Coal chemical enterprises have many risk factors, and the causes of accidents are complex. The traditional risk assessment methods rely on expert experience and previous literature to determine the causes of accidents, which has the problems such as lack of objectivity and low interpretation ability. Analyzing the accident report helps to identify typical accident risk factors and determines the accident evolution rule. However, experts usually judge this work manually, which is subjective and time-consuming. This paper developed an improved approach to identify safety risk factors from a volume of coal chemical accident reports using text mining (TM) technology. Firstly, the accident report was preprocessed, and the Term Frequency Inverse Document Frequency (TF-IDF) was used for feature extraction. Then, the K-means algorithm and apriori algorithm were developed to cluster and for the association rule analysis of the vectorized documents in the TF-IDF matrix, respectively to quickly identify the hidden risk factors and the relationship between risk factors in the accident report and to propose targeted safety management measures. Using the sample data of 505 accidents in a large coal chemical enterprise in Western China in the past seven years, the enterprise accident reports were analyzed by text clustering analysis and association rule analysis methods. Through the analysis, six accident clusters and 13 association rules were obtained, and the main risk factors of each accident cluster were further mined, and the corresponding management suggestions were put forward for the enterprise. This method provides a new idea for coal chemical enterprises to make safety management decisions and helps to prevent safety accidents.

Wooden structures are prone to fire hazards, and studying their combustion properties is vital for their protection. Samples of fresh pine wood (FP) and pine subjected to artificially accelerated aging (treated at 240 circulation) were collected and prepared for analysis. The heat-release rate, smoke production, and yields of carbon monoxide (CO) and carbon dioxide (CO₂) from both types of pine were assessed using conical calorimetry to determine their combustion characteristics. A historic building in Xi’an was modeled using PyroSim to serve as a representative case. The experimentally obtained combustion characteristics of the two pine wood types were inputted into the model, and the Fire Dynamics Simulator (FDS) software was utilized to simulate the development of fire in both newly constructed and aging historic buildings. The results suggest that aging impairs the wood’s combustion properties and increases the susceptibility of pine to ignition. Consequently, this accelerates the spread of fire in wooden structures, leading to a rapid increase in temperature and swift smoke production during fires, thus amplifying the fire risk to historic wooden buildings.

To explore the effects of end groups on the confined crystallization of an alkyl chain, 3-pentadecylphenol (PDP) was infiltrated into the anodic aluminum oxide template (AAO) to investigate the melting and crystallization behaviors of PDP in a nanoconfined environment. Wide-angle X-ray diffraction (WAXD) found that the solid–solid phase transition of PDP occurred under confined conditions, and the absence of the (00L) reflections indicated that the stacking of the end groups of the alkyl chain layered structure was seriously disturbed. Thermal analysis (TG) showed that the thermal stability of the confined samples decreased due to the confinement effect, and the introduction of end groups made the confinement effect more obvious. Differential scanning calorimeter (DSC) results well reflected the space–time equivalence in the PDP crystallization processes, i.e., the solid–solid phase transition can be achieved by reducing the cooling rate or confining PDP in the nanometer space. Compared with C₁₅, the introduction of the end groups with a phenol ring led to the disappearance of the solid–solid phase transition of an alkyl chain at high cooling rates. In the confined environment, the introduction of the end groups with a phenol ring caused the melting double peaks of the alkyl chain to become a single melting peak, and it also caused the disappearance of the surface freezing monolayer for alkyl chains. Through the analysis of crystallinity, it was found that AAO-PDP was more sensitive to AAO pore size changes than AAO-C₁₅, the X_c of AAO-PDP had a good linear relationship with the pore size d, but the X_c of the AAO-C₁₅ had a nonlinear relationship with the pore size d. Attenuated total reflection (ATR)-IR proved that in the confined environment, the order of the alkyl chain decreased and the degree of chain distortion increased.

Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When the early warning time is advanced, many false and missing alarms will occur in the system, which will cause certain problems for on-site personnel; how to ensure the accuracy of early warning as much as possible while the early warning time is a technical problem requiring an urgent solution. In the present work, a bidirectional long short-term memory network (BiLSTM) model was established according to the temporal variation characteristics of process parameters, and the Whale optimization algorithm (WOA) was used to optimize the model's hyperparameters automatically. The predicted value was further constructed as a Modified Inverted Normal Loss Function (MINLF), and the probability of abnormal fluctuations of process parameters was calculated using the residual time theory. Finally, the WOA-BiLSTM-MINLF process parameter prediction model with inherent risk and trend risk was established, and the fluctuation process of the process parameters was transformed into dynamic risk values. The results show that the prediction model alarms 16 min ahead of distributed control systems (DCS), which can reserve enough time for operators to take safety protection measures in advance and prevent accidents.