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Objective: This study aimed to investigate different discriminant analysis models for pseudo-mixtures of walnut oil and rapeseed oil. Methods: Gas chromatography technology was used to analyze the fatty acid content in the adulterated mixture of walnut oil and rapeseed oil. Chemical stoichiometric methods were used to model the gas chromatography data, and discriminant analysis was performed on different proportions of walnut oil and rapeseed oil mixtures. Results: Pure walnut oil and adulterated walnut oil were distinguihed by using Principal Component Analysis (PCA) identified, and percentage of adulteration in the sample was calculated. 83.33% of the samples were successfully categorized using the Bayes discriminant analysis. Partial Least Squares Discriminant Analysis (PLS-DA) achieved 87.50% discrimination accuracy. Based on the BP neural network model for discriminant analysis, the accuracy of the training set was 84.21% and the accuracy of the test set was 80.00%. For both the training and testing sets, the genetic algorithm-based discriminant analysis using an optimized support vector machine (SVM-ga) achieved 100% accuracy. Conclusion: Multiple analytical models can identify the adulteration ratio of walnut oil and rapeseed oil to varying degrees, among which the SVM-ga model had the best prediction accuracy.

Launch safety of explosive charges has become an urgent problem to be solved by all countries in the world as launch situation of ammunition becomes consistently worse. However, the existing numerical models have different defects. This paper formulates an efficient computational model of the combustion of an explosive charge affected by a bottom gap in the launch environment in the context of the material point method. The current temperature is computed accurately from the heat balance equation, and different physical states of the explosive charges are considered through various equations of state. Microcracks in the explosive charges are described with respect to the viscoelastic statistical crack mechanics (Visco–SCRAM) model. The method for calculating the temperature at the bottom of the explosive charge with respect to the bottom gap is described. Based on this combustion model, the temperature history of a Composition B (COM B) explosive charge in the presence of a bottom gap is obtained during the launch process of a 155-mm artillery. The simulation results show that the bottom gap thickness should be no greater than 0.039 cm to ensure the safety of the COM B explosive charge in the launch environment. This conclusion is consistent with previous results and verifies the correctness of the proposed model. Ultimately, this paper derives a mathematical expression for the maximum temperature of the COM B explosive charge with respect to the bottom gap thickness (over the range of 0.00–0.039 cm), and establishes a quantitative evaluation method for the launch safety of explosive charges. The research results provide some guidance for the assessment and detection of explosive charge safety in complex launch environments. Graphic Abstract [ABSTRACT FROM AUTHOR]

The fuzziness and randomness are significant epistemic uncertainty within the qualitative categorization of the two critical factors, the frequency and severity, which are not fully considered in the traditional risk matrix. This paper mainly proposes a cloud risk matrix method for the risk assessment of process safety considering the epistemic uncertainty in expert elicitation. The cloud model is employed to provide a mathematical expression for the fuzziness and randomness in the linguistic variables by its two numerical characteristics entropy En and hyperentropy He. An adjusted Mamdani inference algorithm is constructed for the determination of an integrated risk cloud given the value of input variables. And the centroid method is improved to be adapted to the calculation of risk index from the enormous cloud droplets in the integrated risk cloud. A case study of risk assessment for distillation column unit is performed to illustrate the process of cloud risk matrix in detail, and the validity and rationality are verified by contrast to the results from the fuzzy risk matrix. Results indicate the proposed method can handle the randomness of qualitative concepts, which is more suitable to the practical condition. Moreover, the effect of the hyperentropy He on the randomness of risk index is also investigated and discussed for the reference to parameter selection. The proposed cloud risk matrix can provide an effective risk inference tool in a wide range of engineering fields.

Developing new electrode materials with good temperature‐dependent electrochemical performance has become a great issue for the deployment of hybrid supercapacitors with wide temperature tolerance. In this work, a series of Ta‐substituted SrCo1‐xTaxO3‐δ (x=0.05, 0.10, 0.15, 0.20) perovskites have been studied as positive electrodes for hybrid supercapacitors in terms of their structures, elemental valence states and electrochemical performances. Incorporating Ta into SrCoO3‐δ perovskite not only stabilizes the crystallite structure but also notably improves electrochemical activities. The SrCo0.95Ta0.05O3‐δ@CC delivers the highest specific capacity (Qsp) of 227.91 C g−1 at 1 A g−1, which is attributed to the highest oxygen vacancy content and the fastest oxygen diffusion kinetics. The hybrid supercapacitor SrCo0.95Ta0.05O3‐δ@CC//AC@CC exhibits a high energy density of 22.82 Wh kg−1@775.09 W kg−1 and a stable long‐term cycle life (5000 cycles) with 90.7 % capacity retention. As temperature increases from 25 to 85 °C, the capacitance properties are improved at elevated temperatures for both electrode and device due to the increased electrolyte conductivity. The outstanding electrochemical results present that SrCo1‐xTaxO3‐δ perovskite holds good prospects for hybrid supercapacitors with wide temperature tolerance. [ABSTRACT FROM AUTHOR]