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Objectives This study focuses on the sealing performance of lifting hatches applied to nuclear power plants under accidental working conditions.MethodsAn experimental setup is built to achieve the quantitative measurement of steam micro-leakage. In this device, saturated steam at 0.66 MPa is charged into a pressurized chamber to simulate accident conditions, the leaking steam is collected through a collection chamber with an insulation layer, and no condensation occurs. The changes in temperature, humidity and pressure in the collection chamber are monitored in real time to estimate the steam leakage rate.ResultsThe experimental results show that the steam leakage rates of the lifting hatch models with 1.5 mm and 3.0 mm composite gaskets are 13.93 g/24h and 11.91 g/24h respectively, both meeting the low leakage design requirements, with the 3.0 mm gasket providing superior sealing performance. An analysis of the dry air and steam measurements with the same gasket and at the same pressure shows that the sealing coefficients nd4 obtained for both are very close, which verifies the accuracy of the experimental device for steam microleakage measurements.ConclusionsThe proposed steam microleakage measurement method can be extended to various sealing structures and pressure systems, and provides a new measurement idea for evaluating the sealability of stack chambers under accident conditions.

To meet the different functional needs for soybean protein isolate (SPI) in different food applications, this study utilized infrared spectroscopy to rapidly analyze 70 SPI samples subjected to different pH treatments, and explored the effect of pH changes on the secondary structure content of SPI. Mean centralization (MC), multivariate scattering correction (MSC), standard normal variate transformation, and normalization were used for infrared data preprocessing. Feature wavebands were identified based on two-dimensional correlation infrared spectra, and predictive modeling of the secondary structure content of SPI against pH was performed using partial least squares (PLS) and arithmetic optimization algorithm-random forests (AOA-RF). The results showed that the relative standard deviations of the α-helix, β-sheet, β-turn, and random coil prediction models developed by the combined use of MC and MSC were 1.29%, 1.60%, 1.37%, and 7.28%, respectively, indicating their combination to be the best spectral pre-processing method. The optimal model for predicting α-helix and β-sheet contents was AOA-RF (characteristic wavebands), with calibration determination coefficients of 0.935 0 and 0.926 6 and prediction determination coefficients of 0.856 8 and 0.870 1, respectively. The optimal model for predicting β-turn and random coil contents was PLS (characteristic wavebands), with calibration determination coefficients of 0.915 4 and 0.881 7 and prediction determination coefficients of 0.891 3 and 0.784 3, respectively. The results of this study provide a theoretical basis for product quality detection and processing condition control in industrial settings.