Simulation on a proposed large-scale liquid hydrogen plant using a multi-component refrigerant refrigeration system

In this chapter is addressed the thematic of refrigerants: its historical evolution; properties; legislation applied in the area and future trends. The first refrigerant being marketed on a large scale was ethyl ether (R610), in 1834. Since then, the evolution of the utilized refrigerants was stimulated, initially due to constructive issues in the refrigeration system and later to environmental issues. This evolution may be divided into four generations: 1st use of any fluid that worked; 2nd safety and durability of the equipment; 3rd ozone layer protection and 4th increase of global warming concerns. During the process of evolution many refrigerants were tested to understanding of their properties. Currently, environmental concerns are taken as guide in the search for new refrigerants. The most promising refrigerants to be used in future are the HFEs, HFOs and HFCs with low-GWP, natural refrigerants and blends between (HCs/HFCs and HFCs/HFOs) refrigerants.

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Air Products to build ‘world-scale’ liquid hydrogen plant at LaPorte

Focus on Catalysts ◽

10.1016/j.focat.2018.11.089 ◽

2018 ◽

Vol 2018 (12) ◽

pp. 5-6

Keyword(s):

Liquid Hydrogen ◽

Hydrogen Plant

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Air Liquide to build first large-scale liquid hydrogen production plant for FirstElement

Fuel Cells Bulletin ◽

10.1016/s1464-2859(18)30462-0 ◽

2018 ◽

Vol 2018 (12) ◽

pp. 10

Keyword(s):

Hydrogen Production ◽

Large Scale ◽

Liquid Hydrogen ◽

Production Plant

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Fault diagnosis of air-conditioning refrigeration system based on sparse autoencoder

International Journal of Low-Carbon Technologies ◽

10.1093/ijlct/ctz034 ◽

2019 ◽

Vol 14 (4) ◽

pp. 487-492

Author(s):

Zhiyi Wang ◽

Jiachen Zhong ◽

Jingfan Li ◽

Cui Xia

Keyword(s):

Fault Diagnosis ◽

Air Conditioning ◽

Large Scale ◽

High Efficiency ◽

Support Vector ◽

Small Scale ◽

Refrigeration System ◽

Large Scale Data ◽

Fault Features ◽

Sparse Autoencoder

Abstract To overcome the drawbacks of using supervised learning to extract fault features for classification and low nonlinearity of the features in most of current fault diagnosis of air-conditioning refrigeration system, sparse autoencoder (SAE) is presented to extract fault features that are used as the input to the classifier and to achieve fault diagnosis for air-conditioning refrigeration system. The SAE structure is tuned by adjusting the number of hidden layers and nodes to build the optimal model, which is compared with the fault diagnosis model based on support vector machine. Results indicate that the indexes of the model combined with SAE, such as accuracy, precision and recall, are all improved, especially for the faults with high complexity. Besides, SAE shows high generalization ability with small-scale sample data and high efficiency with large-scale data. Obviously, the use of SAE can effectively optimize the diagnosis performance of the classifier.

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