For an integrated liquefied air energy storage and electricity generation system, mathematical models of the liquefied air energy storage and electricity generation process are established using a thermodynamic theory. The effects of the outlet pressure of the compressor unit, the outlet pressure of the cryogenic pump, the heat exchanger effectiveness, the initial air temperature and pressure before throttling on the performances of the integrated liquefied air energy storage, and the electricity generation system are investigated, using the cycle efficiency and liquid air yield ratio as the evaluation indexes. The results show that if the compressor outlet pressure is raised, both the compression work and the expansion work increase, but because the expansion work increases more slowly, the cycle efficiency of the system gradually decreases. Increasing the cryogenic pump outlet pressure and heat exchanger effectiveness can significantly increase the cycle efficiency of the system; the higher the air pressure and the lower the air temperature before throttling, the greater the liquid air yield after expansion, and the higher the cycle efficiency. The theoretical analysis models and research results can provide a reference for the development of an integrated system of liquefied air energy storage and electricity production, as well as for the development of medium-capacity energy storage technology.
Fault detection is critical to ensure the safely routine operations in nuclear power plants (NPPs), requiring very high accuracy and efficiency. Meanwhile, the rapid development of modern information technologies have profoundly changed and promoted various sectors including nuclear industry. Inspired by the great progress and promising performance of deep learning based image classification recent years, a two-stage fault detection methodology in NPPs has been proposed in this paper. First the time-series data describing the operating status of NPPs have been transformed into two-dimensional images by four methods, preserving the time-series information in images and converting the fault detection problem into a supervised image classification task. Then four specific image classifying models based on three primary deep learning architectures have been separately experimented on the imaged time-series data, achieving excellent accuracies. Further the performances of different combinations of transforming means and classifying models have been compared and discussed with extensive experiments and detailed analysis of throughput for four transforming methods. This methodology proposed has obtained remarkable results by reshaping data format and structure, making image classifying models applicable, which not only efficiently detect and warn possible faults in NPPs but also enhances the capability for safety management in nuclear power systems.
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