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CONTEXT: The thermal hazard of reactions caused by the structural instability of aromatic nitro compounds is a major concern in the field of chemical process safety and one of the main causes of major thermal runaway (TR) accidents such as fire and explosion. Among them, the self-accelerating decomposition temperature (SADT), as an important parameter, has been widely used to evaluate the thermal hazards of aromatic nitro compounds in actual storage and transportation processes. However, the control temperature (CT) and emergency temperature (ET), which depend on and are associated with SADT, have been rarely reported in previous studies. In this work, multiple linear regression (MLR) and artificial neural network (ANN) models for CT and ET were constructed based on the molecular descriptors corresponding to the stable structures of 27 monadic/binary aromatic nitro compounds, combined with advanced adiabatic accelerating calorimetric experiments and quantitative structure-property relationship (QSPR). The optimal subset of descriptors with significant contributions was screened out while the fit, predictive ability, and robustness of the four types of models were evaluated with internal and external validation parameters, and finally, two types of parameters (R2 and ARE) were selected as the main indicators for a comprehensive comparative analysis. The results show that the four models fit the experimental data well. During this period, the accuracy of ANN models is slightly higher than that of MLR models, and the QSPR models under the two modes (linear and nonlinear) are more inclined toward ET in prediction ability. Based on simplifying the calculation process and realizing rapid parameter prediction, this study is expected to provide technical support for engineering applications such as safe operation, safe storage and transportation of substances, and emergency response in the chemical industry. METHODS: In this work, we tested and calculated the thermal safety parameters of 27 monadic/binary aromatic nitro compounds by ARC and AKTS and further used the PubChem database and Gaussian 09 software program to obtain and optimize their corresponding molecular structures. The geometric optimization process adopts DFT on the B3LYP level and the 6-31 + G(d, p) basis set, while the same functional and basis set was used for vibration analysis. The OpenBabel toolbox and ChemDES platform were used for transformation coding and descriptor calculation. Finally, IBM SPSS Statistics 24 and MATLAB software were used to construct MLR models and ANN models, respectively.
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Due to the abrupt nature of the chemical process, a large number of alarms are often generated at the same time. As a result of the flood of alarms, it largely hinders the operator from making accurate judgments and correct actions for the root cause of the alarm. The existing diagnosis methods for the root cause of alarms are relatively single, and their ability to accurately find out complex accident chains and assist decision making is weak. This paper introduces a method that integrates the knowledge-driven method and the data-driven method to establish an alarm causal network model and then traces the source to realize the alarm root cause diagnosis, and develops the related system modules. The knowledge-driven method uses the hidden causality in the optimized hazard and operability analysis (HAZOP) report, while the data-driven method combines the autoregressive integrated moving average model (ARIMA) and Granger causality test, and the traceability mechanism uses the time-based retrospective reasoning method. In the case study, the practical application of the method is compared with the experimental application in a real petrochemical plant. The results show that this method helps to improve the accuracy of correct diagnosis of the root cause of the alarm and can assist the operators in decision making. Using this method, the root cause diagnosis of alarm can be realized quickly and scientifically, and the probability of misjudgment by operators can be reduced, which has a certain degree of scientificity.
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In view of the accidents such as rock mass breakage, roof fall and coal slide in coal mines, polyurethane/mesoscopic fly ash (PU/MFA) reinforcement materials were produced from polymethylene polyphenylene isocyanate (PAPI), the polyether polyol, flame retardant, and MFA using stannous octanate as a catalyst. 3-Glycidoxypropyltrimethoxysilane (GPTMS) was grafted on MFA surface, aiming to improve the mechanical properties of PU/MFA composites. The analyses of infrared spectroscopy and compression resistance reveal that the GPTMS can be successfully attached to the surface of MFA, and the optimum modification dosage of GPTMS to MFA is 2.5 wt % (weight percent). On this basis, the effect of GPTMS on the mechanical properties of PU/MFA reinforcement materials during the curing process was systematically investigated through a compression test, a fracture toughness test, a three-point bending test, a bond property test, and a dynamic mechanics analysis. The results show that the compression property, fracture toughness, maximum flexural strength, and bond strength of PU/MFA composites increase by 21.6%, 10.1%, 8.8%, and 19.3%, respectively, compared with the values before the modification. Furthermore, the analyses of scanning electron microscope and dynamic mechanics suggest that the coupling agent GPTMS can successfully improve the mechanical properties of PU/MFA composites because it eliminates the stress concentration and exerts a positive effect on the crosslink density and hardness of PU/MFA composites.