unclassifiable data that otherwise remained in the dark. This novel insight contributes to increase the vulnerability awareness of industrial assets against lightning strikes.

The bioremediation of soils polluted with hydrocarbons demonstrated to be a simple and cheap technique, even if it needs a long time. The current paper shows the application of statistical analysis, based on two factors involved in the biological process at several levels. We focus on the Design of Experiments (DOE) to determine the number and kind of experimental runs, whereas the use of the categorical factors has not been widely exploited up to now. This method is especially useful to analyze factors with levels constituted by categories and define the interaction effects. Particularly, we focused on the statistical analysis of (1) experimental runs carried out at laboratory scale (test M, in microcosm), on soil polluted with diesel oil, and (2) bench scale runs (test B, in biopile), on refinery oil sludge mixed with industrial or agricultural biodegradable wastes. Finally, the main purpose was to identify the factor’s significance in both the tests and their potential interactions, by applying the analysis of variance (ANOVA). The results demonstrate the robustness of the statistical method and its quality, especially when at least one of the factors cannot be defined with a numerical value.

Industrial facilities, once considered isolated entities, are now approached as complex socio-technical systems that involve intricate networks operating within the confines of the surrounding environment. The increasing frequency of extreme natural events due to climate change has highlighted the vulnerability of industrial plants to NaTech (natural hazards triggering technological scenarios). Current methodologies for NaTech risk assessment focus predominantly on immediate consequences, overlooking the long-term complexities that both industrial systems and their surrounding territories may face. This research proposes a resilience-based framework for NaTech risk management, considering three main stages —awareness, preparedness, recovery—and a loop for continuous learning to address the evolving NaTech challenges. Awareness emphasizes proactive vulnerability characterization under the function-location perspective. It addresses the interactions between industrial plants and multi-hazard contexts by implementing innovative methodological procedures based on qualitative, quantitative, and spatial techniques. Preparedness is supported by an innovative multi-risk tool considering the dynamic vulnerability of equipment categories to individual and overlapped interacting hazards, able to deploy the vulnerability into hierarchical safety layers to design robust and context-specific safer systems. The recovery stage is described through a retrospective case of hydrocarbon pollution caused by rainfall, integrating countermeasures and sustainable technological solutions. Overall, this research highlights the need for multidimensional approaches to tackle the evolving challenges posed by NaTech events. The outcomes present a comprehensive resilience-based framework, and operational procedures guiding the practical implementation of resilience principles and advancing the understanding of complex industrial systems while supporting their long-term sustainability facing natural factors.

Debido al aumento en la cantidad de petróleo utilizado, grandes cantidades de hidrocarburos se liberan anualmente al medio ambiente, constituyendo una de las principales causas de contaminación del suelo a nivel mundial. El Centro de Estudios Ambientales de Cienfuegos, Cuba, ejecutó un proyecto experimental para desarrollar una técnica innovadora que aumentase la resiliencia territorial, ante dicho problema ambiental. El objetivo fue implementar el Despliegue de la Función Calidad (QFD), para determinar los requisitos de diseño de las unidades experimentales a evaluar a escala de banco, durante la degradación de residuos petrolizados mediante diferentes tratamientos de la ecotecnología de biopilas. Se implementó un enfoque de 4 etapas que permitió organizar el equipo, seleccionar la tecnología, adoptar un procedimiento para la implementación del QFD para traducir las necesidades en requisitos de ingeniería y diseminar los resultados. Las concentraciones de microorganismos, nutrientes y de hidrocarburos en la biopila, fueron las características que acumularon más del 50% de los pesos relativos de la matriz de primer nivel. Concentración inicial de hidrocarburos y los porcentajes de humedad y de texturizantes en la mezcla, obtuvieron los mayores pesos relativos en el segundo nivel. El porcentaje de texturizantes en la mezcla de la biopila se identificó como una oportunidad de reducir los costos y de mejoría en la eficacia de la biorremediación. Los resultados y lecciones aprendidas propiciaron delinear los protocolos experimentales para el diseño ingeniero y permitieron construir un prototipo a escala de banco de las biopilas experimentales para la evaluación de diversos tratamientos.