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Carbon/epoxy laminates and foam‐core sandwich composites are frequently used as engineering materials within the aerospace sector. However, epoxy resin matrix and foam‐core materials are highly flammable. In this work, the thermal behaviors of carbon/epoxy laminates and foam‐core sandwich composites were investigated using thermogravimetric analysis at different heating rates in atmospheric air. The morphological images of specimens and the residue after pyrolysis at different characteristic temperatures were further investigated using scanning electron microscopy. Additionally, thermogravimetric Fourier transform infrared spectroscopy was used to analyze the vapors and gases evolved during the thermal decomposition. It was determined that the pyrolysis reactions of carbon/epoxy laminates and foam‐core sandwich composites consist of three decomposition steps. Furthermore, an increase in the heating rate of each material results in higher initial and final temperatures for each thermal decomposition step. Kinetic parameters for the decomposition for carbon/epoxy composites were estimated using Kissinger, Flynn‐Wall‐Ozawa, and Starink methods, and the corresponding thermodynamic parameters were obtained. Through analysis of the reaction kinetics, it was determined that the pyrolysis reactions of carbon/epoxy composites are not easily activated, requiring significant activation energy, but the series of reactions take place easily once this energy barrier is overcome.

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Uncontrolled release of flammable gases and liquids can lead to the formation of flammable vapor clouds. When their concentrations are above the lower flammable limit (LFL), or 1/2 LFL for conservative evaluation, fires and explosions can happen in the presence of an ignition source. The objective of this work is to develop highly efficient consequence models to precisely predict the downwind maximum distance, minimum distance, and maximum vapor cloud width within the flammable limit. In this work, the novel methodology named quantitative property–consequence relationship (QPCR) is proposed and constructed to precisely predict flammable dispersion consequences in a machine learning and data-driven manner. A flammable dispersion database consisting of 450 leak scenarios of 41 flammable chemicals was constructed using PHAST simulations. A state-of-art machine learning regression method, the extreme gradient boosting algorithm, was implemented to develop models. The coefficient of determination (R 2) and root-mean-square error (RMSE) were calculated for statistical assessment, and the developed QPCR models achieved satisfactory predictive capabilities. All developed models had high precision, with the overall RMSE of three models being 0.0811, 0.0741, and 0.0964, respectively. The developed QPCR models can be used to obtain instant flammable dispersion estimations for other flammable chemicals and mixtures at much lower computational costs.

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A number of incidents within the process industries have been attributed to climate extremes. The late Dr. Mannan proposed the concept of a "safety triad," which represents the three layers of an effective safety system, with deficiencies in any of the layers leading to possible incidents. Using this safety triad, past incidents in the process industries resulting from climate extremes can be investigated. From these investigations, appropriate systems and strategies can be implemented to address deficiencies in any of the layers to reduce the likelihood of similar incidents occurring in the future. This article presents a case study involving the DuPont La Porte facility toxic chemical release to demonstrate the use of the safety triad to identify deficiencies of the layers that led to the incident and propose strategies to address them. K E Y W O R D S case histories, emergency response, incident investigations, safety management

For industrial reactions, cooling requirements must be carefully determined to prevent thermal runaway, while product yields are also important for economic analysis. In this work, the suitable operating regions for synthesis of cyclohexanone from cyclohexanol through minimizing the heat release and maximizing product yields are determined using response surface methodology. These are based on the parameters of operating temperature, peroxide to reactant ratio, and number of peroxide injections. The reaction temperature was found to play the greatest role in both the quantity of heat released and cyclohexanone yield for each set of experiments, with the greatest heat release of 9.23 kJ/kg and highest cyclohexanone yield of 0.81 occurring at the highest temperature investigated (60 °C). As a result, response surface methodology has been demonstrated as an effective method for identifying parameter combination effects on heat release and reaction yields for exothermic reactions.